Engine fault diagnosis based on a morphological neural network using a morphological filter as a preprocessor

Abstract

Feature extraction and faults classification are the two most significant issues involved in the field of mechanical fault diagnosis problems. In this work, we address these two problems using mathematical morphology and non-negative matrix factorization. In particular, we present a novel engine fault diagnosis scheme utilizing the averaged multi-scale morphological filter to enhance the vibration signals, non-negative matrix factorization to characterize the signals, and a constructive morphological neural network to classify the engine operating states. Eight engine running states including the healthy state and seven defective states are tested in an engine experiment rig to evaluate the presented fault diagnosis scheme. Conventional feature extraction methods as well as classifiers popularly used in the literature are also employed as a comparison. The experimental results indicate the proposed approach to be an effective and efficient scheme for detection of the intelligent faults of engines.