How to prevent breakdowns, and therefore save time and money?
Machine learning and artificial intelligence can help us monitor and, more importantly, correctly diagnose device deviations.
Last update at
December 23, 2022
In the early afternoon, the assembly hall of a manufacturer in the automotive sector suddenly faces an unexpected issue. The gradual loss of pressure in the compressed air piping system indicates more serious problems, which escalate a few minutes later in a complete loss of pressure in the pipeline, which will prevent the use of all pneumatic tools. The production stops, and a race against time begins. Every hour costs the manufacturer a significant amount of money. This unenviable situation is caused by the air compressor failure.
The signals indicating this failure occurred weeks before this event. But for this part of the equipment, no tools for early failure detection were used. You might think about dozens of similar situations that might cost your business considerable financial and time expenses. What measures could have been taken to prevent this? Condition monitoring is a process of measuring the parameters of machines such as the temperature, vibrations, pressure, el. current etc. to detect and prevent failures.
You might argue that you use a bunch of sensors to monitor your critical infrastructure. Well, that is a good approach. But in such a large amount of data, looking for various outliers is like looking for a needle in a haystack. To overcome this problem, traditional condition monitoring must be combined with data science and machine learning.
In the context of condition monitoring, we must also mention anomalies. A classic definition of an anomaly was given by Douglas Hawkins: “An anomaly is an observation which deviates so much from other observations as to arouse suspicions that it was generated by a different mechanism.”
There are a number of diagnostic approaches that can help reveal anomalies. A few of them are listed below.
However, not all of the known ML methods are suitable for this. In the real world we typically miss enough data representing the fault state. For this reason, using supervised learning methods that map input to output based on many examples in the learning process is not a good choice.
The autoencoder (typically represented by the neural network) learns how the normal operation is represented within the input dataset. When an anomaly occurs, the autoencoder output displays a large reconstruction error.
For smaller applications, often no precautions are taken. This approach is sometimes called "run to failure." However, this does not mean that the consequences of such a failure may not be severe.
Edge computing is an approach where data processing takes place as close to the end device as possible – this is the opposite of cloud computing. When edge computing runs on low-power hardware (e.g. a microcontroller) and involves machine learning, we often call it TinyML.
Chipmakers realize that TinyML will become a fast-growing segment in the upcoming years. Therefore, many have started offering specialized AI processors supporting the energy-efficient running of ML algorithms.
Platforms listed above contain a low-power microcontroller and a hardware accelerator supporting low-power Convolutional Neural Networks inference. It can be built as an all-in-one chip (MAX78000, ECM3532) or split into a control MCU and a separate accelerator chip.
Due to the low power consumption, it is able to power these boards only from a battery and/or combine them with energy harvesting. We can easily build various always-on smart sensors, which will also find use in maintenance prediction. Such hardware enables easy deployment of AI and machine learning in applications where cost was a major barrier in the past. Together with the Internet of Things, it forms one of the cornerstones of Industry 4.0.
Here at Consilia, we also deal with the AI/ML deployment in industrial, medical, and other applications. We can offer case studies, the proof of concept design, or directly support customer product development in this field. You can read more about our services in hardware and software development.
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