Developing a Data-Driven Unsupervised Pattern Recognition Approach for
Sensor Signal Anomaly Detection
Abstract
Coming up with a system for early detection of machine damages and
failures is one of the important challenges in the industrial
maintenance procedure to avoid additional costs and downtimes. To
approach this goal, this paper uses the signal gathered by a sensing
system which employed a spintropic sensor to measure the magnetic field
around the machine which somehow shows the machine’s behaviour. Using
this signal and focusing on analysing and processing the signal, this
paper develops a data-driven method to recognize signal patterns and
subsequently detects anomalies. A challenging task that we succeeded to
overcome in this paper is recognizing relevant signal patterns without
having any prior knowledge. An algorithm designed for this task is
therefore completely unsupervised which makes it consistent and suitable
to apply it for the signals gathered for other types of machines. Using
both frequency and time domain information, the proposed algorithm,
which utilizes signal processing and machine learning techniques, is
able to efficiently identify relevant signal patterns. Clustering
results on the real data gathered by the aforementioned sensor have
shown the high accuracy of 99.38% in recognizing patterns. Furthermore,
an anomaly score measure is used and according to its distribution,
anomalies are detected appropriately.