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.
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.
Evolution of Pneumatic Cylinder Sensors
Today’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.
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.
In 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.
IO-Link Event Data: How Sensors Tell You How They’re Doing
I 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.
Converting Analog Signals to Digital for Improved Performance
We 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.
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)
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.
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.
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.
If you have ever walked through a stamping department at a metal forming facility, you have heard the rhythmic sound of the press stamping out parts, thump, thump. The stamping department is the heart manufacturing facility, and the noise you hear is the heartbeat of the plant. If it stops, the whole plant comes to a halt. With increasing demands for higher production rates, less downtime, and reduction in bad parts, stamping departments are under ever-increasing pressure to optimize the press department through die protection and error-proofing programs.
The die-protection risk assessment team
The first step in implementing or optimizing a die protection program is to perform a die-protection risk assessment. This is much like risk assessments conducted for safety applications, except they are done for each die set. To do this, build a team of people from various positions in the press department like tool makers, operators, and set-up teams.
Once this team is formed, they can help identify any incidents that could occur during the stamping operations for each die set and determine the likelihood and the severity of possible harm. With this information, they can identify which events have a higher risk/severity and determine what additional measures they should implement to prevent these incidents. An audit is possible even if there are already some die protection sensors in place to determine if there are more that should be added and verify the ones in place are appropriate and effective.
The top 4 die processes to check
The majority of quality and die protection problems occur in one of these three areas: material feed, material progression, and part- and slug-out detections. It’s important to monitor these areas carefully with various sensor technologies.
Material feed
Material feed is perhaps the most critical area to monitor. You need to ensure the material is in the press, in the correct location, and feeding properly before cycling the press. The material could be feeding as a steel blank, or it could come off a roll of steel. Several errors can prevent the material from advancing to the next stage or out of the press: the feed can slip, the stock material feeding in can buckle, or scrap can fail to drop and block the strip from advancing, to name a few. Inductive proximity sensors, which detect iron-based metals at short distances, are commonly used to check material feeds.
Material progression
Material progression is the next area to monitor. When using a progressive die, you will want to monitor the stripper to make sure it is functioning and the material is moving through the die properly. With a transfer die, you want to make sure the sheet of material is nesting correctly before cycling the press. Inductive proximity sensors are the most common sensor used in these applications, as well.
Here is an example of using two inductive proximity sensors to determine if the part is feeding properly or if there is a short or long feed. In this application, both proximity sensors must detect the edge of the metal. If the alignment is off by just a few millimeters, one sensor won’t detect the metal. You can use this information to prevent the press from cycling to the next step.
Short feed, long feed, perfect alignment
Part-out detection
The third critical area that stamping departments typically monitor is part-out detection, which makes sure the finished part has come out of the stamping
area after the cycle is complete. Cycling the press and closing the tooling on a formed part that failed to eject can result in a number of undesirable events, like blowing out an entire die section or sending metal shards flying into the room. Optical sensors are typically used to check for part-out, though the type of photoelectric needed depends on the situation. If the part consistently comes out of the press at the same position every time, a through-beam photo-eye would be a good choice. If the part is falling at different angles and locations, you might choose a non-safety rated light grid.
Slug-ejection detection
The last event to monitor is slug ejection. A slug is a piece of scrap metal punched out of the material. For example, if you needed to punch some holes in metal, the slug would be the center part that is knocked out. You need to verify that the scrap has exited the press before the next cycle. Sometimes the scrap will stick together and fail to exit the die with each stroke. Failure to make sure the scrap material leaves the die could affect product quality or cause significant damage to the press, die, or both. Various sensor types can ensure proper scrap ejection and prevent crashes. The picture below shows a die with inductive ring sensors mounted in it to detect slugs as they fall out of the die.
Just like it is important to get regular checkups at the doctor, performing regular die-protection assessments can help you make continuous improvements that can increase production rates and reduce downtime. Material feed, material progression, part-out and slug-out detection are the first steps to optimize, but you can expand your assessments to include areas like auxiliary equipment. You can also consider smart factory solutions like intelligent sensors, condition monitoring, and diagnostics over networks to give you more data for preventative maintenance or more advanced error-proofing. The key to a successful program is to assemble the right team, start with the critical areas listed above, and learn about new technologies and concepts that are becoming available to help you plan ways to improve your stamping processes.
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.
The Industrial Internet of Things (IIoT) may seem large, intimidating, and challenging to implement; however, new systems and solutions will eliminate the perceived barriers for entry. As we wrap up the year and make plans for 2022, now is a great time to resolve to modernize your facility.
Do you have a process, system or machine that has outlived its life expectancy for many years or even decades and isn’t up to current IIoT standards? Great news: you have several options for updating.
Traditional approach
The traditional approach allows you to use your current controller to output your information to your existing database. If you want to try IIoT on your current setup and your controller cannot be modified, a self-contained system will allow for ultimate flexibility. It will provide you with access to the data based off an extra layer of sensing with a focus on condition monitoring. This approach is the least expensive route, however, if database access is restricted the following options may be better choices.
Cloud-based current industry standard
A second option is to use a portable monitoring system that has a condition monitoring sensor. It is essentially five sensors in one package that can hook up to a system using the cellular network to report data to a secure cloud database. This approach is useful in remote locations or where local network access is limited. If you have a problem area, you can apply this temporarily to collect enough data, enabling you to implement predictive maintenance.
Local-based current industry standard
A local self-contained system is a great solution if a cloud database is not desired or allowed. Systems such as a Condition Monitoring Toolkit allow for recording of devices onto the local memory or USB drive. Additionally, multiple alarm set points can be emailed or extracted locally. This approach is best for testing existing machines to help with predictive maintenance, to improve a process, or even to prevent a failure.
All three of these options require data management and analysis to improve your processor and to remedy problematic areas. Using any of them is an opportunity to test the IIoT waters before fully diving in. Extrapolating the results into problem-solving solutions can allow you to expand IIoT to the rest of your facilities in a cost-effective manner.
As technology advances at a faster pace and the world becomes flatter, manufacturing operations are generally focused on efficient production to maximize profitability for the organization. In the new era of industrial automation and smart manufacturing, organizations are turning to data generated on their plant floors to make sound decisions about production and process improvements.
Smart manufacturing improvements can be divided roughly into six different segments: Predictive Analytics, Track and Trace, Error Proofing, Predictive Maintenance, Ease of Troubleshooting, and Remote Monitoring.To implement any or all of these improvements requires interoperable systems that can communicate effectively and sensors and devices with the ability to provide the data required to achieve the manufacturer’s goals. For example, if the goal is to have error free change-overs between production cycles, then feedback systems that include identification of change parts, measurements for machine alignment changes, or even point of use indication for operators may be required. Similarly, to implement predictive maintenance, systems require devices that provide alerts or information about their health or overall system health.
Traditional control system integration methods that rely heavily on discrete or analog (or both) modes of communication are limited to specific operations. For example, a 4-20mA measurement device would only communicate a signal between 4-20mA. When it goes beyond those limits there is a failure in communication, in the device or in the system. Identifying that failure requires manual intervention for debugging the problem and wastes precious time on the manufacturing floor.
The question then becomes, why not utilize only sensors and devices with networking ability such as a fieldbus node? This could solve the data and interoperability problems, but it isn’t an ideal solution:
Most fieldbuses do not integrate power and hence require devices to have separate power drops making the devices bulkier.
Multiple fieldbuses in the plant on different machines requires the devices to support multiple fieldbus/network protocols. This can be cost prohibitive, otherwise the manufacturer will need to stock all varieties of the same sensor.
Several of the commonly used fieldbuses have limitations on the number nodes you can add — in general 256 nodes is capacity for a subnet. Additional nodes requires new expensive switches and other hardware.
IO-Link provides one standard device level communication that is smart in nature and network independent, thus it enables interoperability throughout the controls pyramid making it the most suitable choice for smart manufacturing.
We will go over more specific details on why IO-Link is the best suited technology for smart manufacturing in next week’s blog.
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.
To implement any or all of these improvements requires interoperable systems that can communicate effectively and sensors and devices with the ability to provide the data required to achieve the manufacturer’s goals. For example, if the goal is to have error free change-overs between production cycles, then feedback systems that include identification of change parts, measurements for machine alignment changes, or even point of use indication for operators may be required. Similarly, to implement predictive maintenance, systems require devices that provide alerts or information about their health or overall system health.
IO-Link provides one standard device level communication that is smart in nature and network independent, thus it enables interoperability throughout the controls pyramid making it the most suitable choice for smart manufacturing.