Model Parameter Transfer for Gear Fault Diagnosis under Varying Working Conditions

Chen, Chao; Shen, Fei; Xu, Jiawen; Yan, Ruqiang

doi:10.1186/s10033-020-00520-9

Cited by 8 publications

(3 citation statements)

References 19 publications

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“…The sound signal features selected in this paper have been proven to be effective in recognition classification work in different applications. Among them, the short-time energy, zero crossing rate, and 16-dimensional time-frequency features are all time-domain features of sound signals [ 18 ]; the spectral center of mass, spectral spread, and roll-off coefficient can effectively respond to the energy distribution features of sound signals [ 19 ]; the MFCC and GFCC are two common sound features widely used in the field of speech recognition [ 20 , 21 , 22 ], and short-time Fourier coefficients can effectively respond to the frequency domain features [ 23 ].…”

Section: Feature Extractionmentioning

confidence: 99%

Sound Identification Method for Gas and Coal Dust Explosions Based on MLP

2023

Entropy

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To solve the problems of backward gas and coal dust explosion alarm technology and single monitoring means in coal mines, and to improve the accuracy of gas and coal dust explosion identification in coal mines, a sound identification method for gas and coal dust explosions based on MLP in coal mines is proposed, and the distributions of the mean value of the short-time energy, zero crossing rate, spectral centroid, spectral spread, roll-off, 16-dimensional time-frequency features, MFCC, GFCC, short-time Fourier coefficients of gas explosion sound, coal dust sound, and other underground sounds were analyzed. In order to select the most suitable feature vector to characterize the sound signal, the best feature extraction model of the Relief algorithm was established, and the cross-entropy distribution of the MLP model trained with the different numbers of feature values was analyzed. In order to further optimize the feature value selection, the recognition results of the recognition models trained with the different numbers of sound feature values were compared, and the first 35-dimensional feature values were finally determined as the feature vector to characterize the sound signal. The feature vectors are input into the MLP to establish the sound recognition model of coal mine gas and coal dust explosion. An analysis of the feature extraction, optimal feature extraction, model training, and time consumption for model recognition during the model establishment process shows that the proposed algorithm has high computational efficiency and meets the requirement of the real-time coal mine safety monitoring and alarm system. From the results of recognition experiments, the sound recognition algorithm can distinguish each kind of sound involved in the experiments more accurately. The average recognition rate, recall rate, and accuracy rate of the model can reach 95%, 95%, and 95.8%, respectively, which is obviously better than the comparison algorithm and can meet the requirements of coal mine gas and coal dust explosion sensing and alarming.

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Section: Feature Extractionmentioning

confidence: 99%

Sound Identification Method for Gas and Coal Dust Explosions Based on MLP

2023

Entropy

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“…Therefore, it is of great significance to accurately diagnose the health state of bearings [1]. Rolling bearings usually work under different loads, and the specifications of bearings in different mechanical equipment may be different, which makes bearing fault diagnosis more difficult [2]. In practical engineering, available bearing data with sufficient health label information are scarce, which makes bearing fault diagnosis more and more challenging [3].…”

Section: Introductionmentioning

confidence: 99%

Few-shot rolling bearing fault classification method based on improved relation network

Kang¹,

Liang²,

Wang³

et al. 2022

Meas. Sci. Technol.

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In practical applications, it is difficult to obtain enough fault samples to train the fault classification model of rolling bearings, and the specifications of bearings used in different mechanical equipment may be different. The diagnosis model trained on a certain specification of bearings may not be applicable to other specification of bearings. To solve the above problems, a few-shot rolling bearing fault classification method based on improved relation network is proposed. Firstly, Fourier transform is applied to vibration signals of different specifications of bearings. The data of different specifications of bearings are divided into meta-train set and meta-test set according to the meta-learning training strategy, and each set is further divided into support set and query set. Secondly, an improved relation network is built. The residual shrinkage module and the SELU activation function are introduced into the embedding module of the relation network. The improved embedding module is used to extract the sample features of the support set and query set, and then the features of the two are combined and input into the relation module to get the relation score. The query set samples are classified according to the score. Finally, the rolling bearing fault classification model is obtained after multiple episodes. The experimental results show that, compared with the partial transfer learning and meta-learning methods, the proposed method only needs a few or even a single sample to achieve the fault classification of different specifications of rolling bearings under different loads. In the case of one-shot, the average classification accuracy can reach 93.3%.

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“…Zhang et al combined deep CNN with TL to reduce the dependence of the model on training data and accurately identify different fault types [ 24 ]. Chen et al combined multitask learning with TL and proposed a novel model parameter transfer (NMPT) to improve the performance of gear fault diagnosis (GFD) under different operating conditions [ 25 ]. Qian et al proposed a new method for evaluating distribution differences, which is called auto-balancing higher-order Kullback–Leibler (AHKL) divergence; minimizing the difference in domain distribution can be achieved by this method [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Machinery Fault Diagnosis Method Based on GAN and Transfer Learning under Variable Working Conditions

Chen

Zhou

et al. 2022

Sensors

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Intelligent fault diagnosis is of great significance to guarantee the safe operation of mechanical equipment. However, the widely used diagnosis models rely on sufficient independent and homogeneously distributed monitoring data to train the model. In practice, the available data of mechanical equipment faults are insufficient and the data distribution varies greatly under different working conditions, which leads to the low accuracy of the trained diagnostic model and restricts it, making it difficult to apply to other working conditions. To address these problems, a novel fault diagnosis method combining a generative adversarial network and transfer learning is proposed in this paper. Dummy samples with similar fault characteristics to the actual engineering monitoring data are generated by the generative adversarial network to expand the dataset. The transfer fault characteristics of monitoring data under different working conditions are extracted by a deep residual network. Domain-adapted regular term constraints are formulated to the training process of the deep residual network to form a deep transfer fault diagnosis model. The bearing fault data are used as the original dataset to validate the effectiveness of the proposed method. The experimental results show that the proposed method can reduce the influence of insufficient original monitoring data and enable the migration of fault diagnosis knowledge under different working conditions.

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Model Parameter Transfer for Gear Fault Diagnosis under Varying Working Conditions

Cited by 8 publications

References 19 publications

Sound Identification Method for Gas and Coal Dust Explosions Based on MLP

Sound Identification Method for Gas and Coal Dust Explosions Based on MLP

Few-shot rolling bearing fault classification method based on improved relation network

An Intelligent Machinery Fault Diagnosis Method Based on GAN and Transfer Learning under Variable Working Conditions

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