Light neural network with fewer parameters based on CNN for fault diagnosis of rotating machinery

Jin, Tongtong; Yan, Chuliang; Chen, Chuanhai; Yang, Zhaojun; Tian, Hailong; Wang, Siyuan

doi:10.1016/j.measurement.2021.109639

Cited by 86 publications

(29 citation statements)

References 40 publications

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“…e wavelet timefrequency map of the vibration signal is used as the input of 2D-CNN [27], and the frequency spectrum of the vibration signal is used as the input of 1D-CNN [28], and thus, the twostream CNN diagnostic model is composed. After a series of convolution and pooling operations of 1D-CNN and 2D-CNN models, the respective extracted features are spliced in the fully connected layer after the training.…”

Section: Construction Of Two-stream Cnnmentioning

confidence: 99%

Defect Diagnosis of Gear-Shaft Bearing System Based on the OWF-TSCNN Composed of Wavelet Time-Frequency Map and FFT Spectrum 1

Dai

Wang

et al. 2022

Shock and Vibration

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In the defect diagnosis of the gear-shaft-bearing system with compound defects, the generated vibration signals are complicated. In addition, the information acquired by a single sensor is easily affected by uncertain factors, and low diagnostic accuracy is caused when traditional defect diagnosis methods are used, which cannot meet the high-precision diagnosis requirements. Therefore, a method is developed to identify the defect types and defect degrees of the gear-shaft-bearing system efficiently. In this method, the vibration signals are collected using multiple sensors, the dual-tree complex wavelet and the optimal weighting factor (OWF) methods are used for the data layer fusion, and the preprocessing is realized through wavelet transform and FFT. A learning model based on two-stream CNN composed of 1D-CNN and 2D-CNN is established, and the obtained wavelet time-frequency map and FFT spectrum are used as the input. Then, the trained features from the output of the connected layer are classified by the SVM. Compared with the OWF-1DCNN and OWF-2DCNN models, the time consumption of the OWF-TSCNN model is increased by 14.5%–26.6%, and the convergence speed of the network is decreased. However, its accuracy reaches 100% and 99.83% in the training set and test set, and the loss entropy and over-fitting rate are also greatly reduced. The feature extraction ability and generalization ability of the OWF-TSCNN model are increased, reaching 100% diagnosis accuracy on different defect types and defect degrees, which is more suitable for defect diagnosis of the gear-shaft-bearing system.

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Section: Construction Of Two-stream Cnnmentioning

confidence: 99%

Defect Diagnosis of Gear-Shaft Bearing System Based on the OWF-TSCNN Composed of Wavelet Time-Frequency Map and FFT Spectrum 1

Dai

Wang

et al. 2022

Shock and Vibration

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Section: Introductionmentioning

confidence: 99%

“…However, low classification accuracies have been observed in conventional machine learning models for bearing scratch type of faults diagnostics and classification. 17,18,[42][43][44][45][46][47] Furthermore, the capability of individual features has never been tested in the past to determine fault classification accuracy. The literature review given in earlier paragraphs highlights the limitations of intrusive and non-intrusive condition monitoring techniques and gives a direction for the significant improvements for non-intrusive condition monitoring techniques so that it could be capable of reliable fault diagnosis in a highly noisy environment.…”

Section: Introductionmentioning

confidence: 99%

Condition monitoring of water pump bearings using ensemble classifier

Irfan

Althobiani

Alwadie

et al. 2022

Advances in Mechanical Engineering

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The bearings faults are reported to be the major reason for centrifugal pump (CPs) failures. Limited literature is available to diagnose the minor scratches in the bearing surface through non-intrusive condition monitoring techniques. Recent research on the analysis of bearing scratches through non-intrusive motor current analysis (MCA) has shown encouraging results where the comparison of machine learning and convolutional neural networks (CNNs) was performed in the classification of healthy bearings and faulty bearings (holes and scratches). The fault classification accuracy of 89.26% through MCA combination with machine learning and CNN algorithm was reported which is very low. The key factors of low accuracies were identified as low amplitudes of the harmonics in the MCA spectrum, the magnitude of environmental noise, and utilization of conventional feature extraction techniques. This problem has been tackled in this paper by developing a novel feature extractor (NFE) that extracts powerful features from the integrated current and voltage sensors data. The NFE has been derived using the threshold-based decision mechanism which has the capability to identify the location of the feature harmonic, feature extraction, measure the amplitude of the fault component, and compare it with the derived threshold. The experimental data has been collected for the bearing balls (BB), bearing cage (BC), inner race (IR) and the outer race (OR) faults, and the performance of the NFE has been tested on an ensemble classifier (CatBoost) and the better classification accuracy (99.2% for an individual feature and 100% with the combination of two or more features) of NFE has been achieved as compared to previously reported methods.

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“…Furthermore, not all features are best for representing centrifugal pump conditions and they can affect the condition classification accuracy of the classifier. To address this concern, feature preprocessing for discriminant feature extraction is of primary importance [ 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ]. Several feature dimensionality reduction and discriminancy evaluation techniques have been proposed [ 36 , 37 , 38 ].…”

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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Light neural network with fewer parameters based on CNN for fault diagnosis of rotating machinery

Cited by 86 publications

References 40 publications

Defect Diagnosis of Gear-Shaft Bearing System Based on the OWF-TSCNN Composed of Wavelet Time-Frequency Map and FFT Spectrum 1

Defect Diagnosis of Gear-Shaft Bearing System Based on the OWF-TSCNN Composed of Wavelet Time-Frequency Map and FFT Spectrum 1

Condition monitoring of water pump bearings using ensemble classifier

Multistage Centrifugal Pump Fault Diagnosis Using Informative Ratio Principal Component Analysis

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