Rolling bearing faults severity classification using a combined approach based on multi-scales principal component analysis and fuzzy technique

Babouri, Mohamed Khemissi; Djebala, Abderrazek; Ouelaa, Nouredine; Oudjani, Brahim; Younes, Ramdane

doi:10.1007/s00170-020-05342-6

Cited by 19 publications

(9 citation statements)

References 37 publications

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“…It can be noticed that for all the classifiers, the computational burden is acceptable with a testing time lower than 200 ms regardless of the load condition. We notice that in the literature most of the techniques only provide the classification accuracy [ 65 , 66 , 67 ].…”

Section: Results and Discussionmentioning

confidence: 99%

Performance of Bearing Ball Defect Classification Based on the Fusion of Selected Statistical Features

Mezni

Delpha

Braham

2022

Entropy

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Among the existing bearing faults, ball ones are known to be the most difficult to detect and classify. In this work, we propose a diagnosis methodology for these incipient faults’ classification using time series of vibration signals and their decomposition. Firstly, the vibration signals were decomposed using empirical mode decomposition (EMD). Time series of intrinsic mode functions (IMFs) were then obtained. Through analysing the energy content and the components’ sensitivity to the operating point variation, only the most relevant IMFs were retained. Secondly, a statistical analysis based on statistical moments and the Kullback–Leibler divergence (KLD) was computed allowing the extraction of the most relevant and sensitive features for the fault information. Thirdly, these features were used as inputs for the statistical clustering techniques to perform the classification. In the framework of this paper, the efficiency of several family of techniques were investigated and compared including linear, kernel-based nonlinear, systematic deterministic tree-based, and probabilistic techniques. The methodology’s performance was evaluated through the training accuracy rate (TrA), testing accuracy rate (TsA), training time (Trt) and testing time (Tst). The diagnosis methodology has been applied to the Case Western Reserve University (CWRU) dataset. Using our proposed method, the initial EMD decomposition into eighteen IMFs was reduced to four and the most relevant features identified via the IMFs’ variance and the KLD were extracted. Classification results showed that the linear classifiers were inefficient, and that kernel or data-mining classifiers achieved 100% classification rates through the feature fusion. For comparison purposes, our proposed method demonstrated a certain superiority over the multiscale permutation entropy. Finally, the results also showed that the training and testing times for all the classifiers were lower than 2 s, and 0.2 s, respectively, and thus compatible with real-time applications.

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Section: Results and Discussionmentioning

confidence: 99%

Performance of Bearing Ball Defect Classification Based on the Fusion of Selected Statistical Features

Mezni

Delpha

Braham

2022

Entropy

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“…And consequently, the cumulative contribution rate of PCs which can determine the number of useful PCs is assigned as 85% according to some attractive findings in feature extraction and dimensionality reduction for rolling bearing fault detection and diagnosis. [41][42][43] Thirdly, the original SVM model is only able to achieve the binary classification, but multi-class classification is the most case in the practical fault diagnosis problem. So, an improved classifier, called SVM-based decision tree (SVMDT) designed for solving multi-class classification problem, 44 is employed to identify rolling bearing faults.…”

Section: Proposed Methodsmentioning

confidence: 99%

Intelligent identification of incipient rolling bearing faults based on VMD and PCA-SVM

Deng

Zhang

Zhao

2022

Advances in Mechanical Engineering

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Rolling bearings are the key components of rotating machinery. Incipient fault diagnosis of bearing plays an increasingly important role in guaranteeing normal and safe operation of rotating machinery. However, because of the high complexity of the fault feature extraction, the incipient faults of rolling bearings are difficult to diagnose. To solve this problem, this paper presents a new incipient fault intelligent identification method of rolling bearings based on variational mode decomposition (VMD), principal component analysis (PCA), and support vector machines (SVM). In the proposed method, the bearing vibration signals are decomposed by using VMD, and a series of intrinsic mode functions (IMFs) with different frequencies are obtained. Then, the energy and kurtosis values of each IMF are calculated to reveal the intrinsic characteristics of the vibration signals in different scales. Finally, all energy and kurtosis values of IMFs are processed via PCA and subsequently fed into SVM to achieve the bearing fault identification automatically. The effectiveness of this method is verified through the experimental bearing data. The verification results indicate that the proposed method can effectively extract the bearing fault features and accurately identify the bearing incipient faults, and outperform the two compared methods obviously in identification accuracy and computation time.

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“…For each particle p with position dp = {λp, kp} do Construct the MMD of the marginal distribution using equation (7).…”

Section: Domentioning

confidence: 99%

“…The traditional fault-diagnosis method often needs to manually extract signal features, such as recurrencequantification analysis parameters [5] and multi-scale features based on fault mechanisms [6,7], and feed them into a classification model for health-state identification. For instance, Lu et al [8] adopted complementary ensemble empirical-mode decomposition to decompose bearing signals and extract root mean square values from the decomposed components as discriminative features.…”

Section: Introductionmentioning

confidence: 99%

An autoencoder with adaptive transfer learning for intelligent fault diagnosis of rotating machinery

Tang

Lin

Liu

et al. 2021

Meas. Sci. Technol.

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Under variable working conditions, a problem arises, which is that it is difficult to obtain enough labeled data; to address this problem, an adaptive transfer autoencoder (ATAE) is established to diagnose faults in rotating machinery. First, a data adaptation module, which calculates the maximum mean discrepancy for the network hidden-layer data in reproducing kernel Hilbert space, is introduced to the autoencoder network, thus making the classification model operate under variable working conditions. Variation particle-swarm optimization is then invoked to optimize the data adaptation parameters. Finally, the k-nearest neighbors algorithm, as the classification layer of the network, identifies the state of health of the rotating machinery. The capabilities of the intelligent fault-diagnosis network are verified using vibration signals from a bearing test rig and a gearbox test rig. The experimental results suggest that, compared with state-of-the-art diagnosis methods, the proposed ATAE network can significantly boost diagnostic performance in the absence of target vibration signal labels.

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Rolling bearing faults severity classification using a combined approach based on multi-scales principal component analysis and fuzzy technique

Cited by 19 publications

References 37 publications

Performance of Bearing Ball Defect Classification Based on the Fusion of Selected Statistical Features

Performance of Bearing Ball Defect Classification Based on the Fusion of Selected Statistical Features

Intelligent identification of incipient rolling bearing faults based on VMD and PCA-SVM

An autoencoder with adaptive transfer learning for intelligent fault diagnosis of rotating machinery

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