Fault Diagnosis in Direct Current Electric Motors via an Artificial Neural Network

The combined problem of fault detection and classification (referred to as fault diagnosis) of Direct Current (DC) electric motors via a simple, yet powerful, technique based on an Artificial Neural Network (ANN) is proposed. The ability of an ANN in identifying patterns with high fidelity-without the need of any rigorous mathematical model of the system under investigation-leads to an excellent diagnosis performance, even for faults that result in almost indistinguishable output system responses (both in time and in frequency domain). The flexibility and speed of the presented method indicate that it can easily be applied to on-line fault diagnosis as well.