Deep convolutional neural network based planet bearing fault classification

Zhao, Dongfeng; Wang, Tianyang; Chu, Fulei

doi:10.1016/j.compind.2019.02.001

Cited by 168 publications

(69 citation statements)

References 26 publications

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“…The vibration detection method can be used to monitor the operating state of equipment without stopping the machine, and to achieve early diagnosis and accurate fault location, which is widely used in the field of fault diagnosis and prediction of mechanical transmission system [3]. In recent decades, the research on fault diagnosis of the mechanical transmission system at home and abroad mainly focuses on evaluating the fault based on vibration intensity and vibration standard, and denoising of vibration signals, and multi-class fault intelligent recognition based on artificial neural network, fuzzy theory or support vector machine [4][5][6][7]. While, for a complex mechanical transmission system with multiple coincident fault characteristic frequencies, few researchers study on fault location of specific components, especially in the aspect of shearer ranging arm.…”

Section: Figure 1 the Faults Of Gearof The Shearer Ranging Armmentioning

confidence: 99%

“…The rotation frequency of fault location is got by the contrast analysis of the envelope demodulation spectrum in normal and fault state. (7)Because different gears have different rotation frequencies, the comprehensive analysis results of optimized continuous complex Morlet wavelet envelope demodulation spectrum and spectrum can uniquely determine the fault location of gears.…”

Section: A Accurate Fault Location Scheme Of the Shearer Ranging Armmentioning

confidence: 99%

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Accurate Fault Location Method of the Mechanical Transmission System of Shearer Ranging Arm

Mao

Zhang

et al. 2020

IEEE Access

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In the process of coal mine production, the fault of the mechanical transmission system of the shearer ranging arm directly affects safety production. Therefore, it is very important to achieve the accurate fault location of the shearer ranging arm. The combination method of optimized continuous complex Morlet wavelet envelope demodulation spectrum analysis and spectrum analysis is proposed. Firstly, the spectrum contrast analysis of vibration signals between the normal state and fault state is performed, which can obtain the fault characteristics frequency and preliminary fault location. Then, the optimized continuous complex Morlet wavelet envelope demodulation algorithm is proposed to extract the side-bands characteristics of vibration signals, which can obtain the rotation frequency of fault location by the contrast analysis of side-bands characteristics in normal and fault state. In the algorithm, the center frequency and bandwidth of Morlet wavelet are optimized by wavelet Shannon entropy, and the optimal scale factor can be accurately determined by spectrum contrast analysis of vibration signals in normal and fault state. Finally, the accurate gear fault location is achieved by comprehensive analysis results of optimized continuous complex Morlet wavelet envelope demodulation spectrum and spectrum contrast. The simulation signals and practical vibration signals are applied to verify the proposed accurate gear fault location method. The experiment results indicate that the center frequency and bandwidth determined by wavelet Shannon entropy make the wavelet waveform best match the fault impact signal, and the optimal scale factor can be accurately determined by spectrum contrast analysis of vibration signals in normal and fault state, and the proposed method can achieve the precise fault location of the mechanical transmission system of shearer ranging arm. It provides the method for the on-site rapid accurate fault location of the mechanical transmission system. It is of great significance to the safe and efficient production of the coal mine.

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Section: Figure 1 the Faults Of Gearof The Shearer Ranging Armmentioning

confidence: 99%

Section: A Accurate Fault Location Scheme Of the Shearer Ranging Armmentioning

confidence: 99%

Accurate Fault Location Method of the Mechanical Transmission System of Shearer Ranging Arm

Mao

Zhang

et al. 2020

IEEE Access

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“…Li et al [ 26 ] constructed a novel ST-CNN method for fault diagnosis of bearings by fusing S-transform (ST) and CNN, in which a ST layer converted sensor data into a two-dimensional time-frequency matrix and the following CNN model performed diagnosis results. Zhao et al [ 27 ] calculated the envelope time-frequency representations of the vibration signal using Hilbert transform and synchro-squeezing transform and then built a deep CNN to learn the underlying features and determine the fault types automatically. Zhu et al [ 28 ] presented a new bearing remaining useful life estimation method through time-frequency representation and multiscale CNN, in which the time-series signals revealed the nonstationary properties using wavelet transform.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we proposed a novel end-to-end bearing fault diagnosis method based on WPT and CNN, named WPT-CNN. Unlike other diagnostic methods that treat the time-frequency analysis method as an independent module [ 26 , 27 , 28 ], the WPT-CCN encapsulates time-frequency decomposition and feature classification in a single network by implementing the function of WPT in the form of a modified convolutional neural layer embedded in the overall structure. WPT not only inherits the merits of WT, in that it has a good time resolution in high frequency bands and a good frequency resolution in low frequency bands, but also compensates for the shortage of WT that lacks the capacity of further decomposing the frequency components in higher frequency bands, leading to better information refinement.…”

Section: Introductionmentioning

confidence: 99%

A Novel End-To-End Fault Diagnosis Approach for Rolling Bearings by Integrating Wavelet Packet Transform into Convolutional Neural Network Structures

Xiong

Zhou

et al. 2020

Sensors

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Accidental failures of rotating machinery components such as rolling bearings may trigger the sudden breakdown of the whole manufacturing system, thus, fault diagnosis is vital in industry to avoid these massive economical costs and casualties. Since convolutional neural networks (CNN) are poor in extracting reliable features from original signal data, the time-frequency analysis method is usually called for to transform 1D signal into a 2D time-frequency coefficient matrix in which richer information could be exposed more easily. However, realistic fault diagnosis applications face a dilemma in that signal time-frequency analysis and fault classification cannot be implemented together, which means manual signal conversion work is also needed, which reduces the integrity and robustness of the fault diagnosis method. In this paper, a novel network named WPT-CNN is proposed for end-to-end intelligent fault diagnosis of rolling bearings. WPT-CNN creatively uses the standard deep neural network structure to realize the wavelet packet transform (WPT) time-frequency analysis function, which seamlessly integrates fault diagnosis domain knowledge into deep learning algorithms. The overall network architecture can be trained with gradient descent backpropagation algorithms, indicating that the time-frequency analysis module of WPT-CNN is also able to learn the dataset characteristics, adaptively representing signal information in the most suitable way. Two experimental rolling bearing fault datasets were used to validate the proposed method. Testing results showed that WPT-CNN obtained the testing accuracies of 99.73% and 99.89%, respectively, in two datasets, which exhibited a better and more reliable diagnosis performance than any other existing deep learning and machine learning methods.

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“…Once a fault occurs, it will lead to unplanned downtime, huge economic losses, and even serious disaster consequences [16][17][18][19]. Therefore, it is very important to monitor the health of the gearbox and find out the faults as early as possible so as to plan the shutdown and maintenance properly, so as to ensure the safe operation of the mechanical equipment, improve the production efficiency, and increase the economic benefits [20,21].…”

Section: Introductionmentioning

confidence: 99%

A Diagnosis Method for the Compound Fault of Gearboxes Based on Multi-Feature and BP-AdaBoost

Zhang

Jia

et al. 2020

Symmetry

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Gearbox is an important structure of rotating machinery, and the accurate fault diagnosis of gearboxes is of great significance for ensuring efficient and safe operation of rotating machinery. Aiming at the problem that there is little common compound fault data of gearboxes, and there is a lack of an effective diagnosis method, a gearbox fault simulation experiment platform is set up, and a diagnosis method for the compound fault of gearboxes based on multi-feature and BP-AdaBoost is proposed. Firstly, the vibration signals of six typical states of gearbox are obtained, and the original signals are decomposed by empirical mode decomposition and reconstruct the new signal to achieve the purpose of noise reduction. Then, perform the time domain analysis and wavelet packet analysis on the reconstructed signal, extract three time domain feature parameters with higher sensitivity, and combine them with eight frequency band energy feature parameters obtained by wavelet packet decomposition to form the gearbox state feature vector. Finally, AdaBoost algorithm and BP neural network are used to build the BP-AdaBoost strong classifier model, and feature vectors are input into the model for training and verification. The results show that the proposed method can effectively identify the gearbox failure modes, and has higher accuracy than the traditional fault diagnosis methods, and has certain reference significance and engineering application value.

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Deep convolutional neural network based planet bearing fault classification

Cited by 168 publications

References 26 publications

Accurate Fault Location Method of the Mechanical Transmission System of Shearer Ranging Arm

Accurate Fault Location Method of the Mechanical Transmission System of Shearer Ranging Arm

A Novel End-To-End Fault Diagnosis Approach for Rolling Bearings by Integrating Wavelet Packet Transform into Convolutional Neural Network Structures

A Diagnosis Method for the Compound Fault of Gearboxes Based on Multi-Feature and BP-AdaBoost

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