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Transforming Big Data Into Practical Insights

While RFID technology has been around for almost 70 years, the last decade has seen widespread acceptance, particularly in automated manufacturing. It is now deployed for various common applications, including automatic data transfer in machining operations, quality control in production, logistics traceability, and inventory control.

RFID has contributed significantly to the evolution of data collection and handling. With this evolution has come vast amounts of data which can be crucial for process improvement, quality assurance, and regulatory compliance. Nevertheless, many organizations grapple with the challenge of transforming this abundance of data into actional insights.

Key industry terms such as Industry 4.0 and the Industrial Internet of Things (IIOT) were once perceived as distant concepts crafted by marketing teams, seemingly disconnected from the practicalities of the plant floor. However, these buzzwords emerged as a response to manufacturing organizations worldwide recognizing the imperative for enhanced visibility into their operations. While automation hardware and supporting infrastructure have swiftly progressed in response to this demand, there remains a significant need for software that can effectively transform raw data into actionable data. This software must offer interactive feedback through reporting, dashboards, and real-time indicators.

Meeting the demand will bring vendors from various industries and start-ups, with a few established players in automation rising to the occasion. Competition serves as a motivator, but the crucial factor in bridging the software gap on the plant floor lies in partnering with a vendor attuned to the specific needs of that environment. The question then becomes: How do you discern the contenders from the pretenders? The following is a checklist to help.

Does the potential vendor have:

    • A solid understanding that downtime and scrap must be reduced or eliminated?
    • Core expertise in automation tailored for the plant floor?
    • Smart hardware devices such as RFID and condition monitoring sensors?
    • A comprehensive system solution capable of collecting, analyzing, and transporting data from the device to the cloud?
    • A user-friendly interface that allows interaction with mobile devices like tablets and phones?
    • The ability to generate customized reports tailored to your organization’s requirements?
    • A stellar industry reputation for quality and reliability?
    • A support chain covering pre-sales, installation, and post-sales support?
    • Demonstrated instances of successful system deployments?
    • A willingness to develop or adapt existing devices to address your specific needs?

If you can tick all of these, it’s a safe bet you are in good hands. Otherwise, you’re taking a chance.

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Turning Big Data into Actionable Data

While RFID technology has been available for almost seventy years, the last decade has seen widespread acceptance, specifically in automated manufacturing. Deployed for common applications like automatic data transfer in machining operations, quality control in production, logistics traceability and inventory control, RFID has played a major role in the evolution of data collection and handling. With this evolution has come massive amounts of data that can ultimately hold the key to process improvement, quality assurance and regulatory compliance. However, the challenge many organizations face today is how to turn all that data into actionable data.

Prominent industry buzzwords like Industry 4.0 and the Industrial Internet of Things (IIOT) once seemed like distant concepts conjured up by a marketing team far away from the actual plant floor, but those buzzwords are the result of manufacturing organizations around the globe identifying the need for better visibility into their operations. Automation hardware and the infrastructure that supports it has advanced rapidly due to this request, but software that turns raw data into actionable data is still very much in demand. This software needs to provide interactive feedback in the form of reporting, dashboards, and real time indicators.

The response to the demand will bring vendors from other industries and start-ups, while a handful of familiar players in automation will step up to the challenge. Competition keeps us all on our toes, but the key to filling the software gap in the plant is partnering with a vendor who understands the needs on the plant floor. So, how do you separate the pretenders from the contenders? I compiled a check list to help.

Does the prospective vendor have:

  • A firm understanding that down time and scrap need to be reduced or eliminated?
  • A core competency in automation for the plant floor?
  • Smart hardware devices like RFID and condition monitoring sensors?
  • A system solution that can collect, analyze, and transport data from the device to the cloud?
  • A user-friendly interface that allows interaction with mobile devices like tablets and phones?
  • The capability to provide customized reports to meet the needs of your organization?
  • A great industry reputation for quality and dependability?
  • A chain of support for pre-sales, installation, and post-sales support?
  • Examples of successful system deployments?
  • The willingness to develop or modify current devices to address your specific needs?

If you can check the box for all of these, it is a safe bet you are in good hands. Otherwise, you’re rolling the dice.

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How Condition Monitoring has Evolved and Its Role in IIoT

In recent years, as IIoT and Industry 4.0 have become part of our everyday vocabulary, we’ve also started hearing more about condition monitoring, predictive maintenance (PdM) and predictive analytics. Sometimes, we use these terms interchangeably as well. Strictly speaking, condition monitoring is a root that enables both predictive maintenance and predictive analytics. In today’s blog we will brush up a little on condition monitoring and explore its lineage.

Equipment failures have been around since the beginning of time. Over the years, through observation (collecting data) and brute-force methods, we learned that from time-to-time every piece of equipment needs some TLC. Out of this understanding, maintenance departments came to existence, and there we started having experts that could tell based on touch, smell and noise what is failing or what has gone wrong.

Figure 1: Automation Pyramid

Then we started automating the maintenance function either as a preventative measure (scheduled maintenance) or through some automated pieces of equipment that would collect data and provide alerts about a failure. We proudly call these SCADA systems – Supervisory Control and Data Acquisition. Of course, these systems did not necessarily prevent failures, but help curtail them.  If we look at the automation pyramid, the smart system at the bottom is a PLC and all the sensors are what we call “dumb sensors”. So, that means, whatever information the SCADA system gets would be filtered by the PLC. PLCs were/have been/ and are always focused on the process at hand; they are not data acquisition equipment. So, the data we receive in the SCADA system is only as good as the PLC can provide. That means the information is primarily about processes. So, the only alerts maintenance receives is when the equipment fails, and the process comes to a halt.

With the maintenance experts who could sense impending failures becoming mythological heroes, and  SCADA systems that cannot really tell us the story about the health of the machines, once again, we are looking at condition monitoring with a fresh set of eyes.

Sensors are at the grass root level in the automation pyramid, and until the arrival of IO-Link technology, these sensors were solely focused on their purpose of existence; object detection, or measurement of some kind. The only information one could gather from these sensors was ON/OFF or a signal of 4-20mA, 0-10V, and so on. Now, things are different, these sensors are now becoming pretty intelligent and they, like nosy neighbors, can collect more information about their own health and the environment. These intelligent sensors can utilize IO-Link as a communication to transfer all this information via a gateway module (generally known as IO-Link master) to whomever wants to listen.

Figure 2: IO-Link enabled Balluff photo-eye

The new generation of SCADA systems can now collect information not only from PLCs about the process health, but also from individual devices. For example, a photo-eye can measure the intensity of the reflected light and provide an alert if the intensity drops beyond a certain level, indicating a symptom of pending failure. Or a power supply inside the cabinet providing an alert to the supervisory control about adverse conditions due to increase temperature or humidity in the cabinet. These types of alerts about the symptoms help maintenance prevent unplanned downtime on the plant floor and make factories run more efficiently with reduced scrap, reduced down-time and reduced headaches.

Figure 3: The Next Generation Condition Monitoring

There are many different condition monitoring architectures that can be employed, and we will cover that in my next blog.

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Implement a Smart Factory Using Available Technologies

What is a Smart Factory?

The term smart factory describes a highly digitalized and connected system where machines and equipment using sensor technology improves processes through monitoring, automation, and optimization. The wealth of data enables predictive maintenance and an increase in productivity through planning and decreased downtime.

The smart factory’s core building blocks are various intelligent sensors that provide a critical measure for the machine’s health, such as temperature, vibration, and pressure. This data combined with production, information, and communication technologies forms the backbone of what many refer to as the next industrial revolution, i.e., Industry 4.0.

The technologies that make the Industrial Internet of things or Industry 4.0 possible have always been available for the information technology domain. The same technology and software can be used to implement the next generation of industries.

How would I go about implanting these technologies?

The prerequisite to implementing any smart factory is using a sensor(s) with the ability to provide sensing information and to monitor its health. For example, an optical laser sensor can measure distance and monitor the beam’s strength reflected, alerting that the glass window might be foggy or dirty. These sensors are readily available in the market as most IO-Link sensors come with the diagnostics inbuilt. However, it varies from vendor to vendor.

The second step is getting the data from the operational technology side to the information technology level. The industrial side of things uses PLCs for control, which should be left alone as the single source of control for security reasons and efficiency. However, most IO-Link-enabled network blocks can tap into this data in read-only mode using JSON (JavaScript Object Notation) or a REST API.  With the IO-Link consortium officially formalizing the REST API, we will see more and more vendors adopting it as a feature for their network blocks

The final step is using this data to visualize and optimize the process. There are various SCADA and MES software systems that make it possible to do this without much development. But for maximum customizability, it’s recommended to build a stack that fits your needs and provides the option to scale. There are very mature open-source software options and applications that have been in used in the IT world for decades now and transfer seamlessly to the industrial side.

A data visualization of the current and amperage of an IO-Link device

The stack I have personally used and seen other companies implement is Grafana as a dashboarding software, InfluxdB as a time-series database, telegraf as a collector, and Mosquitto as MQTT broker.

The possibilities for expansion are limitless, leaving the option to add another service like TensorFlow for some analytics.

All of these are deployed as container services using Docker, another open-source project. This helps for easy deployment and maintenance.

A demonstration of this stack can be seen at the following link

https://balluff.app

Username and password are both “balluff” (all lowercase).

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Machine Vision: A Twenty-first Century Automation Solution

Lasers, scanners, fingerprint readers, and face recognition is not just science fiction anymore.  I love seeing technology only previously imagined become reality through necessity and advances in technology.  We, as a world economy, need to be able to verify who we are and ensure transitions are safe, and material and goods are tracked accurately.  With this need came the evolution of laser barcode readers, fingerprint identification devices, and face ID on your phone.  Similar needs have pushed archaic devices to be replaced within factory automation for data collection.

When I began my career in control engineering the 1990s high tech tools were limited to PLCs, frequency drives, and HMIs. The quality inspection data these devices relied on was collected mostly through limit switches and proximity sensors.  Machine vision was still in it’s expensive and “cute” stage.  With the need for more information, seriously accurate measurement, machining specs, and speed; machine vision has evolved, just like our personal technology has, to fill the needs of the modern time.

Machine vision has worked its way into the automation world as a need to have rather than a nice to have.  With the ability to stack several tools and validations on top of each other, within a fraction of a second scan we now have the data our era needs to stay competitive.  Imagine an application requiring you to detect several material traits, measure the part, read a barcode for tracking, and validate  a properly printed logo screened onto the finished product.  Sure, you could use several individual laser sensors, barcode readers and possibly even a vision sensor all working in concert to achieve your goal.  Or you could use a machine vision system to do all the above easily with room to grow.

I say all of this because there is still resistance in the market to move to machine vision due to historical high costs and complexity.  Machine Vision is here to stay and ready for your applications today.  Think of it this way.  How capable would you think a business is they took out a carbon copy credit card machine to run a payment for you?  Well, think of this before you start trying to solve applications with several sensors.  Take advantage of the technology at your fingertips; don’t hold on to nostalgia.

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Industry 4.0: What It Is and How It Improves Manufacturing

Industry 4.0 is a common buzzword that is thrown around along with IIoT and Process visualization but what does that mean and how is it integrated into a manufacturing process? Industry 4.0 refers to the fourth industrial revolution. The first dealing with mechanization and the use of steam and water power, the second referring to mass production using assembly lines and electrical power, and the third referring to automated production and the use of computers and robots. Industry 4.0 takes us a step beyond that to smart factories that include automation and machine learning. Again, buzzwords that can be hard to visualize.

A commonplace example of this would be self-driving cars. They are autonomous because they don’t need a person operating them and they take, in real time, information about their surroundings and use that to determine a course of action. But how can this type of technology affect a manufacturing process?

Industry 4.0 requires data to be analyzed. This is where IO-Link comes into play. With IO-Link, you are able to get information from a sensor more than than just an output signal when it detects a part. A photoelectric sensor is a good example of this. The basic way a photoelectric sensor works an output is given depending on the amount of light being received. If the sensor happens to be in a dirty/dusty environment, there could be dirt collecting on the lens or floating in the air which effects the amount of light being received. An IO-Link (smart) sensor can not only fire an output when detection occurs but can give information about the real time gain of the sensor (how much light is being received). If the gain drops below a certain amount because of dirt on the lens or in the air, it can send another signal to the controller indicating the change in gain.

Now that we have more data, what are we going to do with it?

We now have all of this data coming from different parts of the machine, but where does it go and what do we do with it? This is where process visualization comes into play. We are able to take real time data from a machine and upload it to a database or system that we can monitor outside of the plant floor. We can know if a machine is running properly without having to physically see the machine. The information can also give us indications about when something might fail so preventative maintenance can take place and reduce downtime.

As more manufacturing processes are becoming automated, machines are becoming more and more complex. A machine might be needed to run 6-7 different lines rather than just 1 or 2 which can involve things like tool change or settings changes. Then, more checks need to be in place, so the right process is running for the right part. Industry 4.0 is how we are able to gather all this information and use it to increase efficiency and productivity.

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Adding Smart Condition Monitoring Sensors to Your PLC Control Systems Delivers Data in Real Time

Condition monitoring of critical components on machines delivers enormous benefits to productivity in a plant.  Rather than have a motor, pump, or compressor unexpectedly fail and the machine be inoperable until a replacement part is installed, condition monitoring of those critical pieces on the machine can provide warning signs that something is about to go terribly wrong. Vibration measurements on rotating equipment can detect when there is imbalance or degrade on rolling bearing elements. Temperature measurements can detect when a component is getting overheated and should be cooled down. Other environmental detections such as humidity and ambient pressure can alert someone to investigate why humidity or pressure is building up on a component or in an area. These measurement points are normally taken by specific accelerometers, temperature probes, humidity and pressure sensors and then analyzed through high end instruments with special analysis software. Typically, these instruments and software are separate from the PLC controls system. This means that even when the data indicates a future potential issue, steps need to be taken separately to stop the machine from running.

Using smart condition monitoring sensors with IO-Link allows these measured variables and alarms to be available directly onto the PLC system in real time. Some condition monitoring sensors now even have microprocessors onboard that immediately analyze the measured variables. The sensor can be configured for the measurement limit thresholds of the device it’s monitoring so that the sensor can issue a warning or alarm through the IO-Link communications channel to the PLC once those thresholds have been hit. That way, when a warning condition presents itself, the PLC can react immediately to it, whether that means sending an alert on a HMI, or stopping the machine from running altogether until the alarmed component is fixed or replaced.

Having the condition monitoring sensor on IO-Link has many advantages. As an IEC61131-9 standard, IO-Link is an open standard and not proprietary to any manufacturer. The protocol itself is on the sensor/actuator level and fieldbus independent. IO-Link allows the condition monitoring sensor to connect to Ethernet/IP, Profinet & Profibus, CC-Link & CC-Link IE Field, EtherCAT and TCP/IP networks regardless of PLC. Using an IO-Link master gateway, multiple smart condition monitoring sensors and other IO-Link devices can be connected to the controls network as a single node.

The picture above shows two condition monitoring sensors connected to a single address on the fieldbus network. In this example, a single gateway allows up to eight IO-Link condition monitoring sensors to be connected.

Through IO-Link, the PLC’s standard acyclic channel can be used to setup the parameters of the measured alarm conditions to match the specific device the sensor is monitoring. The PLC’s standard cyclic communications can then be used to monitor the alarm status bits from the condition monitoring sensor.  When an alarm threshold gets hit, the alarm status bit goes high and the PLC can then react in real time to control the machine. This relieves the burden of analyzing the sensor’s condition monitoring data from the PLC as the sensor is doing the work.

 

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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

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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

 

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Are machine diagnostics overburdening our PLCs?

In today’s world, we depend on the PLC to be our eyes and ears on the health of our automation machines. We depend on them to know when there has been an equipment failure or when preventative maintenance is needed. To gain this level of diagnostics, the PLC must do more work, i.e. more rungs of code are needed to monitor the diagnostics supplied to the sensors, actuators, motors, drives, etc.

In terms of handling diagnostics on a machine, I see two philosophies. First, put the bare bones minimum in the PLC. With less PLC code, the scan times are faster, and the PLC runs more efficiently. But this version comes with the high probability for longer downtime when something goes wrong due to the lack of granular diagnostics. The second option is to add lots of diagnostic features, which means a lot of code, which can lessen downtime, but may throttle throughput, since the scan time of the PLC increases.

So how can you gain a higher level of diagnostics on the machine and lessen the burden on the PLC?

While we usually can’t have our cake and eat it too, with Industry 4.0 and IIoT concepts, you can have the best of both of these scenarios. There are many viewpoints of what these terms or ideas mean, but let’s just look at what these two ideas have made available to the market to lessen the burden on our PLCs.

Data Generating Devices Using IO-Link

The technology of IO-Link has created an explosion of data generating devices. The level of diversity of devices, from I/O, analog, temperature, pressure, flow, etc., provides more visibility to a machine than anything we have seen so far. Utilizing these devices on a machine can greatly increase visibility of the processes. Many IO-Link masters communicate over an Ethernet-based protocol, so the availability of the IO-Link device data via JSON, OPC UA, MQTT, UDP, TCP/IP, etc., provides the diagnostics on the Ethernet “wire” where more than just the PLC can access it.

Linux-Based Controllers

After using IO-Link to get the diagnostics on the Ethernet “wire,” we need to use some level of controller to collect it and analyze it. It isn’t unusual to hear that a Raspberry Pi is being used in industrial automation, but Linux-based “sandbox” controllers (with higher temperature, vibration, etc., standards than a Pi) are available today. These controllers can be loaded with Codesys, Python, Node-Red, etc., to provide a programming platform to utilize the diagnostics.

Visualization of Data

With IO-Link devices providing higher level diagnostic data and the Linux-based controllers collecting and analyzing the diagnostic data, how do you visualize it?  We usually see expensive HMIs on the plant floors to display the diagnostic health of a machine, but by utilizing the Linux-based controllers, we now can show the diagnostic data through a simple display. Most often the price is just the display, because some programming platforms have some level of visualization. For example, Node-Red has a dashboard view, which can be easily displayed on a simple monitor. If data is collected in a server, other visualization software, such as Grafana, can be used.

To conclude, let’s not overburden the PLC with diagnostic; lets utilize IIoT and Industry 4.0 philosophy to gain visibility of our industrial automation machines. IO-Link devices can provide the data, Linux-based controllers can collect and analyze the data, and simple displays can be used to visualize the data. By using this concept, we can greatly increase scan times in the PLC, while gaining a higher level of visibility to our machine’s process to gain more uptime.