Getting Condition Data From The Shop Floor to Your Software

IIoT (Industrial Internet of Things)  is becoming more mainstream, leading to more vendors implementing innovative monitoring capabilities in the new generation of sensors. These sensors are now multifunctional and provide a host of additional features such as self-monitoring.

With these intelligent sensors, it is possible to set up a system that enables continuous monitoring of the machines and production line. However, the essential requirement to use the provided data for analysis and condition monitoring for preventative and predictive maintenance is to get it from the shop floor to the MES, ERP, or other analysis software suites.

There are a variety of ways this can be done. In this post we will look at a few popular ways and methods to do so.

The most popular and straightforward implementation is using a REST API(also known as RESTful API). This has been the de facto standard in e consumer space to transport data. It allows multiple data formats to be transferred, including multimedia and JSON (Javascript Object Notation)

This has certain disadvantages like actively polling for the data, making it unsuitable for a spotty network, and having high packet loss.

MQTT(Message Queuing Telemetry Transport) eliminates the above problem. It’s very low bandwidth and works excellent on unreliable networks as it works on a publish/subscribe model. This allows the receiver to passively listen for the data from the broker. The broker only notifies when there is a change and can be configured to have a Quality of Service(QoS) to resend data if one of them loses connection. This has been used in the IoT world for a long time has become a standard for data transport, so most of software suits have this feature inbuilt.

The third option is to use OPCUA, which is the standard for M2M communication. OPCUA provides additional functionality over MQTT as it was developed with machine communication in mind. Notably, inbuilt encryption allows for secure and authenticated communication.

In summary, below is a comparison of these protocols.

A more detailed explanation can be found for these standards :

REST API : https://www.redhat.com/en/topics/api/what-is-a-rest-api

MQTT : https://mqtt.org/

OPCUA : https://opcfoundation.org/about/opc-technologies/opc-ua/

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).

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