A deep learning method for bearing fault diagnosis based on Cyclic Spectral Coherence and Convolutional Neural Networks

Chen, Zhuyun; Mauricio, Alexandre; Li, Weihua; Gryllias, Konstantinos

doi:10.1016/j.ymssp.2020.106683

Cited by 345 publications

(144 citation statements)

References 43 publications

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“…Softmax regression is usually adopted in the last layer of DNNs for multiclass classification [9,30]. Thus, soft regression is utilized as the machinery fault classifier after unsupervised feature learning.…”

Section: B Softmax Regressionmentioning

confidence: 99%

“…As a state-of-the-art machine learning technique, deep learning (DL) can learn hierarchical features automatically from large-scale data instead of manual feature extraction. In recent years, various DL models, e.g., convolutional neural VOLUME XX, 2017 1 network (CNN), residual neural network (ResNet), have been applied to intelligent fault diagnosis of bearings and planet gearboxes [5][6][7][8][9][10][11]. For example, Peng et al [6] proposed a novel deep learning method based on onedimensional ResNet for high-speed bearing fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

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Unsupervised Mechanical Fault Feature Learning Based on Consistency Inference-Constrained Sparse Filtering

et al. 2020

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In machinery fault diagnosis, a large amount of monitoring data is often unlabeled, while the number of labeled data is limited. Therefore, learning effective features from massive unlabeled data is a challenging issue for machinery fault diagnosis. In this paper, a simple unsupervised feature learning method, consistency inference-constrained sparse filtering (CICSF), is proposed to learn mechanical fault features with enhanced clustering performance for fault diagnosis. Firstly, inspired by the data augmentation strategy, consistency inference of latent representations for time series (CILRTS) is derived, which infers that training data instances segmented from the same time series should possess consistent latent feature representations. Then, CILRTS is integrated into sparse filtering (SF) as an additional constraint in the latent feature space. The developed CICSF method can optimize the inter-class sparsity and intra-class similarity of the feature distribution simultaneously. Thus, it can learn more effective features from massive unlabeled data. Finally, based on CICSF, a semi-supervised machinery fault diagnosis method is developed. After unsupervised feature learning by CICSF, a softmax regression classifier is trained with limited labeled data to realize machinery fault diagnosis. Experimental results on bearing and gearbox datasets verify the effectiveness of the proposed method. Moreover, comparisons with standard SF and several auto-encoder (AE) variants validate its superiority in unsupervised feature learning and fault diagnosis using limited labeled data. INDEX TERMS Unsupervised feature learning, machinery fault diagnosis, consistent inference of latent representations for time series, sparse filtering, auto-encoder

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Section: B Softmax Regressionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Unsupervised Mechanical Fault Feature Learning Based on Consistency Inference-Constrained Sparse Filtering

et al. 2020

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“…Xu et al developed a stacked denoising autoencoder (SDAE) to extract the feature of the bearing diagnostic signal, and then used the Gath-Geva (GG) clustering algorithm for fault diagnosis [ 24 ]. Zhu et al first proposed the cyclic spectral coherence analysis (CSCoh) method to obtain a CSCoh diagram of the rolling bearing vibration signal, and established a CNN model to learn the features for classification [ 25 ]. Xu et al proposed a bearing fault diagnosis method based on CNN and RF ensemble learning, using multiple hierarchical features extracted by CNN model training, and performing diagnosis through the integration of multiple RF classifiers, achieving high diagnostic accuracy [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Rolling Bearings Based on a Residual Dilated Pyramid Network and Full Convolutional Denoising Autoencoder

Shi

Chen

et al. 2020

Sensors

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Intelligent fault diagnosis algorithm for rolling bearings has received increasing attention. However, in actual industrial environments, most rolling bearings work under severe working conditions of variable speed and strong noise, which makes the performance of many intelligent fault diagnosis methods deteriorate sharply. In this regard, this paper proposes a new intelligent diagnosis algorithm for rolling bearing faults based on a residual dilated pyramid network and full convolutional denoising autoencoder (RDPN-FCDAE). First, a continuous wavelet transform (CWT) is used to convert original vibration signals into time-frequency images. Secondly, a deep two-stage RDPN-FCDAE model is constructed, which is divided into three parts: encoding network, decoding network and classification network. In order to obtain efficient expression of data denoising feature of encoding network, time-frequency images are first input into the encoding-decoding network for unsupervised pre-training. Then pre-trained coding network and classification network are combined into residual dilated pyramid full convolutional network (RDPFCN) for parameter fine-tuning and testing. The proposed method is applied to bearing vibration datasets of test rig with different speeds and noise modes. Compared with representative machine learning and deep learning method, the results show that the algorithm proposed is superior to other methods in diagnostic accuracy, noise robustness and feature segmentation ability.

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“…In recent years, the technology of artificial intelligence has developed rapidly, deep learning algorithm has been widely used in pattern recognition and fault diagnosis [2], such as bearing fault diagnosis [3,4], rotating machinery fault diagnosis [5], and aircraft engine health diagnosis [6]. The fault diagnosis method based on massive historical operation data has gradually become a research hotspot.…”

Section: Introductionmentioning

confidence: 99%

“…where the inputs of the coal mill system model are outlet pressure of primary fan (p f ), hot air temperature (T H ), cold air temperature (T C ), hot air valve opening (u H ), cold air valve opening (u C ) and coal feed flow (w rc ); the outputs of the coal mill system model are inlet air temperature (T in ), inlet air pressure of mill (p in ), inlet air flow (w in ), outlet air pressure of mill (p out ) and outlet temperature of mixture (T out ); intermediate variables of model are raw coal stored in mill (M rc ), coal powder stored in mill (M pc ), grinding current (I b ), coal powder flow (w pc ) and coal powder moisture (θ pc ). α i (i = 1, 2,3,4) …”

mentioning

confidence: 99%

Modeling of Coal Mill System Used for Fault Simulation

Boyu

Zeng

et al. 2020

Energies

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Monitoring and diagnosis of coal mill systems are critical to the security operation of power plants. The traditional data-driven fault diagnosis methods often result in low fault recognition rate or even misjudgment due to the imbalance between fault data samples and normal data samples. In order to obtain massive fault sample data effectively, based on the analysis of primary air system, grinding mechanism and energy conversion process, a dynamic model of the coal mill system which can be used for fault simulation is established. Then, according to the mechanism of various faults, three types of faults (i.e., coal interruption, coal blockage and coal self-ignition) are simulated through the modification of model parameters. The simulation shows that the dynamic characteristic of the model is consistent with the actual object, the relative error of each output variable is less than 2.53%, and the total average relative error of all outputs is about 1.2%. The model has enough accuracy and adaptability for fault simulation, and the problem of massive fault samples acquisition can be effectively solved by the proposed method.

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A deep learning method for bearing fault diagnosis based on Cyclic Spectral Coherence and Convolutional Neural Networks

Cited by 345 publications

References 43 publications

Unsupervised Mechanical Fault Feature Learning Based on Consistency Inference-Constrained Sparse Filtering

Unsupervised Mechanical Fault Feature Learning Based on Consistency Inference-Constrained Sparse Filtering

Fault Diagnosis of Rolling Bearings Based on a Residual Dilated Pyramid Network and Full Convolutional Denoising Autoencoder

Modeling of Coal Mill System Used for Fault Simulation

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