Information Theoretic Criteria for Induction Motor Fault Identification

Abstract

The objective of our work is to identify the inter-turn incipient short fault that occurs in the induction motor at no load condition. The method used in fault identification is Information Theoretic Criteria, which uses Frequency Signal Dimension Order (FSDO) estimator and fault decision module. The FSDO estimator estimates the number of frequencies in stator current signatures using Minimum Description Length (MDL) criterion and Akaike Information Criteria (AIC). Fault decision module uses the number of frequencies as fault index in detecting the fault and identifying the fault severity. The proposed method is able to identify the fault from the data buried in noise. MDL yields consistent estimate, the fault index obtained using MDL criterion is considered for diagnosing the faults. The CDF plot of MDL and AIC helps in proving the fault severity results obtained from fault index value. With very lesser values of sampled data, the proposed method is able to distinguish between healthy and faulty conditions. This novel approach diagnoses the inter-turn incipient fault with very simple calculation and it needs only very few number of measured data for estimating the number of fault frequencies associated with the faulty conditions.