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.

1

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.

2

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.

3

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.

4

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.