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Predictive Maintenance vs. Predictive Analytics, What’s the Difference?

With more and more customers getting onboard with IIoT applications in their plants, a new era of efficiency is lurking around the corner. Automation for maintenance is on the rise thanks to a shortage of qualified maintenance techs coinciding with a desire for more efficient maintenance, reduced downtime, and the inroads IT is making on the plant floor. Predictive Maintenance and Predictive Analytics are part of almost every conversation in manufacturing these days, and often the words are used interchangeably.

This blog is intended to make the clear distinction between these phrases and put into perspective the benefits that maintenance automation brings to the table for plant management and decision-makers, to ensure they can bring to their plants focused innovation and boost efficiencies throughout them.

Before we jump into the meat of the topic, let’s quickly review the earlier stages of the maintenance continuum.

Reactive and Preventative approaches

The Reactive and Preventative approaches are most commonly used in the maintenance continuum. With a Reactive approach, we basically run the machine or line until a failure occurs. This is the most efficient approach with the least downtime while the machine or line runs. Unfortunately, when the machine or line comes to a screeching stop, it presents us with the most costly of downtimes in terms of time wasted and the cost of machine repairs.

The Preventative approach calls for scheduled maintenance on the machine or line to avoid impending machine failures and reduce unplanned downtimes. Unfortunately, the Preventative maintenance strategy does not catch approximately 80% of machine failures. Of course, the Preventative approach is not a complete waste of time and money; regular tune-ups help the operations run smoother compared to the Reactive strategy.

Predictive Maintenance vs. Predictive Analytics

As more companies implement IIoT solutions, data has become exponentially more important to the way we automate machines and processes within a production plant, including maintenance processes. The idea behind Predictive Maintenance (PdM), aka condition-based maintenance, is that by frequently monitoring critical components of the machine, such as motors, pumps, or bearings, we can predict the impending failures of those components over time. Hence, we can prevent the failures by scheduling planned downtime to service machines or components in question. We take action based on predictive conditions or observations. The duration between the monitored condition and the action taken is much shorter here than in the Predictive Analytics approach.

Predictive Analytics, the next higher level on the maintenance continuum, refers to collecting the condition-based data over time, marrying it with expert knowledge of the system, and finally applying machine learning or artificial intelligence to predict the event or failure in the future. This can help avoid the failure altogether. Of course, it depends on the data sets we track, for how long, and how good our expert knowledge systems are.

So, the difference between Predictive Maintenance and Predictive Analytics, among other things, is the time between condition and action. In short, Predictive Maintenance is a stepping-stone to Predictive Analytics. Once in place, the system monitors and learns from the patterns to provide input on improving the system’s longevity and uptime. Predictive Maintenance or Preventative Maintenance does not add value in that respect.

While Preventative Maintenance and Predictive Maintenance promises shorter unplanned downtimes, Predictive Analytics promises avoidance of unplanned downtime and the reduction of planned downtime.

The first step to improving your plant floor OEE is with monitoring the conditions of the critical assets in the factory and collecting data regarding the failures.

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Looking Into & Through Transparent Material With Photoelectric Sensors

Advance automated manufacturing relies on sensor equipment to ensure each step of the process is done correctly, reliably, and effectively. For many standard applications, inductive, capacitive, or basic photoelectric sensors can do a fine job of monitoring and maintaining the automated manufacturing process. However, when transparent materials are the target, you need a different type of sensor, and maybe even need to think differently about how you will use it.

What are transparent materials?

When I think of transparent materials, water, glass, plexiglass, polymers, soaps, cooling agents, and packaging all come to mind. Because transparent material absorbs very little of the emitted red LED light, standard photoelectric sensors struggle on this type of application. If light can make its way back to the receiver, how can you tell if the beam was broken or not? By measuring the amount of light returned, instead of just if it is there or not, we can detect a transparent material and learn how transparent it is.

Imagine being able to determine proper mixes or thicknesses of liquid based on a transparency scale associated to a value of returned light. Another application that I believe a transparent material photoelectric senor would be ideal for is the thickness of a clear bottle. Imagine the wall thickness being crucial to the integrity of the bottle. Again, we would measure the amount of light allowed back to the receiver instead of an expensive measurement laser or even worse, a time-draining manual caliper.

Transparent material sensor vs. standard photoelectric sensor

So how does a transparent material sensor differ from a standard photoelectric sensor? Usually, the type of light is key. UV light is absorbed much greater than other wavelengths, like red or blue LEDs you find in standard photoelectric sensors. To add another level, you polarize that UV light to better control the light back into the receiver. Polarized UV light with a polarized reflector is the best combination. This can be done on a large or micro scale based on the sensor head size and build.

Uses for transparent material sensor include packaging trays, level tubes, medical tests, adhesive extrusion, and bottle fill levels, just to name a few. Transparent materials are everywhere, and the technology has matured. Make sure you are looking into specialized sensor technologies and working through best set-up practices to ensure reliable detection of transparent materials.

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Tire Industry Automation: When a Photo-Eye Is Failing, Try an Ultrasonic Sensor

Should you use a photo-eye or an ultrasonic sensor for your automation application? This is a great question for tire industry manufacturing.

I was recently at a tire manufacturing plant when a maintenance technician asked me to suggest a photoelectric sensor for a large upgrade project he had coming up. I asked him about the application, project, and what other sensors he was considering.

His reply was a little startling. He said he had always used photo-eyes, but he couldn’t find a dependable one, so he would continually try different brands. My experience in this industry, along with good sensor training and advice from my colleague Jack Moermond, Balluff Sensor Products Manager, immediately made me think that photo-eye sensors were not the right choice for this application.

As I asked more questions, the problem became clear. The tire material the technician was detecting was black and dull. This type of material absorbs light and does not reflect it reliably back to the sensor. Also, environmental factors, such as dust and residue, can diminish a photo-eye’s signal quality.

Ultrasonic sensors for non-reflective materials and harsh environments

The technician didn’t have much experience with ultrasonic sensors, so I went on to explain why these may be a better solution for his application.

While photoelectric sensors send light beams to detect the presence of or measure the distance to an object, ultrasonics bounce sound waves off a target. This means that ultrasonics can be used in applications where an object’s reflectivity isn’t predictable, like with liquids, clear glass or plastic, or other materials. Dust build up on the face of an ultrasonic sensor does not give a false output. Ultrasonic sensors actually have a dead zone a few millimeters from the face where they won’t detect an object until the wave clears the dead zone, so take this into consideration when planning where to install an ultrasonic sensor.

Tire detection for process reliability with BUS ultrasonic sensors

Tire industry applications

The following are some popular tire industry applications where it might be better to choose an ultrasonic sensor over a photo-eye sensor.

    • The tire building process requires a lot of winding and unwinding of material to build the different layers of a tire. As this material is fed through the machines it starts to sag and loop. An ultrasonic sensor in this location will monitor how much sag and loop is in the process.
    • When tires are being loaded into curing presses, the press needs to confirm that the correct size tire is in place. An ultrasonic sensor can measure the height or width of the tire from the sides or top for confirmation.
    • Ultrasonic sensors are great at detecting if a tire or material is in place before a process starts.
    • Hydraulic systems are common in tire manufacturing. Ultrasonic sensors are good for hydraulic fluid level monitoring. Tying them to a SmartLight offers a visual reference and alarm output if needed.
    • Everyone knows the term “wig-wag” in tire mixing and extrusion. The sheets of wig-wag require monitoring as they are fed through the process. When this material gets close to being used up, a new wig-wag must be used.
Wig-wag stacks

So, when there is an application for a photo-eye, especially in a tire manufacturing plant, keep in mind that, rather than a photoelectric sensor, an ultrasonic may be a better option.

The maintenance technician I spoke with is now looking at different options of ultrasonics to use. He said I gave him something new to think about for his machines and opened the door for adding this technology to his plant.

Happy hunting!

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Identify Failures Before They Happen: The PF Curve

The P-F curve is often mentioned in condition monitoring and predictive maintenance discussions. “P-F” refers to the interval between the detection of a potential failure (P) and the occurrence of a functional failure (F).The P-F curve is an illustrative generalization of what happens to an asset, machine or component as it ages, degrades, and eventually fails. It shows the different stages of an asset’s life, how machine failures progress, and how and when different symptoms emerge which might signal impending (or actual) failure.

The time scale in Fig. 1 is obviously exaggerated, and most assets operate for a lengthy period of time before failure starts to occur. The steepness of the failure portion of the curve can vary from asset to asset, but it generally follows the same pattern as shown in the diagram.

At first, performance degradation is minor and may not require significant action. As time progresses, the potential failure indicators become stronger and more easily detectable and the performance degradation becomes more severe, eventually ending in catastrophic failure.

The timeline is split into three domains:

      • Proactive domain – the failure is relatively far off (machine may still be new). Proactive activities include designing for reliability, precision installation & alignment and life cycle asset management. These can significantly extend the time until potential and functional failures occur.
      • Predictive domain – the failure may still be far off, but symptoms are emerging and offer (relatively) early warning signs. Timely action may be taken to prevent failure or replace failing equipment before catastrophic failure occurs.
      • Fault domain – the failure is occurring or inevitable, and symptoms indicate immediate action is needed to address the failure.

During these domains, different indicators/symptoms emerge. Ultrasonic, vibration and oil analysis often signal problems early; then temperature rise and noise emerge a bit later; and finally, parts come loose and more severe damage occurs. Depending on the asset, other indicators may be shown by activities including corrosion monitoring, motor current/power analysis and process parameter trending (e.g., flows, rates, pressures, temperatures, etc.).

By analyzing which symptoms of failure are likely to appear in the predictive domain for a given piece of equipment, you can determine which failure indicators to prioritize in your own condition monitoring and predictive maintenance discussions.

Click here to read more about condition monitoring.

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Manufacturing Insights: Top Blogs From 2021

While last year was filled with challenges and unexpected changes for many industries, including manufacturing, it was not without positive achievements and insights. As we look forward to 2022, let’s not forget some topics that shaped 2021, including our five most-read blogs.

1. 5 Manufacturing Trends to Consider as You Plan for 2022

 

 

 

 

It’s that time of year again where we all start to forget the current year (maybe that’s OK) and start thinking of plans for the next – strategy and budget season! 2022 is only a few weeks away! I thought I’d share 5 insights I’ve had about 2022 that you might benefit from as you start planning for next year.

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2. The Pros and Cons of Flush, Non-Flush and Semi-Flush Mounting


Inductive proximity sensors have been around for decades and have proven to be a groundbreaking invention for the world of automation. This type of technology detects the presence or absence of ferrous objects using electromagnetic fields. Manufacturers typically select which inductive sensor to use in their application based on their form factor and switching distance. Although, another important factor to consider is how the sensor will be mounted.

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3. IO-Link Wireless – IO-Link with Even Greater Flexibility



In a previous blog entry, I discussed IO-Link SPE (Single-Pair Ethernet). SPE, in my opinion, has two great strengths compared to standard IO-Link: cable length and speed. With cable lengths of up to 100 meters and speed of 10 Mbps, compared to 20 meters and max baud rate of 230.4 Kbps, what could be out of reach?

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4. How Condition Monitoring has Evolved and Its Role in IIoT

In recent years, as IIoT and Industry 4.0 have become part of our everyday vocabulary, we’ve also started hearing more about condition monitoring, predictive maintenance (PdM) and predictive analytics. Sometimes, we use these terms interchangeably as well. Strictly speaking, condition monitoring is a root that enables both predictive maintenance and predictive analytics. In today’s blog we will brush up a little on condition monitoring and explore its lineage.

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5. Lithium Ion Battery Manufacturing – RFID is on a Roll



With more and more consumers setting their sights on ‘Drive Electric,’ manufacturers must prepare themselves for alternative solutions to combustion engines. This change will no doubt require an alternative automation strategy for our electric futures.

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Honorable Mention: Top 5 Insights From 2020

And, finally, for the sake of comparison, we can’t help but honorably mention last year’s look-back blog. The top five insights from 2020 include buying a machine vision system; data provided by IO-Link; changes in electrostatic sensing field by capacitive sensors; reducing the number of ethernet nodes on your network using IO-Link; and adding a higher level of visibility to older automation machines.

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We appreciate your dedication to Automation Insights in 2021 and look forward to growth and innovation in 2022!