Planetary-Gearbox Fault Classification by Convolutional Neural Network and Recurrence Plot

Wang, Danfeng; Guo, Yu; Wu, Xing; Na, Jing; Litak, Grzegorz

doi:10.3390/app10030932

Cited by 31 publications

(14 citation statements)

References 20 publications

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“…Intelligent diagnosis methods integrate signal acquisition, feature extraction and final classification, and present remarkable advantage in accomplishing machinery fault classification [ 54 , 55 ]. The methods based on deep network models request special data input with regard to the 2D input and the amount of training datasets [ 56 , 57 ].…”

Section: Proposed Intelligent Fault-diagnosis Methodsmentioning

confidence: 99%

Intelligent Diagnosis towards Hydraulic Axial Piston Pump Using a Novel Integrated CNN Model

Tang

Zhu

Yuan

et al. 2020

Sensors

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As a critical part of a hydraulic transmission system, a hydraulic axial piston pump plays an indispensable role in many significant industrial fields. Owing to the practical undesirable working environment and hidden faults, it is challenging to precisely and effectively detect and diagnose the varying fault in the engineering. Deep learning-based technology presents special strengths in processing mechanical big data. It can simultaneously complete the feature extraction and classification, and achieve the automatic information learning. The popular convolutional neural network (CNN) is exploited for its potent ability of image processing. In this paper, a novel combined intelligent method is developed for fault diagnosis towards a hydraulic axial piston pump. First, the conversion of signals to images is conducted via continuous wavelet transform; the effective feature is preliminarily extracted from the transformed time-frequency images. Second, a novel deep CNN model is constructed to achieve the fault classification. To disclose the potential learning in the disparate layers of the CNN model, the visualization of reduced features is performed by employing t-distributed stochastic neighbor embedding. The effectiveness and stability of the proposed model are validated through the experiments. With the proposed method, different fault types can be precisely identified and high classification accuracy is achieved in a hydraulic axial piston pump.

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Section: Proposed Intelligent Fault-diagnosis Methodsmentioning

confidence: 99%

Intelligent Diagnosis towards Hydraulic Axial Piston Pump Using a Novel Integrated CNN Model

Tang

Zhu

Yuan

et al. 2020

Sensors

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“…With an integration of alternative signal acquisition, feature extraction and fault classification in traditional fault diagnosis, intelligent fault diagnosis presents the potential in developing novel fault classification techniques [39,40,41]. However, in view of training process of the designed model, sufficient data input should be requested; especially, image input can be satisfied for such methods as 2D CNN based methods [42,43]. Therefore, in order to achieve the increase of data and the transformation of the demanded 2D image, it is still essential to perform data preprocessing before the following network learning.…”

Section: Data Preprocessing Approaches In Cnn-based Fault Diagnosismentioning

confidence: 99%

Data Preprocessing Techniques in Convolutional Neural Network Based on Fault Diagnosis Towards Rotating Machinery

2020

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Rotating machinery plays a critical role in many significant fields. However, the unpredictable machinery faults may lead to the severe damage and losses. Hence, it is of great value to explore the precise approaches for fault diagnosis. With the development of the intelligent fault diagnosis methods based on deep learning, convolutional neural network (CNN) has aroused the attention of researchers in machinery fault diagnosis. In the light of the reduction of difficulty in feature learning and the improvement of final diagnosis accuracy, data preprocessing is necessary and crucial in CNN-based fault diagnosis methods. This review focuses on CNN-based fault diagnosis approaches in rotating machinery. Firstly, data preprocessing methods are overviewed. Then, we emphatically analyze and discuss several main techniques applied in CNN-based intelligent diagnosis, principally including the fast Fourier transform, wavelet transform, data augmentation, S-transform, and cyclic spectral analysis. Finally, the potential challenges and research objects are prospected on data preprocessing in intelligent fault diagnosis of rotary machinery. INDEX TERMS Data preprocessing, convolutional neural network, intelligent fault diagnosis, rotating machinery.

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“…where θ 0 represents the initial position of the rotor, f e0 represents the fundamental frequency of the current, p represents the number of the motor poles, and θ tz represents the modulation component of the angular velocity. Formula (8) can be obtained by combining formulas (4), (6) and 7:…”

Section: Mathematical Model Of Fault Currentmentioning

confidence: 99%

“…The traditional fault diagnosis method mainly uses the vibration signal of mechanical devices [3][4][5][6]. This diagnosis method is sensitive to the change of the sensors' installation position and the external environment.…”

Section: Introductionmentioning

confidence: 99%

Study on the Fault Diagnosis Method of Scraper Conveyor Gear under Time-Varying Load Condition

Zhao

Wang

2020

Applied Sciences

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Vibration signal is often used in traditional gear fault diagnosis techniques. However, the working face of the scraper conveyor is narrow, harsh and easily explosive, so it is inconvenient to obtain vibration signals by installing sensors. Motor current signature analysis (MCSA) is a fault-diagnosis method without sensor installation, which is easier to realize in the mine. Therefore, a fault diagnosis method for local gear fault, which is based on bispectral analysis (BA) of analytical signal envelope obtained by processing a stator current under time-varying load condition, is proposed in our paper. In this method, the fault frequency component is enhanced by eliminating the interference of fundamental frequency and coal flow impact. Then, the enhanced fault frequency component is extracted by BA, and a quantitative analysis of the fault strength under time-varying load is carried out from the perspective of energy. Finally, the proposed method is verified on the number HB-kpl-75 scraper conveyor reducer, and the results show that this method can successfully diagnose the failure of the scraper conveyor gear under time-varying load conditions.

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Planetary-Gearbox Fault Classification by Convolutional Neural Network and Recurrence Plot

Cited by 31 publications

References 20 publications

Intelligent Diagnosis towards Hydraulic Axial Piston Pump Using a Novel Integrated CNN Model

Intelligent Diagnosis towards Hydraulic Axial Piston Pump Using a Novel Integrated CNN Model

Data Preprocessing Techniques in Convolutional Neural Network Based on Fault Diagnosis Towards Rotating Machinery

Study on the Fault Diagnosis Method of Scraper Conveyor Gear under Time-Varying Load Condition

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