Novelty detection methodology based on multi-modal one-class support vector machine

The lack of information of complicated industrial systems represents one of the main limitation to implement condition monitoring and diagnosis systems. Novelty detection framework plays an essential role for monitoring systems in which the information about the different operation conditions or fault scenarios is unavailable or limited. In this context, this work presents a novelty detection approach applied to a main rotatory element of an industrial packaging machine, a camshaft. The developed novelty detection method begins with the assumption that only data corresponding to a healthy operation of the machine is available, and the objective is to detect anomalies in the behavior of the machine. To monitor the packing machine, first, the current signals acquired from the main motor are processed by means of a normalized time-frequency map. Next, a set of features are calculated from the frequency maps. Then a set of novelty models are trained. When abnormal data is detected, an alarm will be activated to be confirmed by the user. The proposed methodology includes the re-training of the novelty detection models to include such behaviors. The proposed methodology shows a good performance to identify abnormal behavior on the machine and successfully incorporate novel scenarios.