Fault Classification of an Induction Motor Using Texture Features of Vibration Signals

This paper proposes a method for the reliable fault detection and classification of induction motors using two-dimensional (2D) texture features and a multiclass support vector machine (MCSVM). The proposed model first converts time-domain vibration signals to 2D gray images, resulting in texture patterns (or repetitive patterns), and extracts these texture features by generating the dominant neighborhood structure (DNS) map. The principal component analysis (PCA) is then used for the purpose of dimensionality reduction of the high-dimensional feature vector including the extracted texture features due to the fact that the high-dimensional feature vector can degrade classification performance, and this paper configures an effective feature vector including discriminative fault features for diagnosis. Finally, the proposed approach utilizes the one-against-all (OAA) multiclass support vector machines (MCSVMs) to identify induction motor failures. In this study, the Gaussian radial basis function kernel cooperates with OAA MCSVMs to deal with nonlinear fault features. Experimental results demonstrate that the proposed approach outperforms three state-of-the-art fault diagnosis algorithms in terms of fault classification accuracy, yielding an average classification accuracy of 100% even in noisy environments.

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Induction Motor Stator Fault Classification Using PCA-ANFIS Technique

ELEKTRIKA- Journal of Electrical Engineering ◽

10.11113/elektrika.v19n1.209 ◽

2020 ◽

Vol 19 (1) ◽

pp. 26-32

Author(s):

Ayodele Isqeel Abullateef ◽

Mohammed Faiz Sanusi ◽

Olabanji Sunday Fagbolagun

Keyword(s):

Induction Motor ◽

Membership Function ◽

Induction Motors ◽

Fuzzy Inference ◽

Principal Component ◽

Fault Classification ◽

Function Type ◽

Inference System ◽

Stator Fault

Induction motors are used commonly for industrial operations due to their ease of operation coupled with ruggedness and reliability. However, they are subjected to stator faults which result in damage and consequently revenue losses. The classification of stator fault in a three-phase induction motor based on Adaptive neuro-fuzzy inference system (ANFIS) in combination with Principal Component Analysis (PCA) is proposed in this study. A burnt motor was redesigned and rewound while data acquisition was developed to acquire the current and vibration data needed for the fault classification. The data feature extraction for the fault classification was carried out by PCA while backpropagation and the least-squares algorithms were used for the training of the data. Three principal components, which severs as input for the ANFIS, were used to represent the entire data. The ANFIS was tested under four different paradigms, while the membership function type and epoch number were changed at each instant. The ANFIS model based on the triangular membership function and 10 epoch number was found appropriate and used, bringing the accuracy of the model to over 99% with the lowest ANFIS training RMSE error of 1.1795e-6. The ANFIS validation results of the fault classification show that the results are accurate, indicating that the PCA-ANFIS technique is applicable in fault diagnosis and classification of stator faults in induction motors.

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Detection and Classification of Stator Short-Circuit Faults in Three-Phase Induction Motor

Journal of Applied Sciences and Environmental Management ◽

10.4314/jasem.v24i3.3 ◽

2020 ◽

Vol 24 (3) ◽

pp. 417-424

Author(s):

A.I. Abdullateef ◽

O.S. Fagbolagun ◽

M.F. Sanusi ◽

M.F. Akorede ◽

M.A. Afolayan

Keyword(s):

Wavelet Transform ◽

Induction Motor ◽

Euclidean Distance ◽

Induction Motors ◽

Short Circuit ◽

Fault Classification ◽

Discrete Wavelet ◽

Machine Failure ◽

Wavelet Energy

Induction motors are the backbone of the industries because they are easy to operate, rugged, economical and reliable. However, they are subjected to stator’s faults which damage the windings and consequently lead to machine failure and loss of revenue. Early detection and classification of these faults are important for the effective operation of induction motors. Stators faults detection and classification based on wavelet Transform was carried out in this study. The feature extraction of the acquired data was achieved using lifting decomposition and reconstruction scheme while Euclidean distance of the Wavelet energy was used to classify the faults. The Wavelet energies increased for all three conditions monitored, normal condition, inter-turn fault and phase-to-phase fault, as the frequency band of the signal decreases from D1 to A3. The deviations in the Euclidean Distance of the current of the Wavelet energy obtained for the phase-to-phase faults are 99.1909, 99.8239 and 87.9750 for phases A and B, A and C, B and C respectively. While that of the inter-turn faults in phases A, B and C are 77.5572, 61.6389 and 62.5581 respectively. Based on the Euclidean distances of the faults, Df and normal current signals, three classification points were set: K1 = 0.60 x 102, K2 = 0.80 x 102 and K3 = 1.00 x 102. For K2 ≥ Df ≥ K1 inter-turn faults is identified and for K3 ≥ Df ≥ K2 phase to phase fault identified. This will improve the induction motors stator’s fault diagnosis. Keywords: induction motor, stator fault classification, data acquisition system, Discrete Wavelet Transform

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