At Augury, we use a rainbow of AI techniques: picking the right AI application for each specific purpose. “It’s all about using the right tool for the right job,” says James Newman, Head of Product and Portfolio Marketing at Augury. “Whether it’s Industrial AI, GenAI, or Causal, they all have particular strengths that can work to make your work easier and more impactful.” 

It’s safe to say that Augury has always been in the Reliable AI business. And happily, we continue to create more tools that are reliable when applied correctly and for the proper use cases. We’ll use any technique to improve our models in real-time. It’s what we do. 

Meanwhile, there’s a push to regulate AI, and many governments already have regulations – or will have them soon. In general, it’s about making emerging AI technologies transparent, explainable, safe, and based on FAIR data. These are all reasonable ideas everyone should aspire – in the name of creating  Trustworthy AI.

Reliable AI – AI that works to produce the proper desired outcomes – is very much part of this vision. And as you all know, in an industry like manufacturing, you cannot afford mistakes in terms of both safety and the bottom line.

State Of The Art Industrial AI – And Beyond

Our so-called bread-and-butter AI is industry-renowned for its ability to predict when a machine will break down – in fact, it’s even guaranteed. Designed for purpose-built solutions, this AI will continue growing with more use cases, capabilities, and new ways of leveraging insight. And, we’ll continue to use our neural networks to do the heavy lifting in terms of in-depth modeling. 

We will also keep experimenting with new and emerging forms of AI. For instance, our recent success with our Machine Health algorithms in applying Continuous Learning, which represents one of the more significant milestones in our quest toward overall expert-level AI, was primarily thanks to the rich – and accurate – training data created by Generative AI.  

“Yes, GenAI has a reputation for hallucinating. But it would be best to remember it’s a tool, not an outcome. Gen AI’s accuracy is 100% based on the model it’s being executed against and the parameters around which it is being controlled.”

GenAI Is Your Friend If It’s Used Right

Yes, GenAI has a reputation for hallucinating. But it would be best to remember it’s a tool, not an outcome. GenAI’s accuracy is 100% based on the model it’s being executed against and the parameters around which it is being controlled. GenAI is not evil. It’s down to the people to control it.

The large language models (LLMs) of GenAI work by sucking up lots of data, learning the patterns, and then working to predict the following pattern – in an often-unknowable way. It’s about answering a question that resembles the answers people usually give. Hence, when it doesn’t have the data to fill in the blanks correctly, it starts hallucinating.

In the case of Continuous Learning, we ensured the LLMs we developed only had access to quality data – namely, the over 500 million hours of Machine Health data taken from over 100 types of machines and dozens of industries. In other words, reliable data begot reliable outcomes.  

“By embedding a GenAI agent into our platform, we are now able to let our users engage with our AI very quickly and in the natural language they prefer – bringing our best-of-class AI to the front row, as it were.”

Complicated But Doable: GenAI As Reliable AI Assistant

It will take time to unfold all of GenAI’s potential and value, and it will take even more time for a high-risk industry like manufacturing that cannot afford to base its decisions on a hallucination. GenAI is still coming fast, but it will require a lot of work underneath it. 

However, one other GenAI use case we will see in the short term is related to how the inner workings of all of Augury’s AIs have been largely hidden from our users. Yes, you are accurately told what machine needs fixing and how to do it within a specific time frame before it becomes a problem. However, the users could generally only dig deeper in a rather manual and cumbersome way. 

By embedding a GenAI agent into our platform, we are now able to let our users engage with our AI very quickly and in the natural language they prefer – bringing our best-of-class AI to the front row, as it were.  

An AI To Help Explain AI 

Naturally, we’re not talking about just throwing ChatGPT at it, which would spark hallucinations and insufficient insights. You need to control the data that ChapGPT is dealing with carefully – and the same goes for any custom LLM we develop.

Either way, this AI agent will help users better understand what’s happening with the AI in the background. Those working on the factory floor can start querying the platform directly for supporting evidence: Why must I look at this machine? What is the metadata? Can I compare the metadata with the metadata of another machine? Has this happened before? Who fixed it and how?

This is where GenAI can shine: engaging with a trustworthy model to give you additional insight. 

“It’s not just about considering how one thing impacts another but also how it affects many different factors and what changes you need to make to get your desired result.”

What Will Causal AI Mean For The Future Of The Factory Floor?

In many ways, Causal AI offers the perfect fit for fully transparent and safe AI. Because it’s all about learning cause-and-effect relationships between different data sets, these Causal AI models are very explainable thanks to their very construction. 

As we aspire towards full Production Health, we can loop in more knowledge models based on domain expertise – combining data sets from maintenance, production, operations, quality, etc. As we bring in these other data sets, we’ll use Causal AI to examine all the cause-and-effect relationships between what we see on the machines and what the system produces. 

In short, it’s about finding true causality rather than just the correlations offered by GenAI. It’s not just about considering how one thing impacts another but also how it affects many different factors and what changes you need to make to get your desired result. 

“In short, Reliable AI is not about a single methodology.”

The Dance Of The AIs – As Choreographed By Humans

Causal AI will be huge for manufacturing. By allowing manufacturers to find relationships they may not have seen before, Causal AI will spark whole new ways of doing things to optimize processes. 

And to help explain these intricacies, a GenAI agent may be looped in to help – but without losing the power of the Industrial AI that forms the backbone of highly accurate, and dare we say it, reliable AI insights. It’s this dance between different technologies that will define the future of manufacturing. 

In short, Reliable AI is not about a single methodology. It’s about using the right combination of methods to provide reliable answers to people in reliability. 


Stay tuned for more exciting Augury AI info! Reach out!

The post Reliable AI: Providing Reliable Insights For Reliability Professionals appeared first on Augury.

This global manufacturer produces flavors for the food industry and fragrances used in beauty and well-being products. Deploying Augury in one of its U.S. plants, the company found quick time-to-value when alerted on a Rox 1 fan and motor just days after installation.

Industry—Flavor and Fragrance//
Machine—Rox 1 fan & motor//
Fault Type—Rotating mechanical looseness//

From Baseline to Alarm

Four days after installing Augury Machine Health sensors on a Rox 1 fan and motor, the machine came out of baseline, immediately indicating “Alarm” status due to signs of rotating mechanical looseness. According to Augury’s analysis, the asset displayed an increasing trend in elevated acceleration amplitudes at the driven fan.

Figure 1: Platform showing machine coming out of baselining activity and into Alarm (March 2024)

The Recommended Fix

Augury advised the plant’s maintenance team to move quickly and attend to the following recommendations:

• Slow rolling the shaft to feel for resistance or roughness
• Performing life and endplay checks with dial indicators to verify bearing/housing clearances
• Ensuring adequate lubrication

The Taste of Victory

The plant’s team executed a successful repair during scheduled downtime based on Augury’s feedback. Along with new belts, bearings, and laser alignment, they also secured the base of the asset to the concrete foundation below. 

Figure 2: Reduction in overall velocity post-repair, July 12 onward

Figure 3: Reduction in high-frequency vibration post-repair, July 12 onward

Plant floor leadership estimated slightly more than $18K in costs avoided – a huge win in saving product, maintaining throughput, reducing waste, and using energy efficiently.

More importantly, a win like this goes deeper than costs avoided. It can also be counted in less tangible ways, like increasing worker safety and morale when repairs are planned in advance rather than operating in reactive, firefighting mode. 

Ready to learn how Augury’s Machine Health can help you find stability and predictability on your plant floor? Contact our team for a no-pressure conversation on solutions to your toughest production challenges.

The post Tasting Productivity & Smelling Success: Leading Flavor and Fragrances Manufacturer Avoids $18K in Costs with Machine Health appeared first on Augury.

In the rapidly evolving landscape of industrial technology, Internet of Things (IoT) predictive maintenance has emerged as a groundbreaking approach to equipment management and operational efficiency. This comprehensive guide explores how IoT is revolutionizing maintenance strategies across various industries.

What Does IoT Stand For?

IoT is the Internet of Things, a technological framework connecting physical devices, sensors, and systems through Internet-enabled communication. In maintenance applications, IoT creates a smart network that:

• Transforms traditional reactive maintenance approaches
• Provides unprecedented visibility into equipment performance
• Enables data-driven maintenance strategies
• Connects previously isolated industrial systems
• Supports real-time monitoring and predictive analytics
  

Read: ‘IoT Can Be Reliable And Easy To Maintain – If We Collaborate’

What Does IoT Mean in Maintenance?

In the context of maintenance, IoT represents a network of interconnected sensors, devices, and software systems that collect, transmit, and analyze real-time data about equipment performance and condition. This technological ecosystem enables:

• Continuous monitoring of machinery and infrastructure
• Real-time data collection and analysis
• Proactive identification of potential equipment failures
• Enhanced decision-making capabilities
• Significant reduction in unplanned downtime

How is IIoT Technology Useful in the Predictive Maintenance of Industrial Processes?

Industrial Internet of Things (IIoT) technology offers transformative benefits for industrial maintenance:

1. Enhanced Monitoring Capabilities

• Continuous real-time equipment tracking
• Comprehensive performance data collection
• Instant alerts for potential issues
• Detailed historical performance analysis

2. Operational Efficiency

• Reduce unexpected equipment failures
• Optimize maintenance scheduling
• Minimize production interruptions
• Improve overall equipment effectiveness

3. Cost Optimization

• Decrease emergency repair expenses
• Extend the equipment life cycle
• Reduce unnecessary maintenance interventions
• Lower overall operational costs

What Technologies Are Used in Predictive Maintenance?

Predictive maintenance leverages multiple advanced technologies:

• Machine Learning Algorithms
• Artificial Intelligence
• Cloud Computing
• Big Data Analytics
• Advanced Sensor Technologies
• Edge Computing

Q/ If a machine dies without a sensor to tell you, how will you find out?
A/ Catastrophe and/or downtime

What Sensors Are Required for Predictive Maintenance?

While increasingly found bundled together in a single unit, critical sensors for effective predictive maintenance include:

1. Vibration Sensors

• Detect mechanical irregularities
• Monitor equipment oscillation patterns
• Identify potential bearing or alignment issues

2. Temperature Sensors

• Track thermal variations
• Detect overheating components
• Prevent potential thermal-related failures

3. Acoustic Emission Sensors

• Capture sound frequency changes
• Identify microscopic structural modifications
• Detect early signs of mechanical wear

4. Electrical Current Sensors

• Monitor power consumption
• Detect motor performance variations
• Identify potential electrical system issues

How Does IoT Help the Process of Automation?

IoT significantly enhances automation by:

• Enabling autonomous system monitoring
• Facilitating self-diagnostic capabilities
• Supporting predictive decision-making
• Reducing human intervention in routine tasks
• Creating intelligent, self-optimizing systems
• Integrating multiple technological platforms
• Providing seamless data communication

Like the Future, IoT Is All About Automation

Conclusion: IoT Is A Major Technology That Will Keep Growing in Importance

IoT predictive maintenance represents a paradigm shift in industrial operations. It can really transform how you and your company approach equipment management. By leveraging advanced sensors, intelligent analytics, and interconnected technologies, businesses can achieve unprecedented operational efficiency, cost optimization, and strategic performance.

As technology evolves, IoT predictive maintenance will become increasingly sophisticated, offering industries worldwide even more refined insights and capabilities.

Read: ‘Finally… The World’s First Edge-AI Native Machine Health Sensing Platform’.
Or reach out and contact us.

The post IoT Predictive Maintenance Explained appeared first on Augury.

With its first annual Spotlight Awards Ceremony, Augury recognizes manufacturing facility teams’ dedication and hard work, going the extra mile with Machine Health. After all, while tech can work to impact people, plants, and the planet, it needs people to make it happen. Thank you to our 30+ site winners, including runner-up Frito-Lay Coventry and our grand winner Fortune Brands New London!

Celebrating Success

The inaugural Spotlight Awards ceremony took place on November 20, 2024. For the first edition, Augury highlighted maintenance and reliability teams with incredible initiative, awarding them the “Beam of Excellence” designation. 

Evaluating hundreds of plants, we identified the top 5% of customer sites based on their performance in the following criteria: high response rates, IoT serviceability, number of active users, and quantified wins. In short, these plant teams raised the bar on Machine Health and predictive technology for their companies – and deserve recognition. 

Listed in alphabetical order, the 2024 Spotlight Awards “Beam of Excellence” winners: Amcor / BAZAN Group / Canfor (2 awards) / Colgate-Palmolive (3 Awards) / Dairy Farmers of America / GAF (2 Awards) / Fortune Brands Innovations / Indorama (3 Awards) / ICL Group / Keurig Dr. Pepper / Molson Coors / Nestlé Purina Petcare / PepsiCo (8 Awards) / Reyes Holdings / Roseburg / Toray Industries / Wis-Pak.

It’s About People, Not Just Technology

Hosted by Ed Ballina and Alvaro Cuba, industry veterans and hosts of the popular Manufacturing Meetup podcast, the virtual ceremony opened with special remarks from Augury’s co-founders: CEO Saar Yoskovitz and CPTO Gal Shaul.

“Seeing the work that you guys are doing, the amount of dedication, effort, and creativity that goes into making wood products, food, beverages, toilet paper – and the list goes on and on – has just been amazing and inspiring for us to start a company and see how can we fit in to help you,” says Gal. 

“Especially these days, when manufacturing faces so many headwinds, from geopolitical challenges to supply chain issues, the economy, talent shortage, which we all feel daily. And we believe that there is a way out. Right by leveraging technology and modern tools, we can fundamentally transform how manufacturing is being done,” adds Saar.  

“This impressive diversity tells a great story about how technology is transferable across sectors and geographies.”

“At the same time, we understand it’s insufficient to put the sensor in the right location. It’s not enough to provide an alert just in time. If there’s no one on the other side to take action to repair the machine, we won’t achieve anything. And that’s why you are such a crucial part of our story.” 

It’s In The Diversity – And The Numbers

The winning facilities and teams, covering a broad range of manufacturing verticals, hailed from three continents, eight countries, and 20 U.S. states. 

“This impressive diversity tells a great story about how technology is transferable across sectors and geographies,” says Ed.

But there’s another big story: in the numbers. As a group, these top performers achieved:

• 1.3K+ Machine Improvements 
• 4,300K+ Hours of Downtime Avoided 
• $11.8M+ Maintenance Costs Saved 

Incredible Stories Of Cultural Change

However, metrics only tell part of the story. Leading up to the event, each facility helped craft their transformation story – providing additional input about how they’ve transformed the way they work around Machine Health technology. 

All of these sites have incredible stories of culture change, digital integrations, new processes, and updated roles and responsibilities. And in the end, two stories stood out and were recognized at the ceremony.

Drumroll Please. The Runner-Up Is… Frito-Lay, Coventry

The runner-up was PepsiCo’s Frito-Lay in Coventry, UK, which was already featured in a BBC feature about how the facility advanced manufacturing through Machine Health. Coventry’s numbers are impressive: a response rate of 94% and avoiding 650K+ costs and 65+ downtime hours.

“The introduction of Augury was a bolt out of the blue. We’ve gone from prehistoric to Star Wars in a couple of weeks,” says Kevin Thomas, Condition Monitoring Specialist at the facility. “It allows me to see the bigger picture and have an accurate story of what is going on. We used to run our equipment to destruction or until the line came down. Now, we can collaborate with the engineering teams to balance production. It feels good not to run about and react to major changes every week.”

“Part of what makes this site’s story so exciting is how they’ve connected the dots not just between Machine Health and cost savings, but also with the tangible impacts of other critical areas like safety, equipment lifespan, and team efficiency.”

And First Place Goes To… Fortune Brands’ New London facility

Like many other winners, the first-place team was initially skeptical due to poor experiences with other systems. However, that did not hold back the Fortune Brands’ New London, North Carolina facility. They fully embraced Augury’s solution, achieving 2.5X ROI in 8 months and 94% Serviceability. The facility is now expanding its Machine Health coverage from 40 to 140 machines. 

Part of what makes this site’s story so exciting is how they’ve connected the dots not just between Machine Health and cost savings, but also with the tangible impacts of other critical areas like safety, equipment lifespan, and team efficiency. 

“I was a bit apprehensive,” says Ric Wojcik, Senior Manufacturing Engineering Manager. “In my past maintenance life, I was introduced to and tried similar systems that simply did not perform. I could tell that Augury was unique from the other companies and could be a game-changer for us, and I was willing to take the chance. I am so pleased that we did!”  

“We’ve seen the difference and look forward to what the future holds.”

Looking To The Future: Upward And Onward

“We are excited,” says Ric. “And it just goes to show that the hard work that went in by these guys – Jesse, Jonathan, Troy, Matt, and Todd – was instrumental in getting all this stuff going on here at the facility. We’ve seen the difference and look forward to what the future holds.” 

As Saar put it: “It’s not just solving today’s challenges, but also looking towards tomorrow’s challenges. When we work with you and your peers, we hear these stories of life before Augury: waking up at 2 am, firefighting, heavy, laborious work… Today, you can focus on what really provides more and more impact and what really matters. So, we ask ourselves, how do we take that to the next level? And the answer is by working even closer with you, by listening to your feedback, by having you guide us on our journey.”

Cheers to the winners on a great 2024! Tune in next year for more success!

Learn more about the Spotlight Awards – and Machine Health. 

The post Spotlight Awards: Shining A Light On Augury Customer Achievements appeared first on Augury.

Every manufacturer wants to improve Overall Equipment Effectiveness (OEE) and associated metrics, but achieving world-class OEE is impossible without real visibility into machine health. In this article, we outline how a predictive approach to maintenance can significantly improve a manufacturing operation’s OEE by maximizing machine availability and performance, as well as output quality.

This article was originally published on October 27, 2020 and updated on November 18th, 2024.

As emerging technologies transform manufacturing, traditional approaches to machine maintenance are less effective. Maintenance and reliability teams that utilize a reactive approach to addressing equipment issues and breakdowns lack real insights into Machine Health. As a result, they can’t make meaningful improvements to OEE.

In the increasingly competitive world of manufacturing, this is a major problem.

What Is OEE And How Is It Calculated?

As the premier metric for measuring a manufacturing operation’s productivity, OEE considers equipment availability, performance, and quality of output to assign a numerical value to the overall effectiveness of a machine or production line. Essentially, that numerical value represents a machine’s actual productivity in relation to its full potential over the course of planned operations. It’s expressed as a percentage.

What Is A Good OEE Score?

A low OEE value means that a production line or individual piece of equipment is severely underperforming, whereas an OEE of, say, 100% indicates that an operation is producing only the highest-quality parts, as fast as possible, for the entire duration of planned production time.

Every manufacturer wants to improve OEE and its associated metrics, but achieving world-class OEE is virtually impossible without real visibility into machine health.

Time For A Change

The typical manufacturer allocates approximately 40% of total operating expenditure to maintenance — a substantial amount for any company. Yet when maintenance and reliability teams operate within a traditional reactive or preventive framework for identifying and prioritizing equipment repair, that investment is often insufficient.

Factory walk-throughs, random equipment checks, and annual shutdowns don’t just consume valuable time; they’re also prone to human error and leave opportunities for machine malfunctions to cause unplanned downtime. Furthermore, the data generated by these methods generally isn’t useful for manufacturers trying to forecast future risk and production output.

As a result, manufacturers employing conventional maintenance practices experience more unplanned downtime, lower-quality output, and more maintenance-related costs throughout the year. Eventually, these negative outcomes add up, putting a manufacturing operation at a severe disadvantage in relation to more forward-thinking competitors.

So what’s the solution?

“Not only does condition-based maintenance allow manufacturers to reduce machine downtime and avoid temporary unplanned outages (as well as potentially catastrophic equipment failure), but it also allows them to accurately forecast future risk of failure based on a machine’s current condition.”

How to Improve OEE

Given the increasingly complex nature of modern factory equipment, manufacturers can’t afford to not continuously monitor Machine Health. Real-time insight into equipment performance generated by sensors mounted onto or embedded within individual machines allows teams to perform condition-based and even predictive maintenance. The advantages of this approach over outdated maintenance and monitoring techniques are clear and substantial.

By using vibration analysis and similar data-collection methods in conjunction with advanced analytics, factory operators can anticipate a machine failure before it happens. When a bearing starts to show signs of deterioration, for example, teams utilizing a condition-based maintenance approach can replace the part before it negatively impacts a machine’s effectiveness.

Not only does condition-based maintenance allow manufacturers to reduce machine downtime and avoid temporary unplanned outages (as well as potentially catastrophic equipment failure), but it also allows them to accurately forecast future risk of failure based on a machine’s current condition. By extension, they’re able to more confidently set production targets and plan future maintenance operations around production goals.

To better understand how a predictive approach to maintenance can lead to significant improvements in a manufacturing operation’s OEE, let’s take a deeper dive into the metric’s three primary components.

“In high-level sports, it’s often said that the best ability an athlete can possess is availability. The same is true of machines in the world of manufacturing.”

The Three Components Of OEE

1. Machine Availability

In high-level sports, it’s often said that the best ability an athlete can possess is availability. The same is true of machines in the world of manufacturing. A piece of equipment that’s not functioning at capacity (or at all) when you need it to be operating at 100% is a financial drain on your business. Unscheduled downtime as a result of machine failure is one of the largest obstacles manufacturers face when it comes to improving OEE. A reactive approach to maintenance virtually guarantees that unexpected equipment failure will be a persistent problem.

Manufacturers that rely on a preventive maintenance approach, with scheduled downtime to address equipment issues that may or may not impact OEE, face similar inefficiencies. During a plant shutdown or other scheduled maintenance periods, mechanics and technicians are typically presented with a task checklist and a target completion rate. In order to meet this target within the time allotted for maintenance, they’ll often mark unfinished repairs as complete. This is why a facility with a preventive maintenance checklist that is close to 100% complete can find itself operating at just 50% efficiency.

Moreover, even the best technicians are prone to human error. During scheduled downtime, these experts will often be tasked with inspecting equipment that doesn’t need repair, resulting in further time and cost inefficiencies.

2. Machine Performance

In order to meet the demands of modern consumers, manufacturers must produce as much as possible, as fast as possible. The problem is that an outsize focus on production speed can actually lead to more instances of equipment failure, ultimately hindering productivity rather than improving it. Despite this reality, manufacturers must maintain certain performance standards if they hope to stay in operation.

Idling equipment and minor stoppages due to operational inefficiencies can become major problems if not addressed promptly. However, factories that rely on manual machine inspection processes often don’t know that a machine isn’t performing at full capacity until it fails completely. As a result, they suffer a loss of production that’s exacerbated by the need to divert human and capital resources to diagnostic and maintenance activities.

3. Output Quality

Large manufacturing operations have little room for error when it comes to production quality — they have to get it right the first time. If they don’t, they stand to lose potentially significant amounts of time and money and could suffer severe damage to their reputation as well.

Excess waste — scrap or output that can’t be sent to customers due to structural or cosmetic defects — often occurs when machines aren’t operating at optimal conditions. Whether this results from normal depreciation, overuse, or some other issue, it’s a loss for a manufacturer.

‘But understanding the actual state of your machines with a Machine Health platform is only the first step toward improving OEE.”

Maximizing OEE With Machine Health

Each of these components can be improved manually over time, but if your goal is to achieve world-class OEE, reactive and preventive maintenance approaches won’t get you there — and they certainly won’t get you there ahead of your competitors. Instead, you’ll need a Machine Health monitoring solution that gives you real-time visibility into equipment status, condition, and performance.

But understanding the actual state of your machines with a Machine Health platform is only the first step toward improving OEE. Being able to use those insights to prioritize maintenance schedules and take direct, targeted action to reduce machine downtime, failures, and waste represents the next stage of facility sophistication.

A Holistic Approach to OEE

Within every manufacturing operation, there is a production and a maintenance unit, and it’s logical to assume that the two might work together to pursue common goals. Historically, however, that hasn’t been the case. In fact, their operational mandates have often put these two teams directly at odds with one another.

Maintenance is typically charged with maximizing OEE, while production teams’ objectives tend to shift based on ongoing revisions to quotas or product specifications. Both units are indispensable, but the emphasis on speed and the immense pressure to meet market demands mean production goals are usually prioritized over machine health and maintenance. Inevitably, this leads to losses in productivity and excessive spending on maintenance.

“While industry leaders have understood and implemented TPM’s principles for decades, relatively recent advances in machine learning and data analytics have allowed manufacturers to truly bridge the gap between maintenance and production truly.”

In the latter half of the 20th century, Japanese manufacturing leaders seeking to remedy this situation developed a methodology designed to create synergy between the two teams: Total Productive Maintenance (TPM). The focus of TPM is on reducing operational costs through the continual improvement of equipment effectiveness, and its creators established OEE as a critical metric in manufacturing.

While industry leaders have understood and implemented TPM’s principles for decades, relatively recent advances in machine learning and data analytics have allowed manufacturers to truly bridge the gap between maintenance and production — in a way that the creators of TPM could only imagine.

The Next Evolution of Manufacturing

TPM makes everyone responsible for OEE and calls for a holistic approach to production and maintenance. When manufacturers have access to real-time Machine Health data, such an approach becomes possible.

A Machine Health data platform gives factory operators insight into key equipment metrics (e.g., speed, vibration, heat levels), enabling them to decide whether a machine requires intervention with pinpoint accuracy. These capabilities allow operations to move from preventive maintenance to predictive maintenance, opening up countless opportunities to improve overall manufacturing productivity and efficiency.

With this data, for example, operators don’t just know whether they have the equipment necessary to meet production goals on schedule; they also know whether their machines can support faster or longer production runs. They can then adjust production line speeds or switch production lines altogether to sidestep equipment problems. Without machine health data, however, operators often find themselves gambling on the unknown.

“On a macro level, Machine Health data can be combined with operational data, allowing manufacturing leaders to optimize facility design to maximize the OEE of individual machines.”

When Machine Health metrics decline, a Machine Health platform will alert managers that maintenance is needed before issues arise. The technicians performing repairs then have access to a vast array of data that allow them to work more effectively and efficiently.

On a macro level, Machine Health data can be combined with operational data, allowing manufacturing leaders to optimize facility design in a way to maximize the OEE of individual machines. While it may seem counterintuitive, the reality is that a single optimized machine can actually cause downstream problems and reduce the efficiency of a production line if other equipment hasn’t been similarly optimized.

The Right Data At The Right Time

For example, consider a bottle-filling machine configured to fill 1,000 bottles every minute. A two-minute accumulation time between the filler and downstream labeler means that the labeler will be shut down for two minutes when label rolls need to be replaced. Suppose the filler is “optimized” to fill bottles at a clip that exceeds 1,000 units per minute but the label roll replacement process hasn’t also been improved. In that case, bottles will be filled faster than they can be labeled, resulting in excess waste.

Without a Machine Health platform, operators facing the above scenario might shut down the entire production line for equipment inspection and conclude that the problem is a faulty labeler. However, with the right data, an operator can avoid unplanned downtime and simply reduce the filler’s speed to reset the line and continue production, without having to spot-check each machine individually.

“In the competitive world of 21st-century manufacturing, these operations will quickly become customer favorites, while those that persist with a reactive or preventive approach to maintenance will fall behind and eventually disappear.”

On The Path Of Continuous Improvement

The benefits of a predictive maintenance approach paired with real-time Machine Health monitoring are both instantaneous and cumulative over time. Manufacturers that adopt such an approach will be able to continuously improve OEE, allowing them to set increasingly aggressive production goals and deadlines.

In the competitive world of 21st-century manufacturing, these operations will quickly become customer favorites, while those that persist with a reactive or preventive approach to maintenance will fall behind and eventually disappear. With early adopters of sophisticated machine health monitoring and data analytics solutions already separating themselves from their peers, the window of opportunity for latent adopters to close the gap is shrinking fast.

Want to talk more about pumping up your OEE? Reach out!

The post How To Improve Overall Equipment Effectiveness (OEE) With Machine Health Monitoring appeared first on Augury.

Augury’s new R4000-series sensors are already doing what they’re supposed to be doing. However, the real story comes together early next year, according to Augury’s Head of Product and Portfolio Marketing James Newman. That’s when the Cassia X-2000 is deployed as the leading industrial Bluetooth gateway optimized for industrial IoT applications. “With sensor/gateway integration, a whole new IoT chapter can begin: faster, more reliable, more flexible, and able to put AI wherever you need it most. Bring on your edge use cases!”

Life On The Edge

Three years ago, Augury Co-Founder and CPTO Gal Shaul was asked in an interview what emerging technology he believes has the most potential for manufacturing. “I’m really excited about the movement around AI – especially edge AI, which can work at the endpoint and use models as accurately as those using data from a platform. It will enable us to make many new transitions in the market. […] In a few short years, we will be unable to remember how things used to be because it will all be so different.”

For years, edge computing represented the future. Allowing sensor-enabled devices to collect and process data closer to the manufacturing action would enable more AI-led decisions in near real-time, creating a new rainbow of potential use cases to benefit both productivity and those working on the factory floor. But yes, it remained tricky in terms of issues such as security concerns, scalability, and sheer complexity. 

Until now. Now, it’s payback time.

Our new Machine Health sensing platform can now run AI via our new Halo R4000 series sensors, the upcoming Cassia X2000 Enterprise Bluetooth IOT Gateway, and, of course, as we’ve done traditionally, the Cloud. With our wholly new IoT, we are now experimenting with customers to figure out what works best to double down on increasing value.

In other words, welcome to the world of Machine Health 2.0 – where the long-vaunted potential of edge computing can finally reach fruition.   

“AI-powered end-to-end condition monitoring solution designed for cost-effective monitoring at scale with maximum flexibility.” Read the full press release.

Big Story: Rugged, Future-Proof Sensor

The new Halo R4000 series is the world’s first Edge AI-capable Machine Health sensor. As our most miniature industrial-grade sensor yet, the R4000-series features longer battery life, smart and dynamic diagnostics, self-healing connectivity, and wet environment protection – it’s ready to cover any asset that matters, anywhere.

Thanks to the upcoming HexaLock sensor mounting system, designed for greenfield and brownfield environments, the sensors are easier to install and maintain, eliminating some of the challenges of other designs.

One of the main benefits of the R4000 sensor is increased flexibility, in terms of costs, capabilities, and scaling.

Flexibility In Use – And Costs

One of the main benefits of the R4000 sensor is increased flexibility, in terms of costs, capabilities, and scaling.

You’ll naturally still want the full Cloud-backed coverage for your critical assets. This enables deep and descriptive diagnostics and provides the raw data that helps our vibration analysts truly understand what’s going on with a particular machine. However, the sensor chip can now also process this data more efficiently before sending it to the Cloud, thereby saving bandwidth and transmission costs. 

However, in terms of headline news, the chip will also let you run sophisticated AI models on the edge for less critical assets to provide actionable insights, such as basic anomaly detection – helping customers unlock cost-effective total plant coverage. 

Thanks to collaboration with various partners and early adopters, we have solved this IoT connectivity problem.

Bigger Story: New IoT 

All these new innovations – the sensor, the HexaLock, and the Cassia X2000 Enterprise Bluetooth IOT Gateway – means new IoT. 

We all know working with Internet of Things technologies isn’t always easy – too many things can go wrong between reading the data and getting it to the Cloud, where the more in-depth AI magic can happen. Thanks to collaboration with various partners and early adopters, we have solved this IoT connectivity problem.

For instance, the gateway can apply AI for specific value-added applications, such as auto lubrication.

Opening The Innovation Floodgates

Due for general implementation in early 2025, the Cassia gateway is currently being tested in the field with customers. Significantly, and thanks to the increased efficiency of the overall system, we can further increase the number of sensors to 40 per gateway – more than twice what we could do previously.

The gateway’s ability to cover a much wider range will also help many scenarios, not least of which is safety – particularly in harder-to-reach or environmentally restrictive areas where it’s best to minimize human-machine interaction. 

You’ll also be able to run custom firmware from the gateway itself. For instance, the gateway can apply AI for specific value-added applications, such as auto lubrication.

Can you tell we’re excited? The time has truly arrived to make a difference. Are you ready to double-down with us.

Do you have a use case that might benefit from AI on the edge? Reach out! Let’s talk!
Or first, check out the full press release.

The post Finally… The World’s First Edge-AI Native Machine Health Sensing Platform appeared first on Augury.

In today’s competitive industrial landscape, businesses constantly seek innovative strategies to optimize equipment performance, reduce downtime, and minimize maintenance costs. Predictive maintenance systems have emerged as a game-changing approach that revolutionizes how organizations manage their critical assets and infrastructure.

What is a Predictive Maintenance System?

A predictive maintenance system is an advanced AI-driven approach to equipment management that uses data analysis, sensor monitoring, and sophisticated algorithms to predict potential machinery failures before they occur. Unlike traditional reactive or even preventive maintenance methods, predictive maintenance provides real-time insights into equipment condition, allowing you to:

• Anticipate potential mechanical issues
• Schedule maintenance precisely when needed
• Prevent unexpected equipment breakdowns
• Optimize maintenance resources
• Extend the operational lifespan of critical machinery

This approach transforms maintenance from reactive and panicky “fire-fighting” into a strategy h that directly impacts operational efficiency and bottom-line performance.

Read ‘Why The Confusion? Prescriptive Maintenance Vs Predictive Vs Preventative’.

How Does Predictive Maintenance Work?

Predictive maintenance operates through a sophisticated combination of technologies and analytical processes:

1. Data Collection

• Advanced sensors are installed on critical equipment
• These sensors continuously monitor various parameters such as:
  • Vibration
  • Temperature
  • Acoustic emissions
  • Electrical currents
  • Lubricant condition

2. Real-Time Monitoring

• Collected data is transmitted to centralized monitoring systems
• Machine learning algorithms identify patterns and anomalies

3. Predictive Analysis

• Sophisticated models predict potential failure points
• Maintenance teams receive actionable insights and recommendations
• Required maintenance work is prioritized based on actual equipment condition. Edge AI solutions are now appearing to deal with problems that are time-sensitive – such as safety

Read: ‘The Evolution of Predictive Maintenance Toward Prescriptive Solutions’

Benefits of a Predictive Maintenance Solution

Implementing a predictive maintenance solution can offer you numerous advantages:

Cost Optimization

• Reduce unexpected equipment downtime
• Minimize emergency repair expenses
• Optimize maintenance scheduling
• Extend the equipment life cycle
• Significantly lower overall maintenance costs 
• Improved spare parts strategy

Operational Efficiency

• Maximize equipment performance
• Reduce production interruptions
• Improve overall equipment effectiveness (OEE)
• Enable data-driven decision-making
• Enhance production planning and scheduling

Safety Improvements

• Identify potential equipment failures before they become safety risks
• Reduce workplace accidents related to equipment malfunction – and by minimizing human-machine interaction
• Ensure compliance with safety regulations
• Protect workforce and infrastructure

Strategic Advantages

• Gain a competitive edge
• Improve asset management strategies
• Enable more accurate budgeting and resource allocation

Environmental Benefits

• Reduce waste from premature equipment replacement
• Optimize energy consumption
• Minimize unnecessary resource utilization
• Reduce waste caused by machine malfunction

Conclusion: Predictive Is Just The Beginning

Predictive maintenance represents a transformative approach to equipment management, offering organizations unprecedented insights into their operational infrastructure. By leveraging advanced technologies, manufacturers can unlock new levels of efficiency, reliability, and strategic performance.

As industries evolve, predictive maintenance will become essential for those seeking a competitive edge in an increasingly complex and technology-driven global marketplace.

Predictive Maintenance is a great start. But to go next-level, go prescriptive. Reach out to learn more.

The post Benefits of a Predictive Maintenance Solution appeared first on Augury.

Vibration monitoring has become an essential technology across multiple industries, offering critical insights into machine health, operational efficiency, and worker safety. This comprehensive guide explores the vital role of a vibration monitoring system (VMS) and why they have become indispensable for modern manufacturing.

What is the Objective of Vibration Monitoring?

Vibration monitoring provides a proactive approach to equipment maintenance and performance optimization. Maintenance and reliability can detect potential issues before they escalate into costly failures by continuously analyzing the vibration patterns of machinery and mechanical systems. This predictive maintenance strategy allows you to:

• Identify early signs of mechanical wear and tear
• Prevent unexpected equipment breakdowns
• Reduce maintenance costs
• Extend the operational lifespan of critical assets
• Minimize production downtime

Read: ‘Five Reasons Why Vibration Analysts Love Augury’

What are Vibration Monitors Used For?

Vibration monitors are used across manufacturing to monitor industrial machinery to ensure optimal performance and prevent unexpected failures. Such machines include:

• Motors
• Pumps
• Bearings 
• Rotating equipment

In addition, vibration monitors are versatile tools used widely across numerous (related) industries and applications, including automotive, aerospace, construction, and energy sectors.

Read the ‘Life of a Vibration Analyst’ series:
‘Aiming to Be Obsolete: Behind the Scenes with Augury’s Manager of Reliability Operations’
‘Bringing The Factory Home: A Day In The Life Of A Vibration Analyst’
‘A Trade Transformed: 20 Years Of Vibration Analysis’
‘A Fitbit For Machines: With Vibration Analysis, The Goal Is To Extend Life’ 

What is the Purpose of a Vibration Sensor?

As the fundamental data collection points, sensors form the core of a vibration monitoring system. It can:

• Capture detailed vibration frequency and amplitude data
• Convert mechanical movement into electrical signals
• Provide real-time insights into equipment condition
• Detect anomalies that might indicate potential mechanical issues
• Enable predictive maintenance strategies
• Take in more than vibration data for deeper insights, such as temperature and magnetic flux

Read: ‘How AI-Driven Vibration Analysis Enables Higher-Value Maintenance Work’

What Are The Benefits of Vibration Monitoring?

The benefits of implementing a robust vibration monitoring system are extensive and far-reaching:

• Cost Savings: By detecting potential issues early, you can avoid expensive emergency repairs and unplanned downtime.
• Improved Safety: Identifying mechanical problems before they become critical reduces the risk of equipment failure and potential workplace accidents.
• Enhanced Efficiency: You can optimize maintenance scheduling and improve overall equipment effectiveness (OEE).
• Data-Driven Decision Making: Use actionable insights for maintenance and operational strategies.

Conclusion: It’s All Good Vibrations

With vibration monitoring systems, you can optimize performance, ensure safety, and maximize operational efficiency. By investing in advanced solutions, transforming your maintenance strategies, protecting your workforce, driving sustainable growth is possible.

And as technology gets stronger and demands increase, monitoring will become an even more sophisticated and important tool for predictive maintenance and operational excellence.

Read: ‘Finally… The World’s First Edge-AI Native Machine Health Sensing Platform’.
Or reach out and contact us.

The post Benefits of Using a Vibration Monitoring System appeared first on Augury.

Reactive maintenance remains a surprisingly common approach for many organizations in industrial maintenance. This comprehensive guide explores the intricacies of reactive maintenance, providing insights into its potential benefits and significant limitations.

Reactive maintenance is most often described as “fire-fighting”: when something goes wrong, you try to fix it quickly. While it’s great that it’s being fixed, it’s less great that productivity has ground to a halt in the meantime, and workers often have to perform riskier, dangerous work. But thanks to emerging—and many more established—technologies, reactive maintenance can be thrown in the dustbin of history.

Unfortunately, it’s still a very common maintenance strategy across industries. Why?

What are the Advantages of Reactive Maintenance? 

Despite its limitations, reactive maintenance does offer some short-term benefits:

1. Lower Initial Costs

• Minimal upfront investment in maintenance infrastructure
• No need for advanced monitoring systems
• Reduced initial technology and training expenses

2. Straightforward Implementation

• Easy to understand and execute
• No complex planning or predictive analysis is required
• Minimal training needed for maintenance staff
• An option for non-critical or low-impact equipment

3. Minimal Administrative Overhead

• Reduced paperwork and documentation
• No need for complex maintenance scheduling
• Limited planning resources required

What are the Disadvantages of Reactive Maintenance?

While seemingly convenient, reactive maintenance presents significant challenges:

1. Increased Long-Term Costs

• Higher emergency repair expenses
• Unexpected equipment downtime
• More extensive damage from delayed repairs
• Significantly higher total cost of ownership
• Potential for catastrophic equipment failure

2. Operational Disruptions

• Unpredictable production interruptions
• Extended equipment recovery times
• Reduced overall operational efficiency (OEE)
• Negative impact on production schedules
• Potential loss of customer confidence

3. Safety Risks

• Higher probability of workplace accidents
• Increased risk of equipment-related injuries
• Potential non-compliance with safety regulations
• Compromised worker protection
• Higher insurance and liability risks

4. Equipment Reliability

• Accelerated equipment degradation
• Shortened equipment lifecycle
• Increased wear and tear
• Reduced overall equipment performance
• Higher replacement frequency

Read: ‘Predictive Analytics No Longer Cuts It. Long Live Prescriptive’.

What are Alternatives to Reactive Maintenance?

Organizations have many different, more advanced maintenance strategies, which are also becoming increasingly blended depending on the criticality of a particular machine and the specific use cases the manufacturer is going after.

1. Preventive Maintenance means more regularly scheduled inspections and systematic equipment serving. Benefits include:

• Proactive component replacement
• Reduced unexpected failures
• Lower long-term maintenance costs

2. Predictive Maintenance includes advanced sensor monitor and real-time machine monitoring to generate AI analytics. Benefits include: 

• Precise maintenance scheduling
• Minimal unnecessary interventions

3. Condition-Based Maintenance requires continuous monitoring for data-driven maintenance decisions. Benefits include:

• Immediate issue detection
• Optimized maintenance resources
• Enhanced equipment reliability

4. Reliability-Centered Maintenance includes comprehensive system analysis and strategic maintenance prioritization to manage equipment. Benefits include: 

• Holistic performance optimization
• Advanced risk management

5. Prescriptive Maintenance is the next stage of predictive maintenance, with AI insights that instruct what to fix and when. Benefits include:

• Reduces Downtime
• Optimizes Maintenance Costs
• Improves Equipment Performance 
• Enhances Safety

Conclusion: Reactive Maintenance Should Go The Way Of The Dodo

While reactive maintenance might seem convenient, it represents a short-sighted approach that can significantly impact an organization’s performance, safety, and financial health.

By transitioning to more advanced maintenance strategies, businesses can transform maintenance from a cost center into a strategic value generator, driving innovation, efficiency, and sustainable growth.

Read ‘Why The Confusion? Prescriptive Maintenance Vs Predictive Vs Preventative’.
Or reach out and contact us.

The post Reactive Maintenance: Its Advantages And (Many) Disadvantages appeared first on Augury.

A leading metals and mining manufacturer, operating dozens of facilities across North America, faced a significant challenge in monitoring one of their most critical pieces of equipment: a rotary kiln with a rotating rate of 23 RPM, far below the lower limit of standard vibration analysis.

Industry – Metals and Mining //
Machine – Rotary kiln //
Fault Type – Gear wear and misalignment //

<25 RPM Kiln Requires Unique Machine Health Approach

The manufacturer’s massive incinerator, essential for melting sand, is the center of the production process. With only one kiln per facility, any unplanned downtime could result in substantial financial losses and production delays. Complicating matters more, the kiln’s internal temperature exceeds 200 degrees Fahrenheit, creating a hazardous environment for maintenance workers. 

Initially, the company installed Augury’s Machine Health solution on the kiln, which measures temperature, magnetic flux, and vibration levels. However, the kiln’s slow rotation rate made it difficult to collect reliable data. The kiln is a barrel-shaped steel enclosure, approximately 12 feet in diameter, rotating at a mere 23 RPM, far below the lower limit of standard vibration analysis. This low frequency results in minimal vibration energy, making it challenging to detect subtle changes in the machine’s condition. 

Augury’s Low-RPM Monitoring Solution Uncovers Critical Gear Issue

A lot was riding on one machine–not just continued production, but consumer demand, customer satisfaction, and the company’s brand integrity. Recognizing the limitations of standard monitoring techniques, Augury’s team of experts recommended a novel solution: installing a newly developed high-frequency, ultrasonic sensor. It offered two key advantages that addressed the unique challenges posed by the rotary kiln.

Firstly, it was capable of collecting a longer data samples. By extending the sampling period, it could collect data over multiple rotations of the kiln, providing a more comprehensive picture of the machine’s behavior and enabling the detection of subtle variations that might occur only once per revolution.

Secondly, the ultrasonic component of the sensor enabled the detection of very high, ultrasonic frequencies. This capability was crucial for identifying issues in low-speed components within the slow-rotating kiln, such as the gears in the drive mechanism. It can detect early signs of wear, gear mesh, impacting, inadequate lubrication, or misalignment in these components before they manifest as visible vibrations.

The customer strategically placed sensors at key points including the drive end, non-drive end, and the gearbox. This comprehensive coverage ensured that data was collected from all critical areas of the machine.

Almost immediately after installation, an anomaly was detected. The data showed distinctive peaks indicating potential gear wear or misalignment. Augury’s advanced algorithms analyzed the low-frequency data, and identified these subtle changes. An alert was sent, triggering a collaborative effort between Augury’s analysts and the customer’s maintenance team. 

The maintenance team’s initial inspection of lubrication and other elements appeared normal. However, looking at the persistent abnormal data, Augury’s vibration analysts guided the maintenance team to look for specific signs of mechanical looseness in the internal gear. This data-driven approach led to the discovery and subsequent repair of a problem with the gear mesh. 

After the team made the necessary repairs, Augury’s Ultra Low RPM system continued to detect unusual vibration levels. Further inspection led to the discovery of a loose bolt, which was initially tightened but continued to cause issues. The customer’s team decided to reach out to the kiln manufacturer and they learned that a specialized wrench was required for proper tightening of the problematic bolt. Once the correct tool was acquired and used, Augury confirmed the machine finally returned to normal operation.

Crisis Averted, Downtime Avoided, Savings Realized

Without Augury’s low-RPM Machine Health solution and cross-team collaboration, the customer would have continued operating the kiln with undetected issues. The gear would have eventually failed catastrophically, potentially destroying the gearbox, damaging other kiln components, halting plant operations, and posing a severe safety hazard to workers.

Fortunately, with data-driven detection and early action, the customer avoided an estimated 3,696 hours of unplanned downtime. This translates to approximately 154 days of continuous operation and approximately $108,000 in repair costs.

In the metals and mining industry, where supply chain commitments are critical, the ability to deliver products on time can be the difference between retaining and losing major contracts. By collaborating with Augury and championing the new Ultra Low-RPM solution, this leading mining manufacturer not only avoided a potential disaster but reinforced their position as an industry leader committed to leveraging technology and partnership for continuous improvement.

The post Ultra-Low RPM Machine Challenge Prompts Tech Innovative and Collaboration, Yields $108K in Savings appeared first on Augury.