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How Vibration Measurement Saves Manufacturers Time and Money

Vibration is all around us. We can feel it and we can hear it. Some vibrations we find pleasant, such as music that we like to listen to, and some vibrations we find unpleasant such as scratching fingernails across the chalkboard. Humans also can predict when something is about to fail or determine when something needs our attention based on the vibrations we can feel or hear in our surroundings. An example almost anyone can relate to is when you are driving or riding in a car and the tires are out of balance or are damaged. In addition to the audible noise, you can feel the vibration through the steering wheel and the chassis of the car. Frequency and amplitude of the vibration typically increase as you speed up, and often amplify your worry as well. This can push you to find the cause of the vibration and fix it.

This same principle can be used in a manufacturing plant environment, which is what makes monitoring vibration so important. Without it, machines break down and stop, costing you time, and money. We all know that one maintenance guru that has a special gift of being able to determine what is happening with a machine based on its vibration feedback, the one who can place his hand on a machine, or hear the machine speak to him, and determine what is wrong with it.

However, using this institutional knowledge isn’t full-proof and it can introduce additional variables in the mix; sometimes resulting in wasted parts, labor, unplanned machine downtime, loss of production, etc. And as tenured staff retires and is replaced with less experienced staff, it has become even more important to remove the human element from the equation and properly capture the data to determine the root cause of mechanical issues. But how? By equipping machines with a monitoring system, the machine can then continuously monitor itself. And when the variables exceed the preset acceptable thresholds, the machine can act based on predetermined actions set by the OEM manufacturer or the maintenance team.

There are many monitoring systems on the market today that vary in complexity and cost. More complex systems include sensors, cables, data acquisition cards, computers, analysis software, data base, cloud subscription, and paid service contracts to pinpoint exact condition of the equipment or asset that is being monitored. This type of system or service is very costly, and in most cases, it is cost prohibitive to be used on non-critical equipment or assets. However, there are lower cost solutions that may not be able to pinpoint what has failed but can tell you when something wrong with the machine that needs to be examined by the maintenance technician. Such devices can be easily integrated into an existing controls architecture and can provide continuous condition monitoring of the machine or asset. Practice of continuous condition monitoring of machines can save the company valuable time and money by reducing unscheduled machine downtime, eliminating wasted parts and time for unnecessary scheduled maintenance, improving total OEE (Overall Equipment Effectiveness) of the machine, and increasing production. This all leads to increased profits.

Because there are more and more solutions available in the market today, there are few things you need to consider when choosing the right solution for your application:

  • Overall cost of implementation – hardware, software, and any installation costs?
  • Is the solution proprietary? Hardware, software, or communications?
  • Is there an annual service contract(s)? Subscriptions?
  • Does the machine/asset require periodic or continuous monitoring?
  • Quality of data? Do you need to know the exact failure point or is knowing that the machine is operating outside of its specified parameters good enough?
  • Can the system be easily expanded for the future state?
  • Are there any additional features that can aid in analyzing the condition of the machine such as pressure, temperature, humidity?

Knowing what you need and want ahead of time will help you better choose the correct solution for your application without wasting money and time on unnecessary features and functions.

 

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Getting Condition Data From The Shop Floor to Your Software

IIoT (Industrial Internet of Things)  is becoming more mainstream, leading to more vendors implementing innovative monitoring capabilities in the new generation of sensors. These sensors are now multifunctional and provide a host of additional features such as self-monitoring.

With these intelligent sensors, it is possible to set up a system that enables continuous monitoring of the machines and production line. However, the essential requirement to use the provided data for analysis and condition monitoring for preventative and predictive maintenance is to get it from the shop floor to the MES, ERP, or other analysis software suites.

There are a variety of ways this can be done. In this post we will look at a few popular ways and methods to do so.

The most popular and straightforward implementation is using a REST API(also known as RESTful API). This has been the de facto standard in e consumer space to transport data. It allows multiple data formats to be transferred, including multimedia and JSON (Javascript Object Notation)

This has certain disadvantages like actively polling for the data, making it unsuitable for a spotty network, and having high packet loss.

MQTT(Message Queuing Telemetry Transport) eliminates the above problem. It’s very low bandwidth and works excellent on unreliable networks as it works on a publish/subscribe model. This allows the receiver to passively listen for the data from the broker. The broker only notifies when there is a change and can be configured to have a Quality of Service(QoS) to resend data if one of them loses connection. This has been used in the IoT world for a long time has become a standard for data transport, so most of software suits have this feature inbuilt.

The third option is to use OPCUA, which is the standard for M2M communication. OPCUA provides additional functionality over MQTT as it was developed with machine communication in mind. Notably, inbuilt encryption allows for secure and authenticated communication.

In summary, below is a comparison of these protocols.

A more detailed explanation can be found for these standards :

REST API : https://www.redhat.com/en/topics/api/what-is-a-rest-api

MQTT : https://mqtt.org/

OPCUA : https://opcfoundation.org/about/opc-technologies/opc-ua/

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Be Driven by Data and Decrease Downtime

Being “driven by data” is simply the act of making decisions based on real data instead of guessing or basing them on theoretical outcomes. Why one should do that, especially in manufacturing operations, is obvious. How it is done is not always so clear.

Here is how you can use a sensor, indicator light, and RFID to provide feedback that drives overall quality and efficiency.

 

Machine Condition Monitoring

You’ve heard the saying, “if it ain’t broke, don’t fix it.” However, broken machines cause downtime. What if there was a way to know when a machine is getting ready to fail, and you could fix it before it caused downtime? You can do that now!

The two main types of data measured in manufacturing applications are temperature and vibration. A sudden or gradual increase in either of these is typically an indicator that something is going wrong. Just having access to that data won’t stop the machine from failing, though. Combined with an indicator light and RFID, the sensor can provide real-time feedback to the operator, and the event can be documented on the RFID tag. The machine can then be adjusted or repaired during a planned maintenance period.

Managing Quality – A machine on its way to failure can produce parts that don’t meet quality standards. Fixing the problem before it affects production prevents scrap and rework and ensures the customer is getting a product with the quality they expect.

Managing Efficiency– Unplanned downtime costs thousands of dollars per minute in some industries. The time and resources required to deal with a failed machine far exceed the cost of the entire system designed to produce an early warning, provide indication, and document the event.

Quality and efficiency are the difference makers in manufacturing. That is, whoever makes the highest quality products most efficiently usually has the most profitable and sustainable business. Again, why is obvious, but how is the challenge. Hopefully, you can use the above data to make higher quality products more efficiently.

 

More to come! Here are the data-driven topics I will cover in my next blogs:

  • Part inspection and data collection for work in process
  • Using data to manage molds, dies, and machine tools
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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.

 

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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!

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Rise of the Robots: IO-Link Maximizes Utilization, Saves Time and Money

Manufacturers around the world are buying industrial robots at an incredible pace. In the April 2020 report from Tractia & Statista, “the global market for robots is expected to grow at a compound annual growth rate (CAGR) of around 26 percent to reach just under 210 billion US dollars by 2025.” But are we gaining everything we can to capitalize on this investment when the robots are applied? Robot utilization is a key metric for realizing return-on-investment (ROI). By adding smart devices on and around the robot, we can improve efficiencies, add flexibility, and expand visibility in our robot implementations. To maximize robot utilization and secure a real ROI there are key actions to follow beyond only enabling a robot; these are: embracing the open automation standard IO-Link, implementing smart grippers, and expanding end-effector application possibilities.

In this blog, I will discuss the benefits of implementing IO-Link. Future blog posts will concentrate on the other actions.

Why care about IO-Link?

First, a quick definition. IO-Link is an open standard (IEC 61131-9) that is more than ten years old and supported by close to 300 component suppliers in manufacturing, providing more than 70 automation technologies (figure 1). It works in a point-to-point architecture utilizing a central master with sub-devices that connect directly to the master, very similar to the way USB works in the PC environment. It was designed to be easy to integrate, simple to support, and fast to implement into manufacturing processes.

Figure 1 – The IO-Link consortium has close to 300 companies providing more than 70 automation technologies.

Using standard cordsets and 24Vdc power, IO-Link has been applied as a retrofit on current machines and designed into the newest robotic work cells. Available devices include pneumatic valve manifolds, grippers, smart sensors, I/O hubs, safety I/O, vacuum generators and more. Machine builders and equipment OEMs find that IO-Link saves them dramatically on engineering, building and the commissioning of new machines. Manufacturers find value in the flexibility and diagnostic capabilities of the devices, making it easier to troubleshoot problems and recover more quickly from downtime. With the ability to pre-program device parameters, troublesome complex-device setup can be automated, reducing new machine build times and reducing part replacement times during device failure on the production line.

Capture Data & Control Automation

Figure 2 – With IIoT-ready IO-Link sensors and masters, data can be captured without impacting the automation control.

The final point of value for robotic smart manufacturing is that IO-Link is set up to support applications for the Industrial Internet of Things (IIoT). IO-Link devices are IIoT ready, enabling Industry 4.0 projects and smart factory applications. This is important as predictive maintenance and big-data applications are only possible if we have the capabilities of collecting data from devices in, around and close to the production. As we look to gain more visibility into our processes, the ability to reach deep into your production systems will provide major new insights. By integrating IIoT-ready IO-Link devices into robotic automation applications, we can capture data for future analytics projects while not interrupting the control of the automation processes (figure 2).

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IO-Link Parameterization Maximizes Functionality, Reduces Expenses

Parameters are the key to maximizing performance and stretching sensor functionality on machines through IO-Link. They are typically addressed during set up and then often underutilized because they are misunderstood. Even users familiar with IO-Link parameters often don’t know the best method for adjustment in their systems and how to benefit from using them.

Using parameters reduces setup time
During standard installation, users must acquire all manuals for each IO-Link device and then hope that all manufactures provided detailed information for parameter setting. All IO-Link device manufacturers are required to produce an IODD file, which can be accessed through the IODD Finder. This IODD file provides a list of available parameters for an IO-Link device which will save the user time by eliminating the need for manuals. Some IO-Link masters can permanently store IODD files for rapid IO-Link parameterization. This feature brings the parameters into an online webpage and gives drop down menus with all available options along with buttons for reading and writing the parameters.

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Maximize functionality of the device
Setpoints can be changed on the fly during normal operation of the machine which will allow a device to expand to the actual range and resolution of each device. Multiple pieces of information can be extracted through IO-Link parameters that are not typically available in process data. One example being an IO-Link pressure sensor with a thermistor included so that temperature can be recorded in the parameters while sending normal pressure values. This allows the user to understand the health of their devices and gather optimal information for more visibility into their processes.

Allows for backup and recovery
IO-Link parameterization allows the user to read and write ALL parameters of IO-Link Data of the device. For example, a two-set point sensor will typically have a teach button/potentiometer that technically limits adjustment for only two parameters and cannot be backed up. This method leaves devices vulnerable to extended downtime from loss of setpoints as well as adding complex teach functions that are not precise. IO-Link parameterization on the other hand pulls teach buttons/potentiometers into the digital world with precision and repeatability. Some IO-Link master blocks have a parameter server function that backs up device parameters in case a sensor needs to be replaced, ultimately providing predictive maintenance, reduced downtime, and easy recipe changes quickly throughout the process.

Using IO Link parameterization is highly important because it reduces setup time, maximizes the functionality of the IO-Link device, and allows for backup and recovery of the parameters. Implementing parameters results in being more cost effective and decreases frustration during the installation process and required maintenance. These parameter functions are just one of the many benefits of using IO Link.

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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.

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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.

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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.

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Adding a higher level of visibility to older automation machines

It’s never too late to add more visibility to an automation machine.

In the past, when it came to IO-Link opportunities, if the PLC on the machine was a SLC 500, a PLC-5, or worse yet, a controller older than I, there wasn’t much to talk about. In most of these cases, the PLC could not handle another network communication card, or the PLC memory was maxed, or it used a older network like DeviceNet, Profibus or ASi that was maxed. Or it was just so worn out that it was already being held together with hope and prayer. But, today we can utilize IIoT and Industry 4.0 concepts to add more visibility to older machines.

IIOT and Industry 4.0 have created a volume of products that can be utilized locally at a machine, rather than the typical image of Big Data. There are three main features we can utilize to add a level of visibility: Devices to generate data, low cost controllers to collect and analyze the data, and visualization of the data.

Data Generating Devices

In today’s world, we have many devices that can generate data outside of direct communication to the PLC.  For example, in an Ethernet/IP environment, we can put intelligent devices directly on the EtherNet/IP network, or we can add devices indirectly by using technologies like IO-Link, which can be more cost effective and provide the same level of data. These devices can add monitoring of temperature, flow, pressure, and positioning data that can reduce downtime and scrap. With these devices connected to an Ethernet-based protocol, data can be extracted from them without the old PLC’s involvement.  Utilizing JSON, OPC UA, MQTT, UDP and TCP/IP, the data can be made available to a secondary controller.

Linux-Based Controllers

An inexpensive Raspberry Pi could be used as the secondary controller, but Linux-based open controllers with industrial specifications for temperature, vibration, etc. are available on the market. These lower cost controllers can then be utilized to collect and analyze the data on the Ethernet protocol. With a Linux based “sandbox” system, many programming software packages could be loaded, i.e. Node-Red, Codesys, Python, etc., to create the needed logic.

Visualization of Data

Now that the data is being produced, collected and analyzed, the next step is to view the information to add the extra layer of visibility to the process of an older machine. Some of the programming software that can be loaded into the Linux-based systems, which have a form a visualization, like a dashboard (Node-Red) or an HMI feel (Codesys). This can be displayed on a low-cost monitor on the floor near the machine.

By utilizing the products used in the “big” concepts of IIOT and Industry 4.0, you can add a layer of diagnostic visualization to older machines, that allows for easier maintenance, reduced scrap, and predictive maintenance.