A Novel Deep Learning Model for the Detection and Identification of Rolling Element-Bearing Faults

Shenfield, Alex; Howarth, Martin

doi:10.3390/s20185112

Cited by 118 publications

(65 citation statements)

References 37 publications

(51 reference statements)

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“…It has been used for different tasks within the field of fault detection and diagnostics. Recently published methods include stacked denoising autoencoder [35] or recurrent neural networks [36] (a comprehensive overview is given by Neupane and Seok [37]). The dataset is especially suited to demonstrate solutions related to diagnosing faults under different operating conditions (different loads in this case) and transferring models between these different conditions (domain adaptation) [27][28][29][30].…”

Section: Case Studies 41 Datasetmentioning

confidence: 99%

Contrastive Learning for Fault Detection and Diagnostics in the Context of Changing Operating Conditions and Novel Fault Types

Rombach

Michau

Fink

2021

Sensors

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Reliable fault detection and diagnostics are crucial in order to ensure efficient operations in industrial assets. Data-driven solutions have shown great potential in various fields but pose many challenges in Prognostics and Health Management (PHM) applications: Changing external in-service factors and operating conditions cause variations in the condition monitoring (CM) data resulting in false alarms. Furthermore, novel types of faults can also cause variations in CM data. Since faults occur rarely in complex safety critical systems, a training dataset typically does not cover all possible fault types. To enable the detection of novel fault types, the models need to be sensitive to novel variations. Simultaneously, to decrease the false alarm rate, invariance to variations in CM data caused by changing operating conditions is required. We propose contrastive learning for the task of fault detection and diagnostics in the context of changing operating conditions and novel fault types. In particular, we evaluate how a feature representation trained by the triplet loss is suited to fault detection and diagnostics under the aforementioned conditions. We showcase that classification and clustering based on the learned feature representations are (1) invariant to changing operating conditions while also being (2) suited to the detection of novel fault types. Our evaluation is conducted on the bearing benchmark dataset provided by the Case Western Reserve University (CWRU).

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Section: Case Studies 41 Datasetmentioning

confidence: 99%

Contrastive Learning for Fault Detection and Diagnostics in the Context of Changing Operating Conditions and Novel Fault Types

Rombach

Michau

Fink

2021

Sensors

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“…The effectiveness of this method is justified by the results obtained for reliable detection and this technique can classify the faults into different types. The authors in [33] proposed a novel deep learning-based method for identifying the rolling element-bearing fault. This method is capable of operating on the raw vibration signals for the diagnosis of bearing faults of the electromechanical systems.…”

Section: Literature Reviewmentioning

confidence: 99%

Research on fan vibration fault diagnosis based on image recognition

Huang¹,

Qiao²,

Khanna³

et al. 2021

J. vibroeng.

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The conventional methods for vibration fault detection and diagnosis relies on feature extraction from the waveforms of the vibration signals. This article exploits the scope of image recognition application for the detection and diagnosis of fan vibration faults. In this paper, a novel image recognition technique is proposed for vibration-based fault diagnosis using the spectrum images of the vibration signals. 1D vibration signal spectrum is initially computed using Fast Fourier Transform (FFT) and the FFT frequencies are adjusted such that it captures a vibration spectrum diagram as 2D image representation. FFT based vibration analysis is done and the image recognition concept is utilized for feature extraction and a machine learning classification module is used for fault analysis and diagnosis. Effective feature generation is done using Principal Component Analysis (PCA) by removing the redundancy from the feature vectors and machine learning classifiers are used to obtain improved image recognition and classification performance. Artificial Neural Network (ANN) classifier yields better performance in terms of various performance parameters and percentage improvement in terms of accuracy for ANN classification methods over Support Vector Machine (SVM), k-Nearest Neighbours (kNN) and Random Forest Ensemble (RFE) methods are 10.01 %, 4.51 % and 2.01 % respectively. Comparative scenarios are considered in this work for fan vibration fault detection as well as diagnosis based on the image features for various realistic vibration fault conditions. Effectiveness of the proposed image recognition-based technique is compared with the state-of-the-art methods, justifying its outperformance for fan fault detection and diagnosis using the combination of spectrum adjustment, PCA and ANN classification method.

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“…Correspondingly, intelligent diagnosis methods have gradually become mainstream. On account of the excellent data processing capabilities, many methods based on artificial intelligence have gradually been employed in the territory of mechanical fault diagnosis, such as convolutional neural networks (CNN) [ 7 , 8 , 9 ], autoencoder [ 9 , 10 ], deep belief networks [ 11 , 12 ], and recurrent neural networks [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Fault Diagnosis of Hydraulic Piston Pump Based on Wavelet Analysis and Improved AlexNet

Zhu

Wang

et al. 2021

Sensors

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Hydraulic piston pump is the heart of hydraulic transmission system. On account of the limitations of traditional fault diagnosis in the dependence on expert experience knowledge and the extraction of fault features, it is of great meaning to explore the intelligent diagnosis methods of hydraulic piston pump. Motivated by deep learning theory, a novel intelligent fault diagnosis method for hydraulic piston pump is proposed via combining wavelet analysis with improved convolutional neural network (CNN). Compared with the classic AlexNet, the proposed method decreases the number of parameters and computational complexity by means of modifying the structure of network. The constructed model fully integrates the ability of wavelet analysis in feature extraction and the ability of CNN in deep learning. The proposed method is employed to extract the fault features from the measured vibration signals of the piston pump and realize the fault classification. The fault data are mainly from five different health states: central spring failure, sliding slipper wear, swash plate wear, loose slipper, and normal state, respectively. The results show that the proposed method can extract the characteristics of the vibration signals of the piston pump in multiple states, and effectively realize intelligent fault recognition. To further demonstrate the recognition property of the proposed model, different CNN models are used for comparisons, involving standard LeNet-5, improved 2D LeNet-5, and standard AlexNet. Compared with the models for contrastive analysis, the proposed method has the highest recognition accuracy, and the proposed model is more robust.

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A Novel Deep Learning Model for the Detection and Identification of Rolling Element-Bearing Faults

Cited by 118 publications

References 37 publications

Contrastive Learning for Fault Detection and Diagnostics in the Context of Changing Operating Conditions and Novel Fault Types

Contrastive Learning for Fault Detection and Diagnostics in the Context of Changing Operating Conditions and Novel Fault Types

Research on fan vibration fault diagnosis based on image recognition

Intelligent Fault Diagnosis of Hydraulic Piston Pump Based on Wavelet Analysis and Improved AlexNet

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