Toward Automatic Motor Condition Diagnosis

In this study a method for automatic motor condition diagnosis is proposed. The method is based on a statistical discriminance measure which can be used to select the most discriminative features. New signals are classified to either a normal condition class or a failure class. The classification can be done traditionally using training examples from the both classes or using only probability distribution of the normal condition samples. The latter corresponds to typical situations in practice where the amount of failure data is insufficient. The results are verified using real measurements from induction motors in normal condition and with bearing faults.