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.

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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.

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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

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.

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.

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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.

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.

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.

What data can IO-Link provide?

As an application engineer, one of the most frequent questions I get asked by the customers is “What is IO-Link and what data does it contain?”.

Well, IO-Link is the first worldwide accepted sensor communication protocol to be adopted as an international standard IEC61131-9. It is an open standard, and not proprietary to one manufacturer. It uses bi-directional, single line serial communications to transfer data between the machine controller and sensors/actuators. No other communication protocol is manufacturer and fieldbus independent, and yet provides this level of communication down to the sensor/actuator level. It provides the user with three different data types: process data, parameter data, and diagnostics or event data.

Process Data

Process data of an IO-Link smart device is considered the latest state of that device. Containing both input and output data, process data is cyclically exchanged between IO-Link master and IO-Link slave device (i.e. sensor or actuator). The time interval or data update rate solely depends on amount of data, 1 to 32 bytes, and speed at which an IO-Link slave device communicates. IO-Link standard (IEC61131-9) defines three different communications speeds; COM1 is set to 4.8kBaud (slowest), COM2 is set to 38.4kBaud and COM3 is set to 230.4kBaud (fastest). Depending on the device, process data may contain status of inputs or outputs of remote I/O hub, position feedback of linear transducers, pressure feedback from pressure transducers, information from am RFID (Radio Frequency Identification) reader, and so on. For more information about process data content, refresh rate, and data mapping, one should reference an IO-Link slave device datasheet or user manual.

Lastly, process data is then buffered in memory of the IO-Link master device and passed to the controller via a specific fieldbus at request packet interval. Request packet interval is set in the controller settings.

Process Data

Parameter Data

Parameter data contains information and parameters specific to the IO-Link slave device. This data is exchanged acyclically, which means that it is requested from the IO-Link master or controller and not time based. Parameters can be read from a specific device or written to. Parameter data is primarily used for device configuration, or verification. A key advantage of IO-Link is that it gives the controller the full access to IO-Link slave device parameters, down to a sensor/actuator level. This means that your controller, PLC or PC based, can change the configuration of an IO-Link’s slave device dynamically without taking the device off line, and without use of proprietary cabling or configuration software.

Typical use of parameter data is for automatic machine configuration, recipe change, process tuning, maintenance, and easy component replacement.

Parameter Data

Diagnostics or Event Data

Diagnostic data provides the controller with events that affect the operation and performance of the IO-Link smart device. Content can vary depending on the device used, and the manufacturer. IO-Link smart devices can provide crucial data such as load, temperature, stress level, overload and short circuit diagnostics, error codes, configuration or parameter issues, access issues, etc., as part of diagnostic or event data. The event code size is 2 bytes, and in hexadecimal data format. This information can then be interpreted by the controller/user using a lookup table or IODD (I/O Device Description) file. User manual will have diagnostic data table that can be used as a reference.

Diagnostic and Event Data

Conclusion

In conclusion, IO-Link enables a plug-and-play relationship between the controller and the devices on the machine. Using IO-Link data, the controller can automatically recognize and configure an IO-Link slave device that is connected to its network. Process and diagnostic data provide continuous feedback on machine status and health down to a sensor level — the lowest level of the automation pyramid.

Keep in mind that the content of process data is specific to the device and will vary from device to device, and manufacturer to manufacturer. This makes IO-Link data one of the main differentiators between smart devices and their manufacturers. Luckily, IO-Link is an open standard, and fieldbus and manufacturer independent, so you can mix and match devices best suited for your application without worrying about device compatibility, special cabling, or third-party configuration software packages.

automation pyramid

 

Not All RFID is Created Equal: Is Yours Built for an Industrial Environment?

The retail environments where products are sold look nothing like the industrial environments where they are produced (think of the difference between a new car dealership and an automotive manufacturing plant). Yet the same RFID products developed for retail stores and their supply chain operations are still marketed to manufacturers for production operations. These products may work fine in warehouses, but that does not necessarily qualify them as industrial grade.

IO-Link_RFID

So what are the differences between retail and industrial RFID?

Production environments often require a level of ruggedness, performance, and connectivity that only purpose-built industrial equipment can reliably satisfy. For example, general-purpose RFID equipment may have the physical Ethernet port needed to connect to a PC or server, but will not support EtherNet/IP, Profinet or other industrial protocols that run on PLCs and other industrial automation control equipment. Many retail grade readers need to be supported with an additional protocol conversion, which can require external hardware and slow system performance, and adds to implementation time, difficulty, and expense.

When evaluating RFID equipment, it is essential to make the distinction between what is possible for use in the environment and what is optimal and, therefore, more reliable. There are three fundamental qualities to consider that can determine if RFID systems will perform reliably in demanding production environments:

  • Will the RFID system integrate seamlessly with industrial control systems?
  • Will it provide the reliability and speed that production and their information systems tied in require?
  • Can it maintain uptime and performance long term – will it last on the production line?

RFID is often marketed as a “solution,” however in manufacturing operations, it is almost always used as a supporting technology to provide data and visibility to the MES, ERP, e-Kanban, robotics, asset tracking, material handling, quality control and other systems that run in production facilities. Failure to accurately provide data to these systems at the reliability and speed levels they require eliminates the value of using RFID.

The physical environments in industrial and supply chain settings cause RFID technology to perform differently. Tag density can be a consideration for industrial RFID users like retail, but an industrial environment has much more challenging and powerful potential interference sources, for example, the presence of metal found in most industrial products and environments.

When determining whether RFID products are suitable for a specific environment, it is important to look beyond published marketing hype and misleading specifications. Consider the design and construction of the product and how it could be affected by various work processes. Whenever possible, you should test the products where they will be used rather than in a lab or demonstration area, because the actual work location has interference and environmental conditions that may be overlooked and impossible to duplicate elsewhere.

The key attributes that differentiate industrial RFID equipment from supply chain-oriented alternatives include:

  • Native support for industrial protocols;
  • High tag read reliability and the ability to continuously operate at speeds that won’t slow production systems;
  • Durable housing with secure connectors with IP65 or better rating and relevant certifications for shock, vibration and temperature resistance;
  • The ability to support multiple RFID technologies and supporting devices as needed, including sensors, PLCs, IO-Link, and other industrial automation equipment.

Compromising on any of these criteria will likely result in unnecessary implementation time, support, and replacement costs and increase the risk for system failure.

Increase Efficiencies and Add Value with Data

Industry 4.0 and the Industrial Internet of Things (IIoT) are very popular terms these days.  But they are more than just buzzwords; incorporating these concepts into your facility adds instant value.

Industry 4.0 and IIoT provide you with much needed data. Having information easily available regarding how well your machines are performing allows for process improvements and increased efficiencies. The need for increased efficiency is driving the industry to improve manufacturing processes, reduce downtime, increase productivity and eliminate waste.  Increased efficiency is necessary to stay competitive in today’s manufacturing market.  With technology continuing to advance and be more economical, it is more feasible than ever to implement increased efficiencies in the industry.

Industry 4.0 and IIoT are the technology concepts of smart manufacturing or the smart factory.  IIoT is at the core of this as it provides access to data directly from devices on the factory floor. By implementing a controls architecture with IO-Link and predictive maintenance practices through condition monitoring parameters from the devices on the machine, Industry 4.0 and IIoT is occurring.

Condition monitoring is the process of monitoring the condition of a machine through parameters.  In other words, monitoring a parameter that gives the condition of the machine or a device on the machine such as vibration, temperature, pressure, rate, humidity etc. in order to identify a significant change in condition, which indicates the possible development of a fault.  Condition monitoring is the primary aspect of predictive maintenance.

IO-Link is a point-to-point communication for devices which allows for diagnostics information without interfering with the process data. There are hundreds of IO-Link smart devices, which provide condition monitoring parameters for the health of the device and the health of the machine.  By utilizing capabilities of IO-Link for diagnostics the ability to gather large amounts of data directly from devices on the factory floor gives you more control over the machines efficiency.  Smart factory concepts are available today with IO-Link as the backbone of the smart machine and smart factory.

Dive into big data with confidence knowing you can gather the information you need with the smart factory concepts available today.