Smart Factory Archives - AUTOMATION INSIGHTS

Improve OEE, Save Costs with Condition Monitoring Data

When it comes to IIOT (Industrial Internet of Things) and the fourth industrial revolution, data has become exponentially more important to the way we automate machines and processes within a production plant. There are many different types of data, with the most common being process data. Depending on the device or sensor, process data may be as simple as the status of discrete inputs or outputs but can be as complex as the data coming from radio frequency identification (RFID) data carriers (tags). Nevertheless, process data has been there since the beginning of the third industrial revolution and the beginning of the use of programmable logic controllers for machine or process control.

With new advances in technology, sensors used for machine control are becoming smarter, smaller, more capable, and more affordable. This enables manufacturers of those devices to include additional data essential for IIOT and Industry 4.0 applications. The latest type of data manufacturers are outputting from their devices is known as condition monitoring data.

Today, smart devices can replace an entire system by having all of the hardware necessary to collect and process data, thus outputting relative information directly to the PLC or machine controller needed to monitor the condition of assets without the use of specialized hardware and software, and eliminating the need for costly service contracts and being tied to one specific vendor.

A photo-electric laser distance sensor with condition monitoring has the capability to provide more than distance measurements, including vibration detection. Vibration can be associated with loose mechanical mounting of the sensor or possible mechanical issues with the machine that the sensor is mounted. That same laser distance sensor can also provide you with inclination angle measurement to help with the installation of the sensor or help detect when there’s a problem, such as when someone or something bumps the sensor out of alignment. What about ambient data, such as humidity? This could help detect or monitor for moisture ingress. Ambient pressure? It can be used to monitor the performance of fans or the condition of the filter elements on electrical enclosures.

Having access to condition monitoring data can help OEMs improve sensing capabilities of their machines, differentiating themselves from their competition. It can also help end users by providing them with real time monitoring of their assets; improving overall equipment efficiency and better predicting and, thereby, eliminating unscheduled and costly machine downtime. These are just a few examples of the possibilities, and as market needs change, manufacturers of these devices can adapt to the market needs with new and improved functions, all thanks to smart device architecture.

Integrating smart devices to your control architecture

The most robust, cost effective, and reliable way of collecting this data is via the IO-Link communication protocol; the first internationally accepted open, vendor neutral, industrial bi-directional communications protocol that complies with IEC61131-9 standards. From there, this information can be directly passed to your machine controller, such as PLC, via fieldbus communication protocols, such as EtherNET/Ip, ProfiNET or EtherCAT, and to your SCADA / GUI applications via OPC/UA or JSON. There are also instances where wireless communications are used for special applications where devices are placed in hard to reach places using Bluetooth or WLAN.

In the fast paced ever changing world of industrial automation, condition monitoring data collection is increasingly more important. This data can be used in predictive maintenance measures to prevent costly and unscheduled downtime by monitoring vibration, inclination, and ambient data to help you stay ahead of the game.

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.

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).

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.

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.

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.

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:

RFID: Using Actionable Data to Make Critical Decisions

While RFID technology has been in use since the 1950s, wide-spread implementation has come in waves over the years. Beginning with military applications where it was used to identify friend or foe aircraft, to inventory control in the retail industry, and now to the manufacturing space where it is being used to manage work in process, track assets, control inventory, and aid with automatic replenishment.

The bottom line is RFID is critical in the manufacturing process. Why? Because, fundamentally, it provides actionable data that is used to make critical decisions. If your organization has not yet subscribed to RFID technology then it is getting ready to. This doesn’t mean just in the shipping and receiving area. Wide-spread adoption is happening on the production line, in the tool room, on dies, molds, machine tools, on AGV’s, on pallets, and so much more.

Not an RFID expert? It’s ok. Start with a quick overview.

Learn about the fundamentals of a passive RFID system here.

In the past, controls engineers, quality assurance managers, and maintenance supervisors were early adopters because RFID played a critical role in giving them the data they needed. Thanks to global manufacturing initiatives like Smart Factory, Industry 4.0, the Industrial Internet of things (IIOT) and a plethora of other manufacturing buzz words, CEOs, CFOs, and COOs are driving RFID concepts today. So, while the “hands-on” members of the plant started the revolution, the guys in the corner offices quickly recognized the power of RFID and accelerated the adoption of the technology.

While there is a frenzy in the market, it is important to keep a few things in mind when exploring how RFID can benefit your organization:

Choose your RFID partner based on their core competency in addressing manufacturing applications
Make sure they have decades of experience manufacturing and implementing RFID
Have them clearly explain their “chain of support” from local resources to experts at the HQ.
Find a partner who can clearly define the benefits of RFID in your specific process (ROI)
Partner with a company that innovates the way their customers automate

How IO-Link is Revolutionizing Overall Equipment Efficiency

Zero downtime. This is the mantra of the food and beverage manufacturer today. The need to operate machinery at its fullest potential and then increase the machines’ capability is where the demands of food and beverage manufacturers is at today. This demand is being driven by smaller purchase orders and production runs due to e-commerce ordering, package size variations and the need for manufacturers to be more competitive by being flexible.

Using the latest technology, like IO-Link, allows manufacturers to meet those demands and improve their Overall Equipment Efficiency (OEE) or the percentage of manufacturing time that is truly productive. OEE has three components:

Availability Loss
1. Unplanned Stops/Downtime – Machine Failure
2. Planned Downtime – Set up and AdjustmentsS
Performance Loss
1. Small Stops – Idling and Minor Stops
2. Slow Cycles – Reduced Speed
Quality Loss
1. Production Rejects – Process Defects
2. Startup Rejects – Reduced Yield

IO-Link is a smart, easy and universal way to connect devices into your controls network.

The advantage of IO-Link is that it allows you to connect to EtherNet/IP, CC-Link & CC-LinkIE Field, Profinet & Profibus and EtherCAT & TCP/IP regardless of the brand of PLC. IO-Link also allows you to connect analog devices by eliminating traditional analog wiring and provides values in actual engineering units without scaling back at the PLC processor.

Being smart, easy and universal, IO-Link helps simplify controls architecture and provides visibility down to the sensor and device.

IO-Link communicates the following:

Process data (Control, cyclical communication of process status)
Parameter data (Configuration, messaging data with configuration information)
Event data (Diagnostics, Communication from device to master (diagnostics/errors )

This makes it the backbone of the Smart Factory as shown in the graphic below.