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.

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.

Make 2020 the Year of Smart Manufacturing

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As we near the end of 2019, it is time to start thinking of New Year’s resolutions. Mostly, these are personal — a promise to eat better, to work out, or save money. But the clean slate of a fresh year on the calendar is also a good time to reevaluate business practices and look at how we can improve on the work floor. And as we enter a new decade, one of the areas every manufacturer needs to be considering is smart manufacturing.

Smart manufacturing uses real-time data and technology to help you meet the changing demands and conditions in the factory and supply chain to meet customer needs. This accurate, yet seemingly vague, definition means that the implementation of smart manufacturing into the workplace can help you meet an array of issues that negatively impact efficiency and the bottom line.

Implementation of smart manufacturing can:

  • Reduce manufacturing costs
  • Permit higher machine availability
  • Boost overall equipment effectiveness
  • Improve asset utilization
  • Allow for traceability of products and parts
  • Enhance supply chain
  • Ease new technology integration
  • Improve product quality
  • Reduce scrap rates
  • Minimize die crashes
  • Decrease unplanned downtime

These are big claims, but all achievable with the modernization of our systems, which is long overdue for most. According to the latest polls, 4 out of 10 manufacturers have little to no visibility into the real-time status of their manufacturing processes and an even higher percentage are utilizing at least some equipment that is far past its intended lifespan.

Half of manufacturers only become aware of system issues only after a breakdown occurs. This is unacceptable in 2020. Much like we expect our personal vehicles to alert us to upcoming issues — think of your service engine light or oil-life indicator —we need insight into the operation and performance of our manufacturing equipment.

Of course, joining the next industrial revolution comes at a cost, but if we put a dollar value on downtime and evaluate the cost benefit of the expected outcomes, it is hard to argue with the figures.

While we don’t need the start of a new year to make major changes, the flipping of the calendar page can give us the push we need to evaluate where we are and where we want to be. So, what are you waiting for?

Define your vision – Determine what you want to accomplish. Be clear and concise in articulating what you want to accomplish.

Set an objective for 2020 – You don’t have to change everything at once. Growth can come slower. What can you accomplish in the coming year?

Identify tactics and projects – Break down your vision into bite-size goals and projects. Prioritize realistic goals and set deadlines.

Link to KPIs – Make sure your smart manufacturing goals tie to key performance indicators. Having measurable results demonstrates just how effective the changes are and how they are improving business overall.

Assign responsibility – Designate owners to each step of the process. Make it someone’s responsibility to implement, track and report on the efforts. If it is everyone’s job, then it is no one’s job.

Tracking and Traceability in Mobility: A Step Towards IIoT

In today’s highly competitive automotive environment, it is becoming increasingly important for companies to drive out operating costs in order to ensure their plants maintain a healthy operating profit.

Improved operational efficiency in manufacturing is a goal of numerous measures. For example, in Tier 1 automotive parts manufacturing it is common place to have equipment that is designed to run numerous assemblies through one piece of capital equipment (Flexible Manufacturing). In order to accommodate multiple assemblies, different tooling is designed to be placed in this capital equipment. This reduces required plant floor real-estate and the costs normally required for unidimensional manufacturing equipment. However, with this flexibility new risks are introduced, such as running the machine with incorrect tooling which can cause increased scrap levels, incorrect assembly of parts and/or destruction/damage of expensive tooling, expedited freight, outsourcing costs, increased manpower, sorting and rework costs, and more.

Having operators manually enter recipes or tooling change information introduces the Human Error of Probability (HEP).  “The typical failure rates in businesses using common work practices range from 10 to 30 errors per hundred opportunities. The best performance possible in well managed workplaces using normal quality management methods are failure rates of 5 to 10 in every hundred opportunities.” (Sondalini)

Knowing the frequency of product change-over rates, you can quickly calculate the costs of these potential errors. One means of addressing this issue is to create Smart Tooling whereby RFID tags are affixed on the tooling and read/write antennas are mounted on the machinery and integrated into the control architecture of the capital equipment. The door to a scalable solution has now been opened in which each tool is assigned a unique ID or “license plate” identifying that specific tooling. Through proper integration of the capital equipment, the plant can now identify what tooling is in place at which OP station and may only run if the correct tooling is confirmed in place. In addition, one can then move toward predictive maintenance by placing process data onto the tag itself such as run time, parts produced, and tooling rework data. Collection and monitoring of this data moves the plant towards IIoT and predictive maintenance capabilities to inform key personnel when tooling is near end of life or re-work requirement thus contributing to improved OEE (Overall Equipment Effectiveness) rates.

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For more information on RFID, visit www.balluff.com.

*Source: Mike Sondalini, Managing Director, Lifetime Reliability Solutions, Article: Unearth the answers and solve the causes of human error in your company by understanding the hidden truths in human error rate tables

Diversity in factory automation

This blog was originally posted on the Innovating Automation Blog.

Biodiversity is beneficial not only in biological ecosystems, but in industrial factory automation as well. Diversity helps to limit the effects of unpredictable events.

Typically, in factory automation a control unit collects data from sensors, analyzes this data and, according to its programmed instruction, triggers actuators to a defined operation. In most cases, a single-channel structure consisting of sensor, logic and output perfectly fulfills the application requirements. Yet in some cases two-channel structures are preferred to increase the reliability of the control concept.

Clamping control at machine tool spindles

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To monitor clamping positions of tools in machine tool spindles, several options are possible: Sensors with binary output (e.g. PNP normally open) or sensors with continuous output (e.g. 0..10V or IO-Link) may be installed. The clamping process in many spindles is controlled with hydraulic actuators. This means the clamping force can be controlled by using pressure sensors which control the applied hydraulic pressure in the clamping cylinder.

The combined usage of both position and pressure sensors controls the clamping status in a better manner than using only one sensor principle. Typically, there are three clamping situations: 1) unclamped 2) clamped without object 3) clamped with object. In tooling spindles, the clamped position is usually achieved by using springs which force the mechanics to hold and clamp the object when no pressure is applied. A pneumatic or hydraulic actuator allows the worker to unclamp the object by providing force to overcome the spring load. Without hydraulic or pneumatic pressure, the clamped position should be detected by the position sensor. When enough pressure is being built up, after a short delay, the unclamped position should be achieved. Otherwise something must be wrong.

The advantage of diversity

By using two different sensor principles (in this case pressure sensing and position sensing) the risk of so-called common cause failures is reduced. The probability of concurrent effects of environmental impact on the different sensors is diminished, thereby increasing the detection rate of failures. The machine control can immediately react if the signals of pressure and position sensors do not match, simplifying monitoring of the clamping process.

Improve Your Feeder Bowl System (and Other Standard Equipment) with IO-Link

One of the most common devices used in manufacturing is the tried and true feeder bowl system. Used for decades, feeder bowls take bulk parts, orients them correctly and then feeds them to the next operation, usually a pick-and-place robot. It can be an effective device, but far too often, the feeder bowl can be a source of cycle-time slowdowns. Alerts are commonly used to signal when a feed problem is occurring but lack the exact cause of the slow down.

feeder bowl

A feed system’s feed rate can be reduced my many factors. Some of these include:

  • Operators slow to add parts to the bowl or hopper
  • Hopper slow to feed the bowl
  • Speeds set incorrectly on hopper, bowl or feed track
  • Part tolerance drift or feeder tooling out of adjustment

With today’s Smart IO-Link sensors incorporating counting and timing functions, most of the slow-down factors can be easily seen through an IIoT connection. Sensors can now time how long critical functions take. As the times drift from ideal, this information can be collected and communicated upstream.

A common example of a feed system slow-down is a slow hopper feed to the bowl. When using Smart IO-Link sensors, operators can see specifically that the hopper feed time is too long. The sensor indicates a problem with the hopper but not the bowl or feed tracks. Without IO-Link, operators would simply be told the overall feed system is slow and not see the real problem. This example is also true for the hopper in-feed (potential operator problem), feed track speed and overall performance. All critical operations are now visible and known to all.

For examples of Balluff’s smart IO-Link sensors, check out our ADCAP sensor.

Smart choices deliver leaner processes in Packaging, Food and Beverage industry

In all industries, there is a need for more flexible and individualized production as well as increased transparency and documentable processes. Overall equipment efficiency, zero downtime and the demand for shorter production runs have created the need for smart machines and ultimately the smart factory. Now more than ever, this is important in the Packaging, Food and Beverage (PFB) industry to ensure that the products and processes are clean, safe and efficient.

Take a look at how the Smart Factory can be implemented in Packaging, Food, and Beverage industries.

Updating Controls Architecture

  • Eliminates analog wiring and reduces costs by 15% to 20%
  • Simplifies troubleshooting
  • Enables visibility down to the sensor/device
  • Simplifies retrofits
  • Reduces terminations
  • Eliminates manual configuration of devices and sensors

Automating Guided Format Change and Change Parts

  • Eliminates changeover errors
  • Reduces planned downtime to perform change over
  • Reduces product waste from start-up after a change over
  • Consistent positioning every time
  • Ensures proper change parts are swapped out

Predictive Maintenance through IO-Link

  • Enhances diagnostics
  • Reduces unplanned downtime
  • Provides condition monitoring
  • Provides more accurate data
  • Reduces equipment slows and stops
  • Reduces product waste

Traceability

  • Delivers accurate data and reduced errors
  • Tracks raw materials and finished goods
  • Date and lot code accuracy for potential product recall
  • Allows robust tags to be embedded in totes, pallets, containers, and fixtures
  • Increases security with access control

Why is all of this important?

Converting a manufacturing process to a smart process will improve many aspects and cure pains that may have been encountered in the past. In the PFB industry, downtime can be very costly due to raw material having a short expiration date before it must be discarded. Therefore, overall equipment efficiency (OEE) is an integral part of any process within PFB. Simply put, OEE is the percentage of manufacturing time that is truly productive. Implementing improved controls architecture, automating change over processes, using networking devices that feature predictive maintenance, and incorporating RFID technology for traceability greatly improve OEE and reduce time spent troubleshooting to find a solution to a reoccurring problem.

Through IO-Link technology and smart devices connected to IO-Link, time spent searching for the root of a problem is greatly reduced thanks to continuous diagnostics and predictive maintenance. IO-Link systems alert operators to sensor malfunctions and when preventative maintenance is required.

Unlike preventative maintenance, which only captures 18% of machine failures and is based on a schedule, predictive maintenance relies on data to provide operators and controls personnel critical information on times when they may need to do maintenance in the future. This results in planned downtime which can be strategically scheduled around production runs, as opposed to unplanned downtime that comes with no warning and could disrupt a production run.

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Reducing the time it takes to change over a machine to a different packaging size allows the process to finish the batch quicker than if a manual change over was used, which in turn means a shorter production blog 2.20 2run for that line. Automated change over allows the process to be exact every time and eliminates the risk of operator error due to more accurate positioning.

 

 

blog 2.20 3Traceability using RFID can be a very important part of the smart PFB factory. Utilizing RFID throughout the process —tracking of raw materials, finished goods, and totes leaving the facility — can greatly increase the efficiency and throughput of the process. RFID can even be applied to change part detection to identify if the correct equipment is being swapped in or out during change over.

Adding smart solutions to a PFB production line improves efficiency, increases output, minimizes downtime and saves money.

For more information on the Smart Factory check out this blog post: The Need for Data and System Interoperability in Smart Manufacturing For a deeper dive into format change check out this blog post: Flexibility Through Automated Format Changes on Packaging Machines

 

 

IO-Link — Enables Industry 4.0 and Reduces Costs

Where does IO-Link fit on the road to Industry 4.0 and smart manufacturing?

IO-Link is a major enabling force for Industry 4.0 & smart manufacturing. Motivations for flexible manufacturing, efficient production and visibility require that we have more diagnostics and data available for analysis and monitoring. Lot-size-one flexible manufacturing requires that sensors and field devices be able to adapt to a rapidly changing set of requirements. With the parameterization feature of IO-Link slave devices, we can now send new parameters for production to the sensor on a part by part basis if required. For example, you could change a color sensor’s settings from red to green to orange to grey and back to red if necessary, allowing for significantly more flexible production. With efficient production, IO-Link slaves provide detailed diagnostics and condition monitoring information, allowing for trending of data, prediction of failure modes, and, thus, eliminating most downtime as we can act on the prediction data in a controlled & planned way. Trending of information like the current output of a power supply can give us new insights into changes in the machine over time or provide visibility into why a failure occurred.  For example, if a power supply reported a two amp jump in output three weeks ago, we can now ask, “what changed in our equipment 3 weeks ago that caused that?” This level of visibility can help management make better decisions about equipment health and production requirements.

Has IO-Link been widely accepted? Is anything still holding back its implementation?

In the last year IO-Link has become widely accepted. Major automation players like Balluff, Rockwell Automation, Festo, Siemens, SMC, Turck, Banner, Schmalz, Beckhoff, IFM and more than 100 other companies are engaged, promoting and, most importantly, building an installed base of functional IO-Link applications. We have seen installations in almost every industry segment: automotive OEMs, automotive tier suppliers, food & dairy machinery, primary packaging machinery, secondary packaging machinery, conveying systems, automated welding equipment, robot dress packs, on end-effectors of robots, automated assembly stations, palletized assembly lines, steel mills, wood mills, tire presses and more. The biggest roadblock to IO-Link becoming even further expanded in the market is typically a lack of skillset to support automation in the factory or a wariness of IO-Link as “another industrial network.”

What is the latest trend in IO-Link technology?

One of the biggest trends we are seeing with IO-Link technology is the reduction of analog on the machine.  With analog signals there are many “gotchas” that can ruin a good sensor application: electrical noise on the line, poor grounding design, more wiring, expensive analog input cards, and extra integration work. Analog signals cause a lot of extra math that we don’t need or want to do, for example: a linear position measurement sensor is 205mm long with a 4-20mA output tied into a 16bit input card. How many bits are there per mm?  A controls engineer needs to do a lot of mental gymnastics to integrate this into their machine. With IO-Link and a standard sensor cable, the wiring and grounding issues are typically eliminated and since IO-Link sensors report their measurements in the engineering units of the device, the mathematic gymnastics are also eliminated.  In our example, the 205mm long linear position sensor reports 205mm in the PLC, simple, faster to integrate and usually a much better overall application cost.

Why IO-Link is the Best Suited Technology for Smart Manufacturing

While fieldbus solutions utilize sensors and devices with networking ability, they come with limitations. IO-Link provides one standard device level communication that is smart in nature and network independent. That enables interoperability throughout the controls pyramid, making it the most suitable choice for smart manufacturing.

IO-Link offers a cost effective solution to the problems. Here is how:

  • IO-Link uses data communication rather than signal communication. That means the communication is digital with 24V signal with high resistance to the electrical noise signals.
  • IO-Link offers three different communication modes: Process communication, Diagnostic communication (also known as configuration or parameter communication), and Events.
    • Process communication offers the measurement data for which the device or sensor is primarily selected. This communication is cyclical and continuous in nature similar to discrete I/O or analog communication.
    • Diagnostic communication is a messaging (acyclic) communication that is used to set up configuration parameters, receive error codes and diagnostic messages.
    • Event communication is also acyclic in nature and is how the device informs the controller about some significant event that the sensor or that device experienced.
  • IO-Link is point-to-point communication, so the devices communicate to the IO-Link master module, which acts as a gateway to the fieldbus or network systems or even standard TCP/IP communication system. So, depending on the field-bus/network used, the IO-Link master may change but all the IO-Link devices enjoy the freedom from the choice of network. Power is part of the IO-Link communication, so it does not require separate power port/drop on the devices.
  • Every open IO-Link master port offers expansion possibilities for future integration. For example, you could host an IO-Link RFID device or a barcode reader for machine access control as a part of a traceability improvement program.

For more information, visit www.balluff.com/io-link.