Vibration Sensor-Based Bearing Fault Diagnosis Using Ellipsoid-ARTMAP and Differential Evolution Algorithms

Abstract

Effective fault classification of rolling element bearings provides an important basis for ensuring safe operation of rotating machinery. In this paper, a novel vibration sensor-based fault diagnosis method using an Ellipsoid-ARTMAP network (EAM) and a differential evolution (DE) algorithm is proposed. The original features are firstly extracted from vibration signals based on wavelet packet decomposition. Then, a minimum-redundancy maximum-relevancy algorithm is introduced to select the most prominent features so as to decrease feature dimensions. Finally, a DE-based EAM (DE-EAM) classifier is constructed to realize the fault diagnosis. The major characteristic of EAM is that the sample distribution of each category is realized by using a hyper-ellipsoid node and smoothing operation algorithm. Therefore, it can depict the decision boundary of disperse samples accurately and effectively avoid over-fitting phenomena. To optimize EAM network parameters, the DE algorithm is presented and two objectives, including both classification accuracy and nodes number, are simultaneously introduced as the fitness functions. Meanwhile, an exponential criterion is proposed to realize final selection of the optimal parameters. To prove the effectiveness of the proposed method, the vibration signals of four types of rolling element bearings under different loads were collected. Moreover, to improve the robustness of the classifier evaluation, a two-fold cross validation scheme is adopted and the order of feature samples is randomly arranged ten times within each fold. The results show that DE-EAM classifier can recognize the fault categories of the rolling element bearings reliably and accurately.

Effective Time Domain Features for Identification of Bearing Fault using LDA and NB Classifiers

“…Further WPD for feature extraction and mRMR for feature selection and DE-EAM for classification are employed and average accuracy of 96.1% is obtained. Similarly spectrum imaging and feature enhancement is applied for feature extraction, and classification is realized using ANN by the authors in [22] for dataset Ai with 2HP load and obtains an accuracy of 96.9%. Data setA-iii is assessed for load 0, 1 and 2HP using 20 features by authors in [18] and attained an accuracy of 99.56%,100%,99.89% respectively.…”

“…This analysis using WT methodology [13] suffers a major setback due compared the results thus obtained for variation in parameters like accuracy, sensitivity and specificity with that obtained using LDA classifier. The results evince that time domain features identify the bearing faults with good accuracy compared to other features considered in the literature [2], [13][14], [17][18][19][20][21][22]. Overall 63 feature set combinations from 6 features have been employed for bearing fault diagnosis of 5 groups of data involving 15 datasets which has been drawn in combinations of location of fault and load condition.…”

“…Overall 63 feature set combinations from 6 features have been employed for bearing fault diagnosis of 5 groups of data involving 15 datasets which has been drawn in combinations of location of fault and load condition. Though condition monitoring schemes of bearing faults involve Feature extraction, feature selection, features reduction and classification processes which will be handled by various methodologies like WT for feature extraction and minimum-redundancy maximum-relevancy method for feature selection and differential evolution algorithm for classification [21] and spectrum imaging has been implemented for feature extraction and enhancement [22] in previous works. The present work focuses on the simplest scheme development to serve the purpose;…”

A Novel Bearing Fault Diagnosis Method Based on Gaussian Restricted Boltzmann Machine

“…In many published references [7,21,22], the authors analyzed only a few fault types, and the training data and the testing data have the same speed and load. However, the running conditions of bearing are very complex in engineering application, and the fault diagnosis method should be robust and insensible to the variation of speed and load.…”

On the Efficacy of Ensemble of Constraint Handling Techniques in Self-Adaptive Differential Evolution

“…For recent advances in DE, the readers are referred to [16,17]. EAs suit a variety of applications in the fields of engineering and science [18][19][20][21][22][23][24]. Generally, EAs outperform traditional optimization algorithms for problems which are not continuous, non-differentiable, multi-modal, noisy and not well-defined.…”