Degradation Assessment and Fault Modes Classification Using Logistic Regression

Abstract

Real-time health monitoring of industrial components and systems that can detect, classify and predict impending faults is critical to reducing operating and maintenance cost. This paper presents a logistic regression based prognostic method for on-line performance degradation assessment and failure modes classification. System condition is evaluated by processing the information gathered from controllers or sensors mounted at different points in the system, and maintenance is performed only when the failure∕malfunction prognosis indicates instead of periodic maintenance inspections. The wavelet packet decomposition technique is used to extract features from non-stationary signals (such as current, vibrations), wavelet package energies are used as features and Fisher’s criteria is used to select critical features. Selected features are input into logistic regression (LR) models to assess machine performance and identify possible failure modes. The maximum likelihood method is used to determine parameters of LR models. The effectiveness and feasibility of this methodology have been illustrated by applying the method to a real elevator door system.