This paper (Part 1) describes the principles of a novel unsupervised adaptive neural network anomaly detection technique, called componential coding, in the context of condition monitoring of electrical machines. N umerical examples are given to illustrate the technique' s capabilities. The companion paper (Part 2), which follows, assesses componential coding in its application to real data recorded from a known machine and an entirely unseen machine (a conventional induction motor and a novel transverse ux motor respectively). Componential coding is particularly suited to applications in which no machine-speci c tailored techniques have been developed or in which no previous monitoring experience is available. This is because componential coding is an unsupervised technique that derives the features of the data during training, and so requires neither labelling of known faults nor pre-processing to enhance known fault characteristics. Componential coding offers advantages over more familiar unsupervised data processing techniques such as principal component analysis. In addition, componential coding may be implemented in a computationally ef cient manner by exploiting the periodic convolution theorem. Periodic convolution also gives the algorithm the advantage of time invariance; i.e. it will work equally well even if the input data signal is offset by arbitrary displacements in time. This means that there is no need to synchronize the input data signal with respect to reference points or to determine the absolute angular position of a rotating part.
This paper (Part 2) presents the practical application of componential coding, the principles of which were described in the accompanying Part 1 paper. F our major issues are addressed, including optimization of the neural network, assessment of the anomaly detection results, development of diagnostic approaches (based on the reconstruction error) and also benchmarking of componential coding with other techniques (including waveform measures, F ourier-based signal reconstruction and principal component analysis). This is achieved by applying componential coding to the data monitored from both a conventional induction motor and from a novel transverse ux motor. The results reveal that machine condition monitoring using componential coding is not only capable of detecting and then diagnosing anomalies but it also outperforms other conventional techniques in that it is able to separate very small and localized anomalies.
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