Demystifying Machine Learning

Machine learning can help organizations improve manufacturing operations and increase efficiency, productivity, and safety by analyzing data from connected machines and sensors, machine. For example, its algorithms can predict when equipment will likely fail, so manufacturers can schedule maintenance before problems occur, thereby reducing downtime and repair costs.

How machine learning works

Machine learning teaches computers to learn from data – to do things without being specifically told how to do them. It is a type of artificial intelligence that enables computers to automatically learn or improve their performances by learning from their experiences.

machine learning stepsImagine you have a bunch of toy cars and want to teach a computer to sort them into two groups: red and blue cars. You could show the computer many pictures of red and blue cars and say, “this is a red car” or “this is a blue car” for each one.

After seeing enough examples, the computer can start to guess which group a car belongs in, even if it’s a car that it hasn’t seen before. The machine is “learning” from the examples you show to make better and better guesses over time. That’s machine learning!

Steps to translate it to industrial use case

As in the toy car example, we must have pictures of each specimen and describe them to the computer. The image, in this case, is made up of data points and the description is a label. The sensors collecting data can be fed to the machine learning algorithm in different stages of the machine operation – like when it is running optimally, needs inspection, or needs maintenance, etc.

Data taken from vibration, temperature or pressure measures, etc., can be read from different sensors, depending on the type of machine or process to monitor.

In essence, the algorithm finds a pattern for each stage of the machine’s operation. It can notify the operator about what must be done given enough data points when it starts to veer toward a different stage.

What infrastructure is needed? Can my PLC do it?

The infrastructure needed can vary depending on the algorithm’s complexity and the data volume. Small and simple tasks like anomaly detection can be used on edge devices but not on traditional automation controllers like PLCs. Complex algorithms and significant volumes of data require more extensive infrastructure to do it in a reasonable time. The factor is the processing power, and as close to real-time we can detect the machine’s state, the better the usability.

How to Balance the IIoT Success Equation

What are the key components to being successful when implementing Industrial IoT?  There are three major components to consider when beginning your pilot project for Industry 4.0: Strategy, Data & Action.  With a clear understanding of each of these components, successful implementations are closer than you think.

Strategy:  What is your plan? What do you need to know?  Who needs to know what?  How do we enable people to make the right decisions?  What standards will we follow?  How often do we need the data?  What data don’t we need?

Data Generation:  Devices need to generate cyclic data giving insight into the process and warning/event data to give insight into issues.  Devices should support protocols that allow requesting data without impacting the control system and structured in a way that’s logical and easy to manipulate.

Data Management:  How are we going to handle our data?  What structure does it need to be in?  Do we need internal and external access to the data?  What security requirements do we need to consider?  Which users will need the data?  Where is the data coming from?  How much data are we talking about?

Data Analytics:  Insight, Big Data, Predictive Analytics, etc.  These insights from an industrial point of view should truly drive productivity for every user.  Predictive Analytics should help us know when and where to perform maintenance on equipment and dramatically reduce downtime in the plant.

Action:  The key component of any IIoT Success.  Without daily decisions based on the strategy by every employee, failure is assured.  Supply chain needs to know that we are interested in not just the cheapest replacement component, but one that can help us generate data to improve our analytic capabilities.  Maintenance needs to be taking action on Predictive outputs and move from randomly fighting fires to purposefully preventing downtime all together.

Strategy + (Data Generation + Data Management + Data Analytics) + Action = IIoT Success

We have a strong portfolio of automation devices that enable data generation for IIoT applications, check them out at