An application to transient current signal based induction motor fault diagnosis of Fourier–Bessel expansion and simplified fuzzy ARTMAP

Abstract

The start-up transient signals have been widely used for fault diagnosis of induction motor because they can reveal early defects in the development process, which are not easily detected with the signals in the steady state operation. However, transient signals are non-linear and contain multi components which need a suitable technique to process and identify the fault pattern. In this paper, the fault diagnosis problem of induction motor is conducted by a data driven framework where the Fourier-Bessel (FB) expansion is used as a tool to decompose transient current signal into series of single components. For each component, the statistical features in the time and the frequency domains are extracted to represent the characteristics of motor condition. The high dimensionality of the feature set is solved by generalized discriminant analysis (GDA) implementation to decrease the computational complexity of classification. In the meantime, with the aid of GDA, the separation of the feature clusters is increased, which enables the more classification accuracy to be achieved. Finally, the reduced dimensional features are used for classifier to perform the fault diagnosis results. The classifier used in this framework is the simplified fuzzy ARTMAP (SFAM) which belongs to a special class of neural networks (NNs) and provides a lower training time in comparison to other traditional NNs. The proposed framework is validated with transient current signals from an induction motor under different conditions including bowed rotor, broken rotor bar, eccentricity, faulty bearing, mass unbalance and phase unbalance. Additionally, this paper provides the comparative performance of (i) SFAM and support vector machine (SVM), (ii) SVM in the framework and SVM combined with wavelet transform in previous studies, (iii) the use of FB decomposition and Hilbert transform decomposition. The results show that the proposed diagnosis framework is capable of significantly improving the classification accuracy.

Health Condition Monitoring of Induction Motors

“…For IM bearing fault detection, the characteristic stator current frequency components can be used as frequency domain indicators in spectrum analysis [20]. Several techniques have been proposed in the literature for IM bearing fault detection using stator current signals.…”

An Approach to Fault Diagnosis of Rotating Machinery Using the Second-Order Statistical Features of Thermal Images and Simplified Fuzzy ARTMAP

“…Recently, SVM [24] belonging to statistical approaches has been considered as a remarkable model in classification and attracted much attention by researchers in fault diagnosis. However, in our previous work [25], the comparative performance of SVM and simplified fuzzy ARTMAP (SFAM) [26] was carried out and the result shows that SFAM is superior to SVM in aspect of the accuracy and computational cost. Accordingly, SFAM is used as the classification to diagnose the conditions of rotating machinery in this study.…”

A Two-Dimensional Fault Diagnosis Model of Induction Motors using a Gabor Filter on Segmented Images

“…Current and voltage signals have been utilized in research for easy signal measurement and low-cost monitoring systems [2][3][4][5]; however, these signals contain unnecessary components (characteristic harmonics) that typically cause signal distortion. Vibration analysis has been frequently used to diagnosis induction motor defects because it provides When compared to conversional 1D fault diagnosis models, the texture image conversion in a 2D fault diagnosis has the additional advantages of reduced noise effects and the use of small input signals; however, the extraction of distinct textures for each non-stationary and non-deterministic fault signal is still under research [21].…”