Generalized Roughness Bearing Faults Diagnosis Based on Induction Motor Stator Current

Abstract

Despite their reliability, induction motors tend to fail. Around 41% of faults in motors are bearing related and that is the most common fault in motor field. Due to the lack of research on generalized roughness bearing fault diagnostics by use of a stator current spectrum, the presented study analyses both single-point and generalized roughness bearing faults and their classification possibilities. In this paper, a new method for generalized roughness ball bearing fault identification by use of a stator current signal analysis is presented. The algorithm relies on Discrete Wavelet Transform and Welch's spectral density analysis. The composition of both methods is used for building a feature vector for the classifier. In order to achieve classification, support vector machine classifier with linear kernel function has been applied. The validation experiment and results are presented.

Aircraft Actuator Performance Analysis Based on Dynamic Neural Network

“…DNNs have been used to diagnose faults successfully; they outperform their static counterparts and can learn the dynamics of complex nonlinear systems which the standard static NN cannot describe. An aircraft actuator is a dynamic system so the use of TDNN for fault diagnosis is more suitable compared with the static neural network that is implemented in most of the previous work [26][27][28][29].…”

“…Camarena-Martinez et al [26] used empirical mode decomposition features under variable load conditions to train static NNs, and the overall fault classifcation accuracy is 90%. At the same time, Andrijauska et al [27] and Zhang et al [28] used current and vibration signals, respectively, as fault indicators and WT as feature extraction tools to train and test diferent soft computing techniques. Meanwhile, Moloi and Yusuf [29] used continuous DWT to train GA and NN under variable speed conditions.…”