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Automated Welding With IO-Link

IO-Link technologies have been a game-changer for the welding industry. With the advent of automation, the demand for increasingly sophisticated and intelligent technologies has increased. IO-Link technologies have risen to meet this demand. Here I explain the concepts and benefits of I-O Link technologies and how they integrate into automated welding applications.

What are IO-Link technologies?

IO-Link technologies refer to an advanced communication protocol used in industrial automation. The technology allows data transfer, i.e., the status of sensors, actuators, and other devices through a one-point connection between the control system and individual devices. Also, it enables devices to communicate among themselves quickly and efficiently. IO-Link technologies provide real-time communication, enabling continuous monitoring of devices to ensure optimal performance.

Benefits of IO-Link technologies

    • Enhanced data communication: IO-Link technologies can transfer data between the control system and sensors or devices. This communication creates an open and transparent network of information, reflecting the real-time status of equipment and allowing for increased reliability and reduced downtime.
    • Cost-efficiency: IO-Link technologies do not require complicated wiring and can significantly reduce material costs compared to traditional hardwired solutions. Additionally, maintenance is easier and more efficient with communication between devices, and there is less need for multiple maintenance employees to manage equipment.
    • Flexibility: With IO-Link technologies, the control system can control and monitor devices even when not attached to specific operator workstations. It enables one control system to manage thousands of devices without needing to rewrite programming to accommodate different machine types.
    • Real-time monitoring: IO-Link technologies provide real-time monitoring of devices, allowing control systems to monitor failures before they occur, making it easier for maintenance teams to manage the shop floor.

How are IO-Link technologies used in automated welding applications?

Automated welding applications have increased efficiencies and continuity in processes, and IO-Link technologies have accelerated these processes further. Automated welding applications have different stages, and each step requires real-time monitoring to ensure the process is efficient and effective. IO-Link technologies have been integrated into various parts of the automated welding process, some of which include:

    1. Positioning and alignment: The welding process starts with positioning and aligning materials such as beams, plates, and pipes. IO-Link sensors can detect the height and gap position of the material before the welding process begins. The sensor sends positional data to the control system as a feedback loop, which then adjusts the positioning system using actuators to ensure optimal weld quality.
    2. Welding arc monitoring: The welding arc monitoring system is another critical application for IO-Link technologies. Monitoring the arc ensures optimal weld quality and runs with reduced interruptions. IO-Link temperature sensors attached to the welding tip help control and adjust the temperature required to melt and flow the metal, ensuring that the welding arc works optimally.
    3. Power supply calibration: IO-Link technologies are essential in calibrating the power output of welding supplies, ensuring consistent quality in the welding process. Detectors attached to the power supply record the energy usage, power output and voltage levels, allowing the control system to adjust as necessary.
    4. Real-time monitoring and alerting: Real-time monitoring and alerting capabilities provided by IO-Link technologies help to reduce downtime where machine health is at risk. The sensors monitor the welding process, determining if there are any deviations from the set parameters. They then communicate the process condition to the control system, dispatching alerts to maintenance teams if an issue arises.

Using IO-Link technologies in automated welding applications has revolutionized the welding industry, providing real-time communication, enhanced data transfer, flexibility, and real-time monitoring capabilities required for reliable processes. IO-Link technologies have been integrated at various stages of automated welding, including positioning and alignment, welding arc monitoring, power supply calibration, and real-time monitoring and alerting. There is no doubt that the future of automated welding is bright. With IO-Link technologies, the possibilities are endless, forging ahead to provide more intelligent, efficient, and reliable welding applications.

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Why Choose an IO-Link Ecosystem for Your Next Automation Project?

By now we’ve all heard of IO-Link, the device-level communication protocol that seems magical. Often referred to as the “USB of industrial automation,” IO-Link is a universal, open, and bi-directional communication technology that enables plug-and-play device replacement, dynamic device configuration, centralized device management, remote parameter setting, device level diagnostics, and uses existing sensor cabling as part of the IEC standard accepted worldwide.

But what makes IO-Link magical?

If the list above doesn’t convince you to consider using IO-Link on your next automation project, let me tell you more about the things that matter beyond its function as a communications protocol.

Even though these benefits are very nice, none of them mean anything if the devices connected to the network don’t provide meaningful, relevant, and accurate data for your application.

Evolution of the IO-Link

IO-Link devices, also known as “smart devices,” have evolved significantly over the years. At first, they were very simple and basic, providing data such as the status of your inputs and outputs and maybe giving you the ability to configure a few basic parameters, such as port assignment as an input or an output digitally over IO-Link. Next, came the addition of functions that would improve the diagnostics and troubleshooting of the device. Multi-functionality followed, where you have one device under one part number, and can configure it in multiple modes of operation.

Nothing, however, affected the development of smart devices as much as the introduction of IIoT (Industrial Internet of Things) and the demand for more real-time information about the status of your machine, production line, and production plant starting at a device level. This demand drove the development of smart devices with added features and benefits that are outside of their primary functions.

Condition monitoring

IO-Link supplies both sensor/actuator details and secure information
IO-Link supplies both sensor/actuator details and secure information

One of the most valuable added features, for example, is condition monitoring. Information such as vibration, humidity, pressure, voltage and current load, and inclination – in addition to device primary function data – is invaluable to determine the health of your machine, thus the health of your production line or plant.

IO-Link offers the flexibility to create a controls architecture independent of PLC manufacturer or higher-level communications protocols. It enables you to:

    • use existing low-cost sensor cabling
    • enhance your existing controls architecture by adding devices such as RFID readers, barcode and identification vision sensors, linear and pressure transducers, process sensors, discrete or analog I/O, HMI devices, pneumatic and electro-mechanical actuators, condition monitoring, etc.
    • dynamically change the device configuration, auto-configure devices upon startup, and plug-and-play replacement of devices
    • enable IIOT, predictive maintenance, machine learning, and artificial intelligence

There is no other device-level communications protocol that provides as many features and benefits and is cost-effective and robust enough for industrial automation applications as IO-Link.

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Demystifying Machine Learning

Machine learning can help organizations improve manufacturing operations and increase efficiency, productivity, and safety by analyzing data from connected machines and sensors, machine. For example, its algorithms can predict when equipment will likely fail, so manufacturers can schedule maintenance before problems occur, thereby reducing downtime and repair costs.

How machine learning works

Machine learning teaches computers to learn from data – to do things without being specifically told how to do them. It is a type of artificial intelligence that enables computers to automatically learn or improve their performances by learning from their experiences.

machine learning stepsImagine you have a bunch of toy cars and want to teach a computer to sort them into two groups: red and blue cars. You could show the computer many pictures of red and blue cars and say, “this is a red car” or “this is a blue car” for each one.

After seeing enough examples, the computer can start to guess which group a car belongs in, even if it’s a car that it hasn’t seen before. The machine is “learning” from the examples you show to make better and better guesses over time. That’s machine learning!

Steps to translate it to industrial use case

As in the toy car example, we must have pictures of each specimen and describe them to the computer. The image, in this case, is made up of data points and the description is a label. The sensors collecting data can be fed to the machine learning algorithm in different stages of the machine operation – like when it is running optimally, needs inspection, or needs maintenance, etc.

Data taken from vibration, temperature or pressure measures, etc., can be read from different sensors, depending on the type of machine or process to monitor.

In essence, the algorithm finds a pattern for each stage of the machine’s operation. It can notify the operator about what must be done given enough data points when it starts to veer toward a different stage.

What infrastructure is needed? Can my PLC do it?

The infrastructure needed can vary depending on the algorithm’s complexity and the data volume. Small and simple tasks like anomaly detection can be used on edge devices but not on traditional automation controllers like PLCs. Complex algorithms and significant volumes of data require more extensive infrastructure to do it in a reasonable time. The factor is the processing power, and as close to real-time we can detect the machine’s state, the better the usability.

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Using Guided Changeover to Reduce Maintenance Costs, Downtime

A guided changeover system can drastically reduce the errors involved with machine operation, especially when added to machines using fully automated changeovers. Processing multiple parts and recipes during a production routine requires a range of machines, and tolerances are important to quantify. Only relying on the human element is detrimental to profits, machine maintenance, and production volumes. Implementing operator assistance to guide visual guidance will reveal inefficiencies and allow for vast improvements.

Removing human error

Unverified manual adjustments may cause machine fatigue or failure. In a traditional manual changeover system, the frequency of machine maintenance is greater if proper tolerances are not observed at each changeover. Using IO-Link can remove the variable of human error with step-by-step instructions paired with precise sensors in closed-loop feedback. The machine can start up and run only when all parts are in the correct position.

Preventative maintenance and condition monitoring

Preventative maintenance is achievable with the assistance of sensors, technology, and systems. Using condition monitoring for motors, pumps and critical components can help prevent the need for maintenance and notably improve the effectiveness of maintenance with custom alerts and notifications with a highly useful database and graphing function.

A repeatable maintenance routine based on condition monitoring data and using a system to guide machine changeover will prolong machine life and potentially eliminate downtime altogether.

For more, read this real-world application story, including an automated format change to eliminate human error, reduce waste and decrease downtime.

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Automation Insights: Top Blogs From 2022

It’s an understatement to say 2022 had its challenges. But looking back at the supply chain disruptions, inflation, and other trials threatening success in many industries, including manufacturing, there were practical insights we can benefit from as we dive into 2023. Below are the most popular blogs from last year’s Automation Insights site.

    1. Evolution of Pneumatic Cylinder Sensors

Top 2022 Automation Insights BlogsToday’s pneumatic cylinders are compact, reliable, and cost-effective prime movers for automated equipment. They’re used in many industrial applications, such as machinery, material handling, assembly, robotics, and medical. One challenge facing OEMs, integrators, and end users is how to detect reliably whether the cylinder is fully extended, retracted, or positioned somewhere in between before allowing machine movement.

Read more.

    1. Series: Condition Monitoring & Predictive Maintenance 

Top 2022 Automation Insights BlogsBy 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.

Read the series, including the following blogs:

    1. Know Your RFID Frequency Basics

Top 2022 Automation Insights BlogsIn 2008 I purchased my first toll road RFID transponder, letting me drive through and pay my toll without stopping at a booth. This was my first real-life exposure to RFID, and it was magical. Back then, all I knew was that RFID stood for “radio frequency identification” and that it exchanged data between a transmitter and receiver using radio waves. That’s enough for a highway driver, but you’ll need more information to use RFID in an industrial automation setting. So here are some basics on what makes up an RFID system and the uses of different radio frequencies.

Read more.

    1. IO-Link Event Data: How Sensors Tell You How They’re Doing

Top 2022 Automation Insights BlogsI have been working with IO-Link for more than 10 years, so I’ve heard lots of questions about how it works. One line of questions I hear from customers is about the operating condition of sensors. “I wish I knew when the IO-Link device loses output power,” or, “I wish my IO-Link photoelectric sensor would let me know when the lens is dirty.” The good news is that it does give you this information by sending Event Data. That’s a type of data that is usually not a focus of users, although it is available in JSON format from the REST API.

Read more.

    1. Converting Analog Signals to Digital for Improved Performance

Top 2022 Automation Insights BlogsWe live in an analog world, where we experience temperatures, pressures, sounds, colors, etc., in seemingly infinite values. There are infinite temperature values between 70-71 degrees, for example, and an infinite number of pressure values between 50-51 psi.

Read more.

We appreciate your dedication to Automation Insights in 2022 and look forward to growth and innovation in 2023.

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Why Invest in Smart Manufacturing Practices?

We’re all privy to talks about smart manufacturing, smart factory, machine learning, IIOT, ITOT convergence, and so on, and many manufacturers have already embarked on their smart manufacturing journeys. Let’s take a pause and really think about it… Is it really important or is it a fad? If it is important, then why?

In my role traveling across the U.S. meeting various manufacturers and machine builders, I often hear about their needs to collect data and have certain types of interfaces. But they don’t know what good that data is going to do. Well, let’s get down to the basics and understand this hunger for data and smart manufacturing.

Manufacturing goals

Since the dawn of industrialization, the industry has been focused on efficiency – always addressing issues of how to produce more, better and quicker. The goal of manufacturing always revolved around these four things:

    1. Reduce total manufacturing and supply chain costs
    2. Reduce scrap rate and improve quality
    3. Improve/increase asset utilization and machine availability
    4. Reduced unplanned downtime

Manufacturing megatrends

While striving for these goals, we have made improvements that have tremendously helped us as a society to improve our lifestyle. But we are now in a different world altogether. The megatrends that are affecting manufacturing today require manufacturers to be even more focused on these goals to stay competitive and add to their bottom lines.

The megatrends affecting the whole manufacturing industry include:

    • Globalization: The competition for a manufacturer is no longer local. There is always somebody somewhere making products that are cheaper, better or more available to meet demand.
    • Changing consumer behavior: I am old enough to say that, when growing up, there were only a handful of brands and only certain types of products that made it over doorsteps. These days, we have variety in almost every product we consume. And, our taste is constantly changing.
    • Lack of skilled labor: Almost every manufacturer that I talk to expresses that keeping and finding good skilled people has been very difficult. The baby boomers are retiring and creating huge skills gaps in the workplaces.
    • Aging equipment: According to one study, almost $65B worth of equipment in the U.S. is outdated, but still in production. Changing regulations require manufacturers to track and trace their products in many industries.

Technology has always been the catalyst for achieving new heights in efficiency. Given the megatrends affecting the manufacturing sector, the need for data is dire. Manufacturers must make decisions in real-time and having relevant and useful data is a key to success in this new economy.

Smart manufacturing practices

What we call “smart manufacturing practices” are practices that use technology to affect how we do things today and improve them multifold. They revolve around three key areas:

    1. Efficiency: If a line is down, the machine can point directly to where the problem is and tell you how to fix it. This reduces downtime. Even better is using data and patterns about the system to predict when the machine might fail.
    2. Flexibility: Using technology to retool or change over the line quickly for the next batch of production or responding to changing consumer tastes through adopting fast and agile manufacturing practices.
    3. Visibility: Operators, maintenance workers, and plant management all need a variety of information about the machine, the line, or even the processes. If we don’t have this data, we are falling behind.

In a nutshell, smart manufacturing practices that focus on one or more of these key areas, helps manufacturers boost productivity and address challenges presented by the megatrends. Hence, it is important to invest in these practices to stay competitive.

One more thing: There is no finish line when it comes to smart manufacturing. It should become a part of your continuous improvement program to evaluate and invest in technology that offers you more visibility, improves efficiency, and adds more flexibility to how you do things.

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Industrial Machinery Failure Types and Implications for Maintenance Approaches

Industrial machinery can fail in many different ways and for many different reasons. For critical and/or expensive equipment, it is a major challenge to find a way to detect potential failures before they happen and to take action to prevent or minimize the effects. Closely tied to this is the tradeoff between the cost of detection and the cost of failure. We discussed some of these tradeoffs in the blog “Condition Monitoring & Predictive Maintenance: Cost-Benefit Tradeoffs.”

When assessing how equipment might fail, several industry studies* have identified six primary failure types which may be considered:

    • Type A: Lower probability of failure in early- and mid-life of the asset, with a dramatic increase in probability of failure in late-life. This is typical for mechanical devices, such as engines, fans, compressors, and machines.
    • Type B: Higher initial probability of failure when the asset is new, with a much lower/steady failure probability over the rest of the asset’s life. This is often the profile for electronic devices such as computers, PLCs, etc.
    • Type C: Lower initial probability of failure when the asset is new, with an increase to a steady failure probability in mid- and late-life. These are often devices with high stress work conditions, such as pressure relief valves.
    • Type D: Consistent probability of failure throughout the asset life, similar failure probability in early-, mid- and late-life. This can cover many types of industrial machines, often with stable, proven design and components.
    • Type E: Higher probability of failure in early- and late-life, a lower and constant probability of failure in mid-life (often called a “bathtub curve”). This can be devices that “settle in” after a wear-in period and then experience higher failures at the end of life, such as bearings.
    • Type F: Lower probability of failure when new, with a gradual increase over time and without the steep increase in failure probability at the end of life of Type A. This is often typical where age-based wear is steady and gradual in equipment such as turbine engines and structural components (pressure vessels, beams, etc.).

Age-related and non-age-related failures

These six failure types fall into two categories: age-related and non-age-related failures. The studies show that 15-30% of failures are age-related (Types A, E & F) and 70-85% of failures are non-age-related (Types B, C & D). The age-related failures have a clear correlation between the age of the asset and the likelihood of failure. In these cases, preventative maintenance at regular time-based intervals may be appropriate and cost-effective. The non-age-based failures are more “random,” due to improper design/installation, operator error, quality issues, machine overuse, etc. In these cases, preventative maintenance will likely not prevent failure and may waste time and money on unnecessary maintenance.

Table is based on data from studies conducted by United Airlines (1978), Broberg (1973), U.S. Navy (1993 MSDP) and U.S. Navy (2001 SUBMEPP) and ARC Consulting

The fact that approximately 80% of failures are non-age-related has major implications for manufacturers trying to decide on a maintenance approach. The traditional preventative-maintenance approach is not likely to address these failures and may even cause failures when improperly done. It is therefore important to consider a more proactive approach, such as condition-based monitoring or predictive maintenance, especially for assets that are critical to the process and/or expensive.

Preventative maintenance and regular inspection may be a good approach for assets more likely to experience age-based failures in Types A, E, and F. These include fans, bearings, and structural components – and in many cases, the cost of condition monitoring or predictive maintenance may not be worth the cost. But for critical components or equipment, such as bearings on an expensive milling machine or transfer line, it may be worthwhile to apply condition monitoring or predictive maintenance.

And when the assets are more likely to experience non-age-related failures (Types B, C, and D), the proactive approaches are better. Many industrial machines and industrial control/motion components fall into this category, and condition monitoring or predictive maintenance can significantly reduce preventative maintenance costs and unplanned failures while improving machine uptime and Overall Equipment Effectiveness.

You can use this information to improve your maintenance operations. Start by considering your maintenance approach(es), especially for your most critical assets:

    • Are they more likely to experience age-related failures or non-age-related failures?
    • Should you change your maintenance approach to be more proactive?
    • What components and indicators should you measure?

We’ll discuss ideas on how to assess your equipment for condition monitoring/predictive maintenance and what you might measure in separate blogs.

* Studies conducted by United Airlines (1978), Broberg (1973), U.S. Navy (1993 MSDP) and U.S. Navy (2001 SUBMEPP)

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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.

Other related Automation Insights blogs:

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How IO-Link Sensors With Condition Monitoring Features Work With PLCs

As manufacturers continually look for ways to maximize productivity and eliminate waste, automation sensors are taking on a new role in the plant. Once, sensors were used only to provide detection or measurement data so the PLC could process it and run the machine. Today, sensors with IO-Link measure environmental conditions like temperature, humidity, ambient pressure, vibration, inclination, operating hours, and signal strength. By setting alarm thresholds, it’s possible to program the PLC to use the resulting condition monitoring data to keep machines running smoothly.

Real-time data for real-time response

A sensor with condition monitoring features allows a PLC to use real-time data with the same speed it uses a sensor’s primary process data. This typically requires setting an alarm threshold at the sensor and a response to those alarms at the PLC.

When a vibration threshold is set up on the sensor and vibration occurs, for example, the PLC can alert the machine operator to quickly check the area, or even stop the machine, to look for a product jam, incorrect part, or whatever may be causing the vibration. By reacting to the alarm immediately, workers can reduce product waste and scrap.

Inclination feedback can provide diagnostics in troubleshooting. Suppose a sensor gets bumped and no longer detects its target, for example. The inclination alarm set in the sensor will indicate after a certain degree of movement that the sensor will no longer detect the part. The inclination readout can also help realign the sensor to the correct position.

Detection of other environmental factors, including humidity and higher-than-normal internal temperatures, can also be set, providing feedback on issues such as the unwanted presence of water or the machine running hotter than normal. Knowing these things in real-time can stop the PLC from running, preventing the breakdown of other critical machine components, such as motors and gearboxes.

These alarm bits can come from the sensors individually or combined together inside the sensor. Simple logic, like OR and AND statements, can be set on the sensor in the case of vibration OR inclination OR temperature alarm OR humidity, output a discrete signal to pin 2 of the sensors. Then pin 2 can be fed back through the same sensor cable as a discrete alarm signal to the PLC. A single bit showing when an alarm occurs can alert the operator to look into the alarm condition before running the machine. Otherwise, a simple ladder rung can be added in the PLC to look at a single discrete alarm bit and put the machine into a safe mode if conditions require it.

In a way, the sensor monitors itself for environmental conditions and alerts the PLC when necessary. The PLC does not need to create extra logic to monitor the different variables.

Other critical data points, such as operating hours, boot cycle counters, and current and voltage consumption, can help establish a preventative and predictive maintenance schedule. These data sets are available internally on the sensors and can be read out to help develop maintenance schedules and cut down on surprise downtimes.

Beyond the immediate benefits of the data, it can be analyzed and trended over time to see the best use cases of each. Just as a PLC shouldn’t be monitoring each alarm condition individually, this data must not be gathered in the PLC, as there is typically only a limited amount of memory, and the job of the PLC is to control the machines.

This is where the IT world of high-level supervision of machines and processes comes into play. Part two of my blog will explore how to integrate this sensor data into the IT level for use alongside the PLC.

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Condition Monitoring & Predictive Maintenance: Addressing Key Topics in Packaging

A recent study by the Packaging Machinery Manufacturers Institute (PMMI) and Interact Analysis takes a close look at packaging industry interest and needs for Condition Monitoring and Predictive Maintenance. Customer feedback reveals interesting data on packaging process pain points and the types of machines and components which are best monitored, the data which should be gathered, current maintenance approaches, and the opportunity for a better way: Condition Monitoring and Predictive Maintenance.

What keeps customers awake at night?

The PMMI survey indicates that form, fill & seal machines are very critical to packaging processes and more likely to fail than many other machines. Also critical to the process and a common failure point are filling & dosing machines, and labeling machines.

These three categories of machines are in use in primary packaging and are often the key components in the production line; the downstream processes are usually less critical. They often process a lot of perishable products at high speeds, therefore, any downtime is a big problem for overall equipment effectiveness (OEE), quality, and profitability.

In terms of the components on these machines that are most likely to fail, the ones are pneumatic systems, gearboxes, motors/drives, and sensors.

How can customers reduce unplanned downtime and improve OEE?

Our data shows that the top customer issue is unplanned machine breakdowns, but many packaging firms use reactive or preventative maintenance approaches, which may not be effective for most failures. An ARC study found that only about 20% of failures are age-related. The 80% of failures that are non-age-related would likely not be addressed by reactive or preventative maintenance programs.

A better way to address these potential failures is to monitor the condition of critical machines and components. Condition monitoring can provide early detection of machine deterioration or impending failure and the data can be used for predictive maintenance. Many “smart sensors” can now measure vibration, temperature, humidity, pressure, flow, inclination, and many other attributes which may be helpful in notifying users of emerging problems. And some of these “smart sensors” can also “self-monitor” and help alert users to potential failures in the sensor itself.

What are packaging customers actually doing?

The good news is that the packaging industry is moving forward to find a better way and users understand that Condition Monitoring/Predictive Maintenance gives them the opportunity to prevent unplanned failures, reduce unplanned downtime, and improve OEE, quality and profitability. About 25% of customers have already implemented some sort of Condition Monitoring / Predictive Maintenance, while about 20% are piloting it and 30% plan to implement it. This means that 75% of customers are very interested in Condition Monitoring/Predictive Maintenance, by far the most interest in any technology discussed in the PMMI survey.

Where do you start?

    • Look for the machines which cause you the most frustration. PMMI identified form, fill & seal, filling & dosing, and labeling machines, but there are other machines, including bottling, cartoning, and case/tray handling, that could fail and cause production downtime or damaged product.
    • Consider where, when, and how equipment can fail. Look to your own experience, ask partners with similar machines or perhaps the equipment supplier to help you determine the most common failure points and modes.
    • Analyze which parts of the machine fail. Moving parts are usually the highest potential failure point. On packaging machines, these include motors, gearboxes, fans, pumps, bearings, conveyors, and shafts.
    • Consider what to measure. Vibration is common, and often assessed in combination with temperature and humidity. On some machines, pressure, flow, or amperage/voltage should be measured.
    • Determine the most appropriate maintenance program for each machine. Consider the costs/benefits of reactive, preventative, condition-based monitoring or predictive approaches. In some cases, it may be OK to let a non-critical, low-value asset “run-to-failure,” while in other cases it might be worth investing in Condition Monitoring or Predictive Maintenance to prevent a critical machine’s costly failure.
    • Start small by implementing condition monitoring on one or two machines, and then scaling up once you’ve learned what does and doesn’t work. Using a low-cost sensor, which can be easily integrated with existing controls architectures or added on externally, is also a great way to start.

Condition Monitoring and Predictive Maintenance offer packaging firms a “better way” to address key topics including machine downtime, failures, and OEE. Users can move from a reactive to a proactive maintenance approach by monitoring attributes such as vibration and temperature on critical machines and then analyzing the data. This will allow them to detect and predict potential failures before they become critical, and thereby, reduce unplanned downtime, improve OEE, and save money.