A novel intelligent fault diagnosis method of rolling bearing based on two-stream feature fusion convolutional neural network

Feng, Xue; Zhang, Wei Min; Xue, Fei; Li, Dongdong; Xie, Shulian; Fleischer, Jürgen

doi:10.1016/j.measurement.2021.109226

Cited by 82 publications

(35 citation statements)

References 39 publications

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“…A two-dimensional CNN is used to extract spatial information for every one day matrix. Spatial information refers to data having location-based relation with other data [15]. Remember that in the previous Section 3.5 images composed of 144 measurements corresponding to one day are constructed, maintaining their temporal order.…”

Section: Hybrid Neural Network (Cnn + Lstm)mentioning

confidence: 99%

Wind Turbine Main Bearing Failure Prediction using a Hybrid Neural Network

Bermúdez

Ortiz-Holguin

Tutivén

et al. 2022

J. Phys.: Conf. Ser.

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Energy is necessary for economic growth and improved well-being, but it poses a great challenge to be generated without increasing costs and avoiding pollution. A viable option is wind energy because it is a clean and renewable. However, continuous monitoring and maintenance of wind turbines is required for the further development of wind farms. Main bearing failures were identified by the European Academy of Wind Energy as a critical issue in terms of increasing the availability and reliability of wind turbines. In this work, it is proposed a hybrid neural network for main bearing failure prognosis. This network consists of a two-dimensional convolutional neural network (to extract spatial-temporal characteristics from the data) sequentially connected with a long short-term memory network (to learn sequence patterns) to predict the slow-speed shaft temperature (the closest temperature to the main bearing). The mean square error between its real measurement and its prediction gives a failure indicator. When it is greater than a defined threshold, then an alarm is triggered and gives the maintenance staff time to check the component. The advantage of this strategy is that it does not need faulty data to be trained, since it is based on a normality model, that is, it is trained with a single class of data (healthy) and does not require incurring high costs per acquisition of new sensors since SCADA data is used (which comes in all industrial size turbine). The results show that the use of a hybrid network can identify failures around four months before a fatal failure occurs.

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Section: Hybrid Neural Network (Cnn + Lstm)mentioning

confidence: 99%

Wind Turbine Main Bearing Failure Prediction using a Hybrid Neural Network

Bermúdez

Ortiz-Holguin

Tutivén

et al. 2022

J. Phys.: Conf. Ser.

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“…Besides this, Bai et al [20] proposed a diagnosis strategy based on multichannel CNN combining multiscale clipping fusion data augmentation technique. Xue et al [21] established a two-stream feature fusion CNN model for rolling bearing fault diagnosis. In the aspect of variable condition diagnosis, many scholars have put forward methods of resolution.…”

Section: Introductionmentioning

confidence: 99%

A Novel 1D-CNN-Based Diagnosis Method for a Rolling Bearing with Dual-Sensor Vibration Data Fusion

Liao

Zhang

et al. 2022

Mathematical Problems in Engineering

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Due to variation of working conditions and influence of noise in vibration data, rolling bearing intelligent diagnosis based on deep learning faces challenges in efficient utilization of monitoring data and scientific extraction of fault features. This study proposes a one-dimensional convolution neural network (1D-CNN)-based intelligent diagnosis method for a rolling bearing, which fuses the horizontal and the vertical vibration signals, makes full use of spectral order features by full-spectrum analysis, and achieves accurate classification of fault pattern by 1D-CNN model. The experimental datasets of constant and variable working conditions of rolling bearing are constructed. The test results of the proposed method show that spectral order features are extracted effectively by full-spectrum analysis and high diagnostic accuracy is obtained by the constructed 1D-CNN model on both datasets. The comparison with the other four similar methods indicates that the diagnostic accuracy of the proposed method outperforms the comparative methods significantly in the case of variable operating conditions.

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“…In view of the fault diagnosis of combine harvesters [15][16][17], experts and scholars adopted SDAE [18,19], random forest [20][21][22], and SVM [23,24] to carry out the analysis and fault diagnosis of the combine harvester by obtaining the monitoring data of combine harvesters. Due to the complex structure of the equipment and the large number of parts to be diagnosed [25][26][27], the fusion of multisensor signal features can increase the diversity and integrity of fault information [28,29].…”

Section: Introductionmentioning

confidence: 99%

Research on Comprehensive Operation and Maintenance Based on the Fault Diagnosis System of Combine Harvester

et al. 2022

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In view of the difficulty in diagnosing and discriminating fault conditions during the operation of combine harvesters, difficulty in real-time processing of health status, and low timeliness of fault processing, a comprehensive operation and maintenance platform for combine harvesters was developed in this study which realized the functions of data monitoring and the full operation and maintenance of a combine harvester. At the same time, through the comprehensive operation and maintenance platform, the harvester information was obtained in real-time, the diagnosis results were obtained, and the maintenance service was effectively carried out through the platform. The IPSO-SVM fault diagnosis algorithm was proposed, and the performance of the fault diagnosis of the combine harvester was verified by the simulation test. The experimental verification showed that the system met the requirements of remote monitoring of combine harvesters, and the prediction accuracy of this method was 97.96%. Compared with SVM (87.51%), GA-SVM (89.44%), and PSO-SVM (92.56%), this system had better generalization ability and effectively improved the management level of the comprehensive operation and maintenance of the combine harvester. A theoretical basis and technical reference will be provided for the follow-up research for the comprehensive operation and maintenance platform of the combine harvester in this paper.

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A novel intelligent fault diagnosis method of rolling bearing based on two-stream feature fusion convolutional neural network

Cited by 82 publications

References 39 publications

Wind Turbine Main Bearing Failure Prediction using a Hybrid Neural Network

Wind Turbine Main Bearing Failure Prediction using a Hybrid Neural Network

A Novel 1D-CNN-Based Diagnosis Method for a Rolling Bearing with Dual-Sensor Vibration Data Fusion

Research on Comprehensive Operation and Maintenance Based on the Fault Diagnosis System of Combine Harvester

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