LAUSR

Predictive maintenance (PdM) has become a major issue in system health monitoring, as machines are operating under more complex and diverse conditions nowadays. Besides minimizing the risk of a catastrophic failure, a proper maintenance scheme can amplify system yield as well as largely reduce production and maintenance costs. This paper presents a comprehensive study of a permanent magnet brushless DC (BLDC) motor’s fault diagnosis using vibration signals. Based on the degree of deviation from the normal operating condition, three health states are chosen from the entire lifecycle of motor. Acquired signals are decomposed using ensemble empirical mode decomposition (EEMD) and the appropriate intrinsic mode function (IMF) is selected based on the similarity index. Later, selected IMF is analyzed in time-frequency domain by using continuous wavelet transform (CWT) for better localization of fault frequencies. Several statistical features that indicate the health state of the motor are also extracted to diagnose different fault states. Later, feature dimensions were reduced using principal component analysis (PCA) technique and classified using a supervised machine learning technique named k-nearest neighbor (KNN). Extracted IMF from EEMD provides significant fault related information to detect and diagnose different fault states. Proposed method is effectively used to diagnose fault at the incipient stage as well as classify different fault states at incipient stage and severe stage.