I have an IoT problem I am trying to operationalize.
I have multiple machines that should behave similarly over time (a good example is wind turbines nearby). They each have multiple sensors. And I have data over time.
Besides detecting absolute values that are dangerous (e.g. overheating), I am trying to operationalize how would I cross-reference the different sensors and different machines to detect other anomalies such as sensor failure.
Best I can think of at the moment are disjointed models within-machine/cross-sensors regression, and across-machines/across-sensors regression and then looking at then flagging large residuals.
HOWEVER, I am thinking there could be a way to maybe assemble this as some sort of covariance matrix or another, more computationally elegant solution. However, googling and reviewing literature has not yielded much of value yet.
I know this is broad, but hopefully allowed.