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I have series of sensors (around 4k) and each sensor will measure the amplitudes at each point.Suppose I train the neural network with sufficent set of 4k values (N * 4k shape). The machine will find a pattern in the series of values.If the values stray away from the pattern (that is anomaly) it can detect the point and will be able to say that anomaly is in the 'X'th sensor.Is this possible.If so what kind of neural network should I use?

Fasty
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1 Answers1

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Since you are having a time series inputs you can use sequential models like RNN, LSTM, GRU. And use softmax layer at the end, which can output (normal/anomaly). you can use the same model(weights) 4k times to find which sensor is at fault. Or same sequential network can be trained with multi dimensional softmax (anomaly1/normal1 ... fault4k/ normal4k)

But such networks won't work well when data is imbalanced(anomalies are rare).

you can also try RPCA for anomaly detection.

newlearnershiv
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