Novel Bearing Fault Diagnosis Using Gaussian Mixture Model-Based Fault Band Selection

Maliuk, Andrei S.; Prosvirin, Alexander E.; Ahmad, Zahoor; Kim, Cheol Hong; Kim, Jong‐Myon

doi:10.3390/s21196579

Cited by 25 publications

(16 citation statements)

References 43 publications

(42 reference statements)

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“…Two types of races, named the inner and outer race, a group of rolling balls, and the cage where all the balls are enclosed with equal distance, are the basic elements of bearings. Faults in bearings can occur for multiple reasons, such as extreme load, wrong installation, misalignments in the rotors, inappropriate lubrication, and material fatigue [57]. The fundamental structure of the bearing and two fault conditions that are considered in this research (outer race fault and inner race fault) are shown in Figure 1.…”

Section: Bearing Fault Frequenciesmentioning

confidence: 99%

A Bearing Fault Classification Framework Based on Image Encoding Techniques and a Convolutional Neural Network under Different Operating Conditions

Toma

Piltan

et al. 2022

Sensors

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Diagnostics of mechanical problems in manufacturing systems are essential to maintaining safety and minimizing expenditures. In this study, an intelligent fault classification model that combines a signal-to-image encoding technique and a convolution neural network (CNN) with the motor-current signal is proposed to classify bearing faults. In the beginning, we split the dataset into four parts, considering the operating conditions. Then, the original signal is segmented into multiple samples, and we apply the Gramian angular field (GAF) algorithm on each sample to generate two-dimensional (2-D) images, which also converts the time-series signals into polar coordinates. The image conversion technique eliminates the requirement of manual feature extraction and creates a distinct pattern for individual fault signatures. Finally, the resultant image dataset is used to design and train a 2-layer deep CNN model that can extract high-level features from multiple images to classify fault conditions. For all the experiments that were conducted on different operating conditions, the proposed method shows a high classification accuracy of more than 99% and proves that the GAF can efficiently preserve the fault characteristics from the current signal. Three built-in CNN structures were also applied to classify the images, but the simple structure of a 2-layer CNN proved to be sufficient in terms of classification results and computational time. Finally, we compare the experimental results from the proposed diagnostic framework with some state-of-the-art diagnostic techniques and previously published works to validate its superiority under inconsistent working conditions. The results verify that the proposed method based on motor-current signal analysis is a good approach for bearing fault classification in terms of classification accuracy and other evaluation parameters.

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Section: Bearing Fault Frequenciesmentioning

confidence: 99%

A Bearing Fault Classification Framework Based on Image Encoding Techniques and a Convolutional Neural Network under Different Operating Conditions

Toma

Piltan

et al. 2022

Sensors

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“…Bearing vibration signals frequency bands can be divided into low-frequency, mid-frequency, and high-frequency in frequency domain. Low-frequency signals are usually components related to rotating speed [ 26 ]. These low-frequency components will affect the noise reduction effect of K-adaptive VMD.…”

Section: K-adaptive Vmd and Rbf-fuzzyenmentioning

confidence: 99%

Feature Enhancement Method of Rolling Bearing Based on K-Adaptive VMD and RBF-Fuzzy Entropy

Jiao

Yue

2022

Entropy

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The complex and harsh working environment of rolling bearings cause the fault characteristics in vibration signal contaminated by the noise, which make fault diagnosis difficult. In this paper, a feature enhancement method of rolling bearing signal based on variational mode decomposition with K determined adaptively (K-adaptive VMD), and radial based function fuzzy entropy (RBF-FuzzyEn), is proposed. Firstly, a phenomenon called abnormal decline of center frequency (ADCF) is defined in order to determine the parameter K of VMD adaptively. Then, the raw signal is separated into K intrinsic mode functions (IMFs). A coefficient En for selecting optimal IMFs is calculated based on the center frequency bands (CFBs) of all IMFs and frequency spectrum for original signal autocorrelation operation. After that, the optimal IMFs of which En are bigger than the threshold are selected to reconstruct signal. Secondly, RBF is introduced as an innovative fuzzy function to enhance the feature discrimination of fuzzy entropy between bearings in different states. A specific way for determination of parameter r in fuzzy function is also presented. Finally, RBF-FuzzyEn is used to extract features of reconstructed signal. Simulation and experiment results show that K-adaptive VMD can effectively reduce the noise and enhance the fault characteristics; RBF-FuzzyEn has strong feature differentiation, superior noise robustness, and low dependence on data length.

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“…However, because of the low energy of the fault impulses, they are often obscured by interference and background noises [ 7 ]. Andrei et al [ 8 ] discriminated the bearing fault harmonics from interference noises using the fault-oriented window series of a Gaussian mixture model. However, techniques based on narrowband demodulation are unable to discriminate between interference noise and fault impulses [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Multistage Centrifugal Pump Fault Diagnosis Using Informative Ratio Principal Component Analysis

Ahmad

Nguyen

Ahmad

et al. 2021

Sensors

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This study proposes a fault diagnosis method (FD) for multistage centrifugal pumps (MCP) using informative ratio principal component analysis (Ir-PCA). To overcome the interference and background noise in the vibration signatures (VS) of the centrifugal pump, the fault diagnosis method selects the fault-specific frequency band (FSFB) in the first step. Statistical features in time, frequency, and wavelet domains were extracted from the fault-specific frequency band. In the second step, all of the extracted features were combined into a single feature vector called a multi-domain feature pool (MDFP). The multi-domain feature pool results in a larger dimension; furthermore, not all of the features are best for representing the centrifugal pump condition and can affect the condition classification accuracy of the classifier. To obtain discriminant features with low dimensions, this paper introduces a novel informative ratio principal component analysis in the third step. The technique first assesses the feature informativeness towards the fault by calculating the informative ratio between the feature within the class scatteredness and between-class distance. To obtain a discriminant set of features with reduced dimensions, principal component analysis was applied to the features with a high informative ratio. The combination of informative ratio-based feature assessment and principal component analysis forms the novel informative ratio principal component analysis. The new set of discriminant features obtained from the novel technique are then provided to the K-nearest neighbor (K-NN) condition classifier for multistage centrifugal pump condition classification. The proposed method outperformed existing state-of-the-art methods in terms of fault classification accuracy.

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Novel Bearing Fault Diagnosis Using Gaussian Mixture Model-Based Fault Band Selection

Cited by 25 publications

References 43 publications

A Bearing Fault Classification Framework Based on Image Encoding Techniques and a Convolutional Neural Network under Different Operating Conditions

A Bearing Fault Classification Framework Based on Image Encoding Techniques and a Convolutional Neural Network under Different Operating Conditions

Feature Enhancement Method of Rolling Bearing Based on K-Adaptive VMD and RBF-Fuzzy Entropy

Multistage Centrifugal Pump Fault Diagnosis Using Informative Ratio Principal Component Analysis

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