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.

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

 

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.

IO-Link vs. Analog in Measurement Applications

IO-Link is well-suited for use in measurement applications that have traditionally used analog (0…10V or 4…20mA) signals. This is thanks in large part to the implementation of IO-Link v1.1, which provides faster data transmission and increased bandwidth compared to v1.0. Here are three areas where IO-Link v1.1 excels in comparison to analog.

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Fewer data errors, at lower cost

By nature, analog signals are susceptible to interference caused by other electronics in and around the equipment, including motors, pumps, relays, and drives. Because of this, it’s almost always necessary to use high-quality, shielded cables to transmit the signals back to the controller. Shielded cables are expensive and can be difficult to work with. And even with them in place, signal interference is a common issue that is difficult to troubleshoot and resolve.

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With IO-Link, measurements are converted into digital values at the sensor, before transmission. Compared to analog signals, these digital signals are far less susceptible to interference, even when using unshielded 4-wire cables which cost about half as much as equivalent shielded cables. The sensor and network master block (Ethernet/IP, for example) can be up to 20 meters apart. Using industry-standard connectors, the possibility for wiring errors is virtually eliminated.

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Less sensor programming required

An analog position sensor expresses a change in position by changing its analog voltage or current output. However, a change of voltage or current does not directly represent a unit of measurement. Additional programming is required to apply a scaling factor to convert the change in voltage or current to a meaningful engineering unit like millimeters or feet.

It is often necessary to configure analog sensors when they are being installed, changing the default characteristics to suit the application. This is typically performed at the sensor itself and can be fairly cumbersome. When a sensor needs to be replaced, the custom configuration needs to be repeated for the replacement unit, which can prolong expensive machine downtime.

IO-Link sensors can also be custom configured. Like analog sensors, this can be done at the sensor, but configuration and parameterization can also be performed remotely, through the network. After configuration, these custom parameters are stored in the network master block and/or offline. When an IO-Link sensor is replaced, the custom parameter data can be automatically downloaded to the replacement sensor, maximizing machine uptime.

Diagnostic data included

A major limitation of traditional analog signals is that they provide process data (position, distance, pressure, etc.) without much detail about the device, the revision, the manufacturer, or fault codes. In fact, a reading of 0 volts in a 0-10VDC interface could mean zero position, or it could mean that the sensor has ceased to function. If a sensor has in fact failed, finding the source of the problem can be difficult.

With IO-Link, diagnostic information is available that can help resolve issues quickly. As an example, the following diagnostics are available in an IO-Link magnetostrictive linear position sensor: process variable range overrun, measurement range overrun, process variable range underrun, magnet number change, temperature (min and max), and more.

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This sensor level diagnostic information is automatically transmitted and available to the network, allowing immediate identification of sensor faults without the need for time-consuming troubleshooting to identify the source of the problem.

To learn about the variety of IO-Link measurement sensors available, read the Automation Insights post about ways measurement sensors solve common application challenges. For more information about IO-Link and measurement sensors, visit www.balluff.com.

RFID in the Manufacturing Process: A Must-Have for Continuous Improvement

There is quite an abundance of continuous improvement methodologies implemented in manufacturing processes around the globe. Whether it’s Lean, Six Sigma, Kaizen, etc., there is one thing that all of these methodologies have in common, they all require actionable data in order to make an improvement.  So, the question becomes: How do I get my hands on actionable data?

All data begins its life as raw data, which has to be manipulated to produce actionable data. Fortunately, there are devices that help automate this process. Automatic data collection (ADC), which includes barcode and RFID technology, provides visibility into the process. RFID has evolved to become the more advanced method of data collection because it doesn’t require a centralized database to store the data like barcode technology. RFID stores the data directly on the product or pallet in the process, which allows for much more in-depth data collection.

rfid

RFID’s greatest impact on the process tends to be improving overall quality and efficiency. For example, Company X is creating widgets and there are thirty-five work cells required to make a widget. Between every work cell there is a quality check with a vision system that looks for imperfections created in the prior station. When a quality issue is identified, it is automatically written to the tag.  In the following work cell the RFID tag is read as soon as it enters the station. This is where the raw data becomes actionable data. As soon as a quality issue has been identified, someone or something will need to take action. At this point the data becomes actionable because it has a detailed story to tell. While the error code written to the tag might just be a “10”, the real story is: Between cells five and six the system found a widget was non-conforming. The action that can be taken now is much more focused. The process at cell five can be studied and fixed immediately, opposed to waiting until an entire batch of widgets are manufactured with a quality issue.

Ultimately, flawless execution is what brings success to organizations.  However, in order to execute with efficiency and precision the company must first have access to not only data, but actionable data. Actionable data is derived from the raw data that RFID systems automatically collect.

Learn more about RFID technology at www.balluff.com.