Fault classification of broken rotor bars in induction motors based on envelope current analysis

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.

Download Full-text

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.

Download Full-text

Broken rotor bars fault detection in induction motors based on current envelope and neural network

Eastern-European Journal of Enterprise Technologies ◽

10.15587/1729-4061.2021.227315 ◽

2021 ◽

Vol 3 (2 (111)) ◽

pp. 88-95

Author(s):

Mohammed Obaid Mustafa

Keyword(s):

Neural Network ◽

Condition Monitoring ◽

Induction Motors ◽

Fault Classification ◽

Monitoring Method ◽

Broken Rotor Bars ◽

Stator Current ◽

Motor Current ◽

Machine Failure ◽

Feature Extraction And Selection

The growing demand for dependable manufacturing techniques has sped up research into condition monitoring and fault diagnosis of critical motor parts. On the other hand, in modern industry, machine maintenance is becoming increasingly necessary. An insufficient maintenance strategy can result in unnecessarily high downtime or accidental machine failure, resulting in significant financial and even human life losses. Downtime and repair costs rise as a result of failure. Furthermore, developing an online condition monitoring method may be one solution to come up for the problem. Early detection of faults is very vital since they grow quickly and can cause further problems to the motor. This paper proposes an effective strategy for the classification of broken rotor bars (BRBs) for induction motors (IMs) that uses a new approach based on Artificial Neural Network (ANN) and stator current envelope. The stator current envelope is extracted using the cubic spline interpolation process. This is based on the idea that the amplitude-modulated motor current signal can be revealed using the motor current envelope. The stator current envelope is used to select seven features, which will be used as input for the neural network. Five IM conditions were experimentally used in this study, including a part of BRB, 1 BRB, 2 BRBs and 3 BRBs. The new feature extraction and selection approach achieves a higher level of accuracy than the conventional method for motor fault classification, according to the experimental results. Indeed, the results are impressive, and it is capable of detecting the exact number of broken rotor bars under full load conditions

Download Full-text

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

Download Full-text

Bearing Fault Classification of Induction Motors Using Discrete Wavelet Transform and Ensemble Machine Learning Algorithms

Applied Sciences ◽

10.3390/app10155251 ◽

2020 ◽

Vol 10 (15) ◽

pp. 5251 ◽

Cited By ~ 2

Author(s):

Rafia Nishat Toma ◽

Jong-Myon Kim

Keyword(s):

Machine Learning ◽

Wavelet Transform ◽

Discrete Wavelet Transform ◽

Induction Motors ◽

Fault Classification ◽

Discrete Wavelet ◽

Current Signal ◽

Bearing Fault ◽

Ensemble Machine Learning

Bearing fault diagnosis at early stage is very significant to ensure seamless operation of induction motors in industrial environment. The identification and classification of faults helps to undertook maintenance operation in an efficient manner. This paper presents an ensemble machine learning-based fault classification scheme for induction motors (IMs) utilizing the motor current signal that uses the discrete wavelet transform (DWT) for feature extraction. Three wavelets (db4, sym4, and Haar) are used to decompose the current signal, and several features are extracted from the decomposed coefficients. In the pre-processing stage, notch filtering is used to remove the line frequency component to improve classification performance. Finally, the two ensemble machine learning (ML) classifiers random forest (RF) and extreme gradient boosting (XGBoost) are trained and tested using the extracted feature set to classify the bearing fault condition. Both classifier models demonstrate very promising results in terms of accuracy and other accepted performance indicators. Our proposed method achieves an accuracy slightly greater than 99%, which is better than other models examined for the same dataset.

Download Full-text

Insulation Deterioration Detection by Zero-Sequence Current Analysis for Induction Motors

IEEJ Transactions on Industry Applications ◽

10.1541/ieejias.131.1457 ◽

2011 ◽

Vol 131 (12) ◽

pp. 1457-1464 ◽

Cited By ~ 3

Author(s):

Hideki Iwanaga ◽

Hiroshi Inujima

Keyword(s):

Induction Motors ◽

Current Analysis ◽

Zero Sequence

Download Full-text

Fault Classification of Hydroelectric Generating Unit Based on Improved Evidence Theory

The Open Fuels & Energy Science Journal ◽

10.2174/1876973x01407010078 ◽

2014 ◽

Vol 7 (1) ◽

pp. 78-83 ◽

Cited By ~ 1

Author(s):

Jiatang Cheng ◽

Li Ai ◽

Zhimei Duan ◽

Yan Xiong

Keyword(s):

Fault Diagnosis ◽

Information Fusion ◽

Evidence Theory ◽

Fault Classification ◽

Fusion Method ◽

Inconsistent Result ◽

High Conflict

Aiming at the problem of the conventional vibration fault diagnosis technology with inconsistent result of a hydroelectric generating unit, an information fusion method was proposed based on the improved evidence theory. In this algorithm, the original evidence was amended by the credibility factor, and then the synthesis rule of standard evidence theory was utilized to carry out information fusion. The results show that the proposed method can obtain any definitive conclusion even if there is high conflict evidence in the synthesis evidence process, and may avoid the divergent phenomenon when the consistent evidence is fused, and is suitable for the fault classification of hydroelectric generating unit.

Download Full-text