Implement a Smart Factory Using Available Technologies

What is a Smart Factory?

The term smart factory describes a highly digitalized and connected system where machines and equipment using sensor technology improves processes through monitoring, automation, and optimization. The wealth of data enables predictive maintenance and an increase in productivity through planning and decreased downtime.

The smart factory’s core building blocks are various intelligent sensors that provide a critical measure for the machine’s health, such as temperature, vibration, and pressure. This data combined with production, information, and communication technologies forms the backbone of what many refer to as the next industrial revolution, i.e., Industry 4.0.

The technologies that make the Industrial Internet of things or Industry 4.0 possible have always been available for the information technology domain. The same technology and software can be used to implement the next generation of industries.

How would I go about implanting these technologies?

The prerequisite to implementing any smart factory is using a sensor(s) with the ability to provide sensing information and to monitor its health. For example, an optical laser sensor can measure distance and monitor the beam’s strength reflected, alerting that the glass window might be foggy or dirty. These sensors are readily available in the market as most IO-Link sensors come with the diagnostics inbuilt. However, it varies from vendor to vendor.

The second step is getting the data from the operational technology side to the information technology level. The industrial side of things uses PLCs for control, which should be left alone as the single source of control for security reasons and efficiency. However, most IO-Link-enabled network blocks can tap into this data in read-only mode using JSON (JavaScript Object Notation) or a REST API.  With the IO-Link consortium officially formalizing the REST API, we will see more and more vendors adopting it as a feature for their network blocks

The final step is using this data to visualize and optimize the process. There are various SCADA and MES software systems that make it possible to do this without much development. But for maximum customizability, it’s recommended to build a stack that fits your needs and provides the option to scale. There are very mature open-source software options and applications that have been in used in the IT world for decades now and transfer seamlessly to the industrial side.

A data visualization of the current and amperage of an IO-Link device

The stack I have personally used and seen other companies implement is Grafana as a dashboarding software, InfluxdB as a time-series database, telegraf as a collector, and Mosquitto as MQTT broker.

The possibilities for expansion are limitless, leaving the option to add another service like TensorFlow for some analytics.

All of these are deployed as container services using Docker, another open-source project. This helps for easy deployment and maintenance.

A demonstration of this stack can be seen at the following link

https://balluff.app

Username and password are both “balluff” (all lowercase).

Machine Vision: A Twenty-first Century Automation Solution

Lasers, scanners, fingerprint readers, and face recognition is not just science fiction anymore.  I love seeing technology only previously imagined become reality through necessity and advances in technology.  We, as a world economy, need to be able to verify who we are and ensure transitions are safe, and material and goods are tracked accurately.  With this need came the evolution of laser barcode readers, fingerprint identification devices, and face ID on your phone.  Similar needs have pushed archaic devices to be replaced within factory automation for data collection.

When I began my career in control engineering the 1990s high tech tools were limited to PLCs, frequency drives, and HMIs. The quality inspection data these devices relied on was collected mostly through limit switches and proximity sensors.  Machine vision was still in it’s expensive and “cute” stage.  With the need for more information, seriously accurate measurement, machining specs, and speed; machine vision has evolved, just like our personal technology has, to fill the needs of the modern time.

Machine vision has worked its way into the automation world as a need to have rather than a nice to have.  With the ability to stack several tools and validations on top of each other, within a fraction of a second scan we now have the data our era needs to stay competitive.  Imagine an application requiring you to detect several material traits, measure the part, read a barcode for tracking, and validate  a properly printed logo screened onto the finished product.  Sure, you could use several individual laser sensors, barcode readers and possibly even a vision sensor all working in concert to achieve your goal.  Or you could use a machine vision system to do all the above easily with room to grow.

I say all of this because there is still resistance in the market to move to machine vision due to historical high costs and complexity.  Machine Vision is here to stay and ready for your applications today.  Think of it this way.  How capable would you think a business is they took out a carbon copy credit card machine to run a payment for you?  Well, think of this before you start trying to solve applications with several sensors.  Take advantage of the technology at your fingertips; don’t hold on to nostalgia.

Industry 4.0: What It Is and How It Improves Manufacturing

Industry 4.0 is a common buzzword that is thrown around along with IIoT and Process visualization but what does that mean and how is it integrated into a manufacturing process? Industry 4.0 refers to the fourth industrial revolution. The first dealing with mechanization and the use of steam and water power, the second referring to mass production using assembly lines and electrical power, and the third referring to automated production and the use of computers and robots. Industry 4.0 takes us a step beyond that to smart factories that include automation and machine learning. Again, buzzwords that can be hard to visualize.

A commonplace example of this would be self-driving cars. They are autonomous because they don’t need a person operating them and they take, in real time, information about their surroundings and use that to determine a course of action. But how can this type of technology affect a manufacturing process?

Industry 4.0 requires data to be analyzed. This is where IO-Link comes into play. With IO-Link, you are able to get information from a sensor more than than just an output signal when it detects a part. A photoelectric sensor is a good example of this. The basic way a photoelectric sensor works an output is given depending on the amount of light being received. If the sensor happens to be in a dirty/dusty environment, there could be dirt collecting on the lens or floating in the air which effects the amount of light being received. An IO-Link (smart) sensor can not only fire an output when detection occurs but can give information about the real time gain of the sensor (how much light is being received). If the gain drops below a certain amount because of dirt on the lens or in the air, it can send another signal to the controller indicating the change in gain.

Now that we have more data, what are we going to do with it?

We now have all of this data coming from different parts of the machine, but where does it go and what do we do with it? This is where process visualization comes into play. We are able to take real time data from a machine and upload it to a database or system that we can monitor outside of the plant floor. We can know if a machine is running properly without having to physically see the machine. The information can also give us indications about when something might fail so preventative maintenance can take place and reduce downtime.

As more manufacturing processes are becoming automated, machines are becoming more and more complex. A machine might be needed to run 6-7 different lines rather than just 1 or 2 which can involve things like tool change or settings changes. Then, more checks need to be in place, so the right process is running for the right part. Industry 4.0 is how we are able to gather all this information and use it to increase efficiency and productivity.

Adding Smart Condition Monitoring Sensors to Your PLC Control Systems Delivers Data in Real Time

Condition monitoring of critical components on machines delivers enormous benefits to productivity in a plant.  Rather than have a motor, pump, or compressor unexpectedly fail and the machine be inoperable until a replacement part is installed, condition monitoring of those critical pieces on the machine can provide warning signs that something is about to go terribly wrong. Vibration measurements on rotating equipment can detect when there is imbalance or degrade on rolling bearing elements. Temperature measurements can detect when a component is getting overheated and should be cooled down. Other environmental detections such as humidity and ambient pressure can alert someone to investigate why humidity or pressure is building up on a component or in an area. These measurement points are normally taken by specific accelerometers, temperature probes, humidity and pressure sensors and then analyzed through high end instruments with special analysis software. Typically, these instruments and software are separate from the PLC controls system. This means that even when the data indicates a future potential issue, steps need to be taken separately to stop the machine from running.

Using smart condition monitoring sensors with IO-Link allows these measured variables and alarms to be available directly onto the PLC system in real time. Some condition monitoring sensors now even have microprocessors onboard that immediately analyze the measured variables. The sensor can be configured for the measurement limit thresholds of the device it’s monitoring so that the sensor can issue a warning or alarm through the IO-Link communications channel to the PLC once those thresholds have been hit. That way, when a warning condition presents itself, the PLC can react immediately to it, whether that means sending an alert on a HMI, or stopping the machine from running altogether until the alarmed component is fixed or replaced.

Having the condition monitoring sensor on IO-Link has many advantages. As an IEC61131-9 standard, IO-Link is an open standard and not proprietary to any manufacturer. The protocol itself is on the sensor/actuator level and fieldbus independent. IO-Link allows the condition monitoring sensor to connect to Ethernet/IP, Profinet & Profibus, CC-Link & CC-Link IE Field, EtherCAT and TCP/IP networks regardless of PLC. Using an IO-Link master gateway, multiple smart condition monitoring sensors and other IO-Link devices can be connected to the controls network as a single node.

The picture above shows two condition monitoring sensors connected to a single address on the fieldbus network. In this example, a single gateway allows up to eight IO-Link condition monitoring sensors to be connected.

Through IO-Link, the PLC’s standard acyclic channel can be used to setup the parameters of the measured alarm conditions to match the specific device the sensor is monitoring. The PLC’s standard cyclic communications can then be used to monitor the alarm status bits from the condition monitoring sensor.  When an alarm threshold gets hit, the alarm status bit goes high and the PLC can then react in real time to control the machine. This relieves the burden of analyzing the sensor’s condition monitoring data from the PLC as the sensor is doing the work.

 

IO-Link Boosts Plant Productivity

In my previous blog, Using Data to Drive Plant Productivity, I categorized reasons for downtime in the plant and also discussed how data from devices and sensors could be useful in boosting productivity on the plant floor. In this blog, I will focus on where this data is and how to access it. I also touched on the topic of standardizing interfaces to help boost productivity – I will discuss this topic in my future blog.

Sensor technology has made significant progress in last two decades. The traditional transistor technology that my generation learned about is long gone. Almost every sensor now has at least one microchip and possibly even MEMs chips that allow the sensor to know an abundance of data about itself and the environment it which it resides. When we use these ultra-talented sensors only for simple signal communication, to understand presence/absence of objects, or to get measurements in traditional analog values (0-20mA, 0-10V, +5/-5V and so on), we are doing disservice to these sensors as well as keeping our machines from progressing and competing at higher levels. It is almost like choking the throat of the sensor and not letting it speak up.

To elaborate on my point, let’s take following two examples: First, a pressure sensor that is communicating 4-20mA signal to indicate pressure value. Now, that sensor can not only read pressure value but, more than likely, it can also register the ambient temperatures and vibrations. Although, the sensor is capable of understanding these other parameters, there is no way for it to communicate that information to the higher level controller. Due to this lack of ambient information, we may not be able to prevent some eminent failures. This is because of the choice of communication technology we selected – i.e. analog signal communication.

For the second example, let us take a simple photoeye sensor that only communicates presence/absence through discrete input and 0/1 signal. This photoeye also understands its environment and other more critical information that is directly related to its functionality, such as information about its photoelectric lens. The sensor is capable of measuring the intensity of re-emitted light, because based on that light intensity it is determining presence or absence of objects. If the lens becomes cloudy or the alignment of the reflector changes, it directly impacts the remitted light intensity and leads to sensor failure. Due to the choice of digital communication, there is no way for the sensor to inform the higher level control of this situation and the operator only learns of it when the failure happens.

If  a data communication technology, such as IO-Link, was used in these scenarios instead of signal communication, we could unleash these sensors to provide useful information about themselves as well as about their environment. If we collect this data or set alerts in the sensor for the upper/lower limits on this type of information, the maintenance teams would know in advance about the possible failures and prevent these failures to avoid eminent downtime.

Collecting this data at appropriate frequencies could help build a more relevant database and demonstrate patterns for the next generation of machine learning and predictive maintenance initiatives. This would be data driven continuous improvement to prevent failures and boost productivity.

The information collected from sensors and devices – so called smart devices – not only helps end users of automation to boost their plant’s productivity, but also helps machine builders to better understand their own machine usage and behaviors. Increased knowledge improves the designs for the next generation of machines.

If we utilized these smart sensors and devices at our change points in the machine, it would help fully or partially automate the product change-overs. With IO-Link as a technology, these sensors can be reconfigured and re-purposed for different applications without needing different sensors or manual tunings.

IO-Link technology has a built in feature called “automatic parameterization” that helps reduce plant down-time when sensors need replaced. This feature stores IO-Link devices’ configuration on the master port as well as all the configuration is also persistent in the sensor. Replacement is as simple as connecting the new sensor of the same type, and the IO-Link master downloads all the parameters and  replacement is complete.

Let’s recap:

  1. IO-Link unleashes a sensor’s potential to provide information about its condition as well as the ambient conditions, enabling condition monitoring, predictive maintenance and machine learning.
  2. IO-Link offers remote configuration of devices, enabling quick and automated change overs on the production line for different batches, reducing change over times and boosting plant productivity.
  3. IO-Link’s automatic parameterization feature simplifies device replacement, reducing unplanned down-time.

Hope this helps boost productivity of your plant!

Building Blocks of the Smart Factory Now More Economical, Accessible

A smart factory is one of the essential components in Industry 4.0. Data visibility is a critical component to ultimately achieve real-time production visualization within a smart factory. With the advent of IIoT and big-data technologies, manufacturers are finally gaining the same real-time visibility into their enterprise performance that corporate functions like finance and sales have enjoyed for years.

The ultimate feature-rich smart factory can be defined as a flexible system that self-optimizes its performance over a network and self-adapts to learn and react to new conditions in real-time. This seems like a farfetched goal, but we already have the technology and knowhow from advances developed in different fields of computer science such as machine learning and artificial intelligence. These technologies are already successfully being used in other industries like self-driving cars or cryptocurrencies.

1
Fig: Smart factory characteristics (Source: Deloitte University Press)

Until recently, the implementation or even the idea of a smart factory was elusive due to the prohibitive costs of computing and storage. Today, advancements in the fields of machine learning and AI and easy accessibility to cloud solutions for analytics, such as IBM Watson or similar companies, has made getting started in this field relatively easy.

One of the significant contributors in smart factory data visualization has been the growing number of IO-Link sensors in the market. These sensors not only produce the standard sensor data but also provide a wealth of diagnostic data and monitoring while being sold at a similar price point as non-IO-Link sensors. The data produced can be fed into these smart factory systems for condition monitoring and preventive maintenance. As they begin to produce self-monitoring data, they become the lifeblood of the smart factory.

Components

The tools that have been used in the IT industry for decades for visualizing and monitoring server load and performance can be easily integrated into the existing plant floor to get seamless data visibility and dashboards. There are two significant components of this system: Edge gateway and Applications.

2
Fig: An IIoT system

Edge Gateway

The edge gateway is the middleware that connects the operation technology and Information technology. It can be a piece of software or hardware and software solutions that act as a universal protocol translator.

As shown in the figure, the edge gateway can be as simple as something that dumps the data in a database or connects to cloud providers for analytics or third-party solutions.

Applications

One of the most popular stacks is Influxdb to store the data, Telegraf as the collector, and Grafana as a frontend dashboard.

These tools are open source and give customers the opportunity to dive into the IIoT and get data visibility without prohibitive costs. These can be easily deployed into a small local PC in the network with minimal investment.

The applications discussed in the post:

Grafana

Telegraf

Influxdb

Node-red Tutorial

From Design and Build, to Operation and Maintenance, IO-Link Adds Flexibility

With almost twelve million installed nodes as of 2019, IO-Link is being rapidly adopted in a wide range of industries and applications. It is no wonder since it provides more flexibility in how we build and maintain our machines and delivers more data.

Design
As an IEC standard (IEC 61131-9), IO-Link provides consistency in how our devices are connected and integrated. With an already large and ever growing base of manufacturers providing IO-Link devices, we have an incredible amount of choice when it comes to what vendors we use and what devices we incorporate into our systems, all while having the confidence that all of these devices will work and communicate together. Fieldbus independent and based on a point-to-point connection using standard 3 and 4 wire sensor cables, IO-Link allows designers to replace PLC input cards in the control cabinet with machine-mounted IO-Link masters and input hubs. This technology means we are drastically less limited in how we design our machines.

Build/Commissioning
IO-Link is well known for simplifying and reducing build time of machines. Standardization of connections means that readily available double ended quick disconnect sensor cables can replace individually terminated wires, and analogue devices and devices using RS232 connections can be replaced with IO-Link devices which connect directly to a machine mounted IO-Link master or IO hub. Simplified wiring along with delivered diagnostics leads to greatly simplified network architecture and reduced build/commissioning time, as well as increased trouble shooting ability. This all leads to reduced hardware and labor cost.

When it comes to the software side of things, you might think that all of this additional functionality and flexibility increases the burden on programmers, however through the use of configuration files provided by the device manufacturers for both the IO-Link devices and the PLC, this additional functionality and data is at our fingertips with minimal time and effort. With the large adoption of IO-Link and growing manufacturer base comes great amounts of reference material, videos, example programs, and support, all of which can help to get our systems up and running quickly.

Operation
When it comes to operation IO-Link opens a world of possibilities. Bidirectional communication of not only process data but diagnostics and parameter data delivers real time visibility into the entire system during operation all the way down to the device level. Things like automated or guided changeover become possible, for example if a manufacturer produces two different parts on the same line, after the production of part A, devices can be reparameterized for production of part B with the push of a button.

Maintenance
Maintenance sees massive benefits from IO-Link thanks to reduced unplanned downtime through device diagnostics which allow for predictive maintenance practices. If a device does get damaged or fails at an inconvenient time, the issue can be found much quicker and be replaced. Once the IO-Link master recognizes that the device was replaced with the same hardware ID, it can automatically reparameterize the device.

IO-Link is already making our lives easier and providing manufacturers with more possibilities in their automated systems, and as we push into Industry 4.0 it continues to prove its value.

For more information on IO-Link and Industry 4.0 visit www.Balluff.com

 

Improve Error Proofing with IO-Link and IoT-Enabled Sensors

Though error-proofing sensors and poka yoke have been around for decades, continuing advancements related to the Industrial Internet of Things (IIoT) are making both more accessible and easier to maintain.

Balluff - The IO-Link Revolution!

Designed to eliminate product defects by preventing human errors or correcting them in real time, poka yoke has been a key to a lean manufacturing process since it was first applied to industrial applications in 1960. Today, error proofing relies far less on manual mechanisms and more on IoT-enabled error proofing sensors that connect devices and systems across the shop floor.

IoT is enabling immediate control of error-proofing devices such as sensors. This immediacy guards against error-proofing devices being bypassed, which has been a real problem for many years. Now, if a sensor needs adjustment it can be done remotely. A good example of this is with color sensors. When receiving sub-components from suppliers, colors can shift slightly. If the quality group identifies the color lot as acceptable but the sensor does not, often the color sensor is bypassed to keep production moving until someone can address it, creating a vulnerable situation. By using IoT-enabled sensors, the color sensor can be adjusted remotely at any time or from any location.

The detection of errors has been greatly improved by integrating sensors directly into the processes. This is a major trend in flexible manufacturing where poka yoke devices have to be adjusted on-the-fly based on the specific product version being manufactured. This means that buttons or potentiometers on discrete sensors are not adequate. Sensors must provide true data to the control system or offer a means to program them remotely. They must also connect into the traceability system, so they know the exact product version is being made. Connections like this are rapidly migrating to IO-Link. This technology is driving flexible manufacturing at an accelerated rate.

IO-Link enables sensors to process and produce enriched data sets. This data can then be used to optimize efficiencies in an automated process, increase productivity and minimize errors.

Additionally, the easily expandable architecture built around IO-Link allows for easy integrations of poka yoke and industrial identification devices. By keeping a few IO-Link ports open, future expansion is easy and cost effective. For poka yoke, it is important that the system can be easily expanded and that updates are cost-effective.

Using Data to Drive Plant Productivity

What is keeping us from boosting productivity in our plants to the next level? During a recent presentation on Industry 4.0 and IIoT, I was asked this question.

The single biggest thing, in my opinion, that is keeping us from boosting productivity to the next level is a lack of DATA. Specifically, data about the systems and the processes.

1

Since the beginning of time, we have been hungry for efficiency. While early man invented more efficient methods to hunt and survive, today we are looking for ways to produce more efficiently in our plants with minimum or zero waste. After exhausting all the avenues for lean operations on plant procedures and our day-to-day activities, we are now looking at how we can recover from unanticipated downtime quickly. I am sure in future we will be seeking information on how can we prevent the downtime altogether.

There are plentiful of reasons for downtime. Just a few examples:

  1. Unavailability of labor – something we might be experiencing these days, when the COVID-19 pandemic has reduced some labor forces
  2. Unavailability of raw materials
  3. Unavailability of replacement components
  4. Unavailability of assets
  5. Failures in machines/components

In this list, the first two reasons, are beyond the scope of this blog’s intentions and frankly somewhat out of controls from the production standpoint.

The next two reasons, however, are process related and the last one is purely based on the choices we made. These three reasons, to a certain extent, can be reduced or eliminated.

If the downtime is process related, we can learn from them and improve our processes with so called continuous improvement initiatives. We can only do these continuous improvements based on observable factors (a.k.a. data) and we cannot improve our processes based on speculations. Well, I shouldn’t say “cannot”, but it will be more like a fluke or luck. It is apt to say “ what can’t be measured, can’t be improved!”

A good example for elaborating my point is change-over in the plant to produce a different product. Unless there is a good process in place for ensuring all the change-over points are properly addressed and all the change parts are correctly installed and replaced, the changeover time could and will likely lead to tremendous amounts of lost productivity. Secondly, if these processes are done manually and not automated, that is also a loss of productivity or, as I like to say, an area for continuous improvement to boost productivity based on observable facts. Sometimes, we take these manual change-overs as a fact of life and incorporate that time required as a part of “planned” downtime.  Of course, if you do change-overs once a year – it may be cost effective to keep the process manual even in today’s situation. But, if your plant has multiple short batch productions per day or per week, then automating the changeovers could significant boost productivity. The cost benefit analysis should help prove if it is continuous improvement or not.

Assets are an important part of the equation for smooth operations. An example would be molds in the stamping plant or cutting-deburring tools in metal working plants. If plants have no visibility or traceability of these important assets for location, shape or form, it could lead to considerable downtime. The calibration data of these tools or number of parts produced with the tool are also important pieces of data that needs to be maintained for efficient operations. Again, this is data about the system and the integration of these traceability initiatives in the existing infrastructure.

Failures in machines or components could cause severe downtime and are often considered as unavoidable. We tackle these failures in a two-step approach. First, we hunt for the problem when it is not obvious, and two, we find the replacement part in the store room to change it out quickly. And, as process improvement, we schedule preventative maintenance to inspect, lubricate and replace parts in our regular planned downtime.

The preventative maintenance is typically scheduled based on theoretical rate of failure. This is a good measure, especially for mechanical components, but, predictive or condition-based maintenance usually yields higher returns on productivity and helps keep plants running smooth. Again, predictive maintenance relies on data about the condition of the system or components. So, where is this data and how do we get to it?

Standardization of interfaces is another important component for boosting productivity. In my next blog, I will share how IO-Link as a technology can help address all of these challenges and boost productivity to the next level.

Are machine diagnostics overburdening our PLCs?

In today’s world, we depend on the PLC to be our eyes and ears on the health of our automation machines. We depend on them to know when there has been an equipment failure or when preventative maintenance is needed. To gain this level of diagnostics, the PLC must do more work, i.e. more rungs of code are needed to monitor the diagnostics supplied to the sensors, actuators, motors, drives, etc.

In terms of handling diagnostics on a machine, I see two philosophies. First, put the bare bones minimum in the PLC. With less PLC code, the scan times are faster, and the PLC runs more efficiently. But this version comes with the high probability for longer downtime when something goes wrong due to the lack of granular diagnostics. The second option is to add lots of diagnostic features, which means a lot of code, which can lessen downtime, but may throttle throughput, since the scan time of the PLC increases.

So how can you gain a higher level of diagnostics on the machine and lessen the burden on the PLC?

While we usually can’t have our cake and eat it too, with Industry 4.0 and IIoT concepts, you can have the best of both of these scenarios. There are many viewpoints of what these terms or ideas mean, but let’s just look at what these two ideas have made available to the market to lessen the burden on our PLCs.

Data Generating Devices Using IO-Link

The technology of IO-Link has created an explosion of data generating devices. The level of diversity of devices, from I/O, analog, temperature, pressure, flow, etc., provides more visibility to a machine than anything we have seen so far. Utilizing these devices on a machine can greatly increase visibility of the processes. Many IO-Link masters communicate over an Ethernet-based protocol, so the availability of the IO-Link device data via JSON, OPC UA, MQTT, UDP, TCP/IP, etc., provides the diagnostics on the Ethernet “wire” where more than just the PLC can access it.

Linux-Based Controllers

After using IO-Link to get the diagnostics on the Ethernet “wire,” we need to use some level of controller to collect it and analyze it. It isn’t unusual to hear that a Raspberry Pi is being used in industrial automation, but Linux-based “sandbox” controllers (with higher temperature, vibration, etc., standards than a Pi) are available today. These controllers can be loaded with Codesys, Python, Node-Red, etc., to provide a programming platform to utilize the diagnostics.

Visualization of Data

With IO-Link devices providing higher level diagnostic data and the Linux-based controllers collecting and analyzing the diagnostic data, how do you visualize it?  We usually see expensive HMIs on the plant floors to display the diagnostic health of a machine, but by utilizing the Linux-based controllers, we now can show the diagnostic data through a simple display. Most often the price is just the display, because some programming platforms have some level of visualization. For example, Node-Red has a dashboard view, which can be easily displayed on a simple monitor. If data is collected in a server, other visualization software, such as Grafana, can be used.

To conclude, let’s not overburden the PLC with diagnostic; lets utilize IIoT and Industry 4.0 philosophy to gain visibility of our industrial automation machines. IO-Link devices can provide the data, Linux-based controllers can collect and analyze the data, and simple displays can be used to visualize the data. By using this concept, we can greatly increase scan times in the PLC, while gaining a higher level of visibility to our machine’s process to gain more uptime.