Accurate predictive models require a large number of devices in the field to have failed so that they have enough fail data to use for predictions. For some well-crafted industrial devices, failures on this scale can take years. Anomaly detection can identify devices that are not behaving like the other devices in the fleet. It can also be used to wade through thousands of similar messages and pinpoint the messages that are not like the others.
Anomaly detection in machine learning can be unsupervised, supervised, or semi-supervised. Usually, it starts by using an unsupervised machine learning algorithm to cluster data into patterns of behavior or groups. This presents a series of data in buckets. When the machines are examined, some of the buckets identify behavior while some identify an issue with the device. The device may have exhibited different patterns of behavior in a resting state, an in-use state, a cold state, or something that...
