While there is ever-increasing use of industrial IoT and we are moving closer to a world where machines will be able to take care of themselves, those machines will not be fully robotised. This is according machine learning expert Mike Brooks of Aspen Technology. His company looks at large machinery used in manufacturing such as compressors and assesses the health of those compressors by taking readings from their sensors that are streamed out.
The senior director and asset performance manager at Aspen Technology explained the things he looks for in those readings. He said: “Some of these compressors have 160 signals that get streamed out to us. We combine that with the event data we get from maintenance systems to understand what are the relationships that make them work and what are the patterns that lead to failure.”
The advantage of machine learning, said Brooks, is that is can look across those 160 dimensions, whereas a human can usually only identify issues in the sensor data in retrospect if something happens.
"Patterns give us information that humans cannot see."
“There are a lot of places where the patterns that lead to failure are not evident to humans.
We found that patterns give us information that humans cannot see,” he added. Brooks likened this activity to the way that financial services companies would carry out fraud detection by looking for anomalous behaviour.
If some errant behaviour is found, Aspen Technology will request that the equipment owner take a look and confirm that either the machine is indeed breaking or that there is a change in the process. If the latter is the case Aspen Technology will need to modify its knowledge of the machine’s behaviour for the future.
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