Patent ID: 11860615
Assignee: TATA CONSULTANCY SERVICES LIMITED
Field: Control (Instruments)
Classification: CPC G | IPC G

Claim 8:
9. A non-transitory computer readable medium for anomaly detection and diagnosis, the non-transitory computer readable medium performs the anomaly detection and diagnosis, comprising:
collecting an input data from a plurality of sensors, via one or more hardware processors, wherein the input data is a multivariate time series data;
dividing the input data into a plurality of segments, via the one or more hardware processors, wherein each of the plurality of segments has fixed window size; and
processing the input data in each of the plurality of segments, via the one or more hardware processors, comprising:
extracting a plurality of features from the input data in the segment being processed, at a plurality of stages, using an encoding mechanism;
reconstructing the input data in the segment, by processing the extracted plurality of features using a decoding mechanism;
calculating a reconstruction error value (RE) for each instance in each of plurality of segments, corresponding to the reconstruction of the input data using the plurality of features wherein the RE value is calculated as at least one of an absolute difference as, RE
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  ,, between the original and reconstructed data, where REi, is the reconstruction error value for instance or time i, Xij is an original value of sensor j at instance or time i, X′i,j is a reconstructed value of sensor j at instance or time i, m is total number of sensors;

identifying (212) one or more segments from among the plurality of segments as segments containing anomalous data, comprising:
comparing the reconstruction error value of each of the instance with a threshold of reconstruction error, wherein the threshold of the reconstruction error is obtained as μ+Kσ where μ and σ are the mean and standard deviation of all the reconstruction errors and K is a user-defined parameter such that 95% or 99% of the reconstruction error values are lower than the threshold of reconstruction errors; and
determining all segments in which the reconstruction error value exceeds the threshold of reconstruction error in at least one instance, as segments containing anomalous data; and

identifying at least one of the plurality of sensors as faulty, based on the input data in the segments determined as containing the anomalous data.