1
$\begingroup$

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.

Thanks

$\endgroup$

1 Answer 1

0
$\begingroup$

Just a thought, you could use "boosting", e.g. monitor the relation of two similar sensors. To stick to the example, the ratio of temparature measurement of unit 1 and unit 2 should stay within a certain boundary.

I would consider combining sensors that either measure similar things, or use similar technologies, co-variances may be too computationally heavy.

Another technique, similar to the above, that is used in a different context is to compare one such measurement to the median and average of all such measurements. If the value of one sensor exceeds a threshold, you can do a relatively simple voting algorithm. This is how aeroplanes select altitudes from four sensors.

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.