Study on nature of crossover phenomena with application to gearbox fault diagnosis

Jiang, Xingxing; Li, Shunming; Wang, Yong

doi:10.1016/j.ymssp.2016.06.012

Cited by 28 publications

(19 citation statements)

References 47 publications

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“…The vibration signals of gearbox have the characteristics of macro-stability, micro-fluctuation, and overall periodicity in time domain. 28,29 Macroscopically, the vibration signals under the same conditions show the same information in time. Microscopically, the speed fluctuation produced by unbalanced inertial force in the rotating machinery and the phase information of various rotating components make the time-domain signal unstable.…”

Section: Introductionmentioning

confidence: 90%

“…Considering the importance of distribution characteristics, whether SF meets its distribution requirements in the practical application of fault diagnosis is analyzed in detail. 20,29…”

Section: Motivation Of This Papermentioning

confidence: 99%

“…Considering the importance of distribution characteristics, whether SF meets its distribution requirements in the practical application of fault diagnosis is analyzed in detail. 20,29 First, the local features per segment are sparse due to the column normalization of the feature matrix. The average process changes the magnitude, but does not change the distribution of the magnitude.…”

Section: Motivation Of This Papermentioning

confidence: 99%

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Fast convolution sparse filtering and its application on gearbox fault diagnosis

Zhang

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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Transmission, as a critical part of vehicles, is the hub of power transmission and the core of controlling speed change. Condition monitoring and diagnosis of transmissions have become an effective tool to ensure vehicle safety travelling. The intelligent fault diagnosis strategy using artificial intelligent methods has been studied and applied for gearbox fault diagnosis. However, most algorithms cannot guarantee both accuracy and training efficiency. In this paper, fast convolutional sparse filtering based on convolutional activation and feature normalization is proposed for gearbox fault diagnosis without any time-consuming preprocessing. In fast convolutional sparse filtering, the features of samples are optimized instead of local features, which could obviously reduce the dimension and construction time of the Hessian matrix. In addition, the output features are equally active to guarantee that all features have similar contributions. The l2-norm of the training features is recorded and used for pseudo-normalization of the test features. The proposed fast convolutional sparse filtering is validated by a bearing fault dataset and a planetary gear fault dataset. Verification results confirm that fast convolutional sparse filtering is a promising tool for fault diagnosis, which has obviously improved the diagnosis accuracy, training efficiency, and robustness and provides the greater advantage of handling large-scale datasets.

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Section: Introductionmentioning

confidence: 90%

“…Considering the importance of distribution characteristics, whether SF meets its distribution requirements in the practical application of fault diagnosis is analyzed in detail. 20,29…”

Section: Motivation Of This Papermentioning

confidence: 99%

Section: Motivation Of This Papermentioning

confidence: 99%

See 1 more Smart Citation

Fast convolution sparse filtering and its application on gearbox fault diagnosis

Zhang

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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show abstract

“…To further validate the proposed method, a motorcycle gearbox dataset [32] is taken as the second case analysis. Figure 14 displays the gearbox being referred to; besides the gearbox, there are an electrical motor with the rotation speed 1420 rpm, a data acquisition system, a tachometer, a triaxial accelerometer, and a load mechanism.…”

Section: Case 2: Fault Diagnosis Of a Motorcycle Gearboxmentioning

confidence: 99%

A Stacked Autoencoder-Based Deep Neural Network for Achieving Gearbox Fault Diagnosis

Liu

Bao

Han

2018

Mathematical Problems in Engineering

135

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Machinery fault diagnosis is pretty vital in modern manufacturing industry since an early detection can avoid some dangerous situations. Among various diagnosis methods, data-driven approaches are gaining popularity with the widespread development of data analysis techniques. In this research, an effective deep learning method known as stacked autoencoders (SAEs) is proposed to solve gearbox fault diagnosis. The proposed method can directly extract salient features from frequency-domain signals and eliminate the exhausted use of handcrafted features. Furthermore, to reduce the overfitting problem in training process and improve the performance for small training set, dropout technique and ReLU activation function are introduced into SAEs. Two gearbox datasets are employed to conform the effectiveness of the proposed method; the result indicates that the proposed method can not only achieve significant improvement but also is superior to the raw SAEs and some other traditional methods.

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“…Fault diagnosis and pattern identification are crucial to the health monitoring of rotating machinery, especially for the high speed machinery and its components, such as bearing, gear and rotor in the aircraft engine. Vibration signals of rotating components always exhibit the non-linear and non-stationary characteristics due to the degradation and deterioration of working conditions [1]. Therefore, the efficient fault diagnosis method plays a significant role in the health management and condition monitoring of mechanical equipment.…”

Section: Introductionmentioning

confidence: 99%

An intelligent fault diagnosis method of rotating machinery based on deep neural networks and time-frequency analysis

Xin¹,

Li²,

Cheng³

et al. 2018

J. vibroeng.

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As the crucial part of the health management and condition monitoring of mechanical equipment, the fault diagnosis and pattern recognition using vibration signal are essential researching contents. The time-frequency representation method cannot identify the fault patterns from time-frequency representation effectively because of the complex work conditions of rotating machinery parts and the interference of strong background noise. Considering these disadvantages, a new reliable and effective method based on the time-frequency representation and deep convolutional neural networks is presented. In this method, the time-frequency features are calculated by the short time Fourier transform (STFT), and the pseudo-color map as the new identification objects. A novel feature learning method based on the sparse autoencode with linear decode is used to extract these time-frequency features, which is an unsupervised feature learning method with the goal of minimizing the loss function. The convoluting and pooling are applied to establish the hierarchical deep convolutional neural networks and filter the useful features layer by layer from the output of sparse autoencode. And a softmax classifier is used to obtain the faults classification. The experimental datasets from roller bearing and gearbox have been taken to verify the reliability and effectiveness of the proposed method for fault diagnosis and pattern recognition. The results show that the proposed method have excellent performance of the recognized objects.

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Study on nature of crossover phenomena with application to gearbox fault diagnosis

Cited by 28 publications

References 47 publications

Fast convolution sparse filtering and its application on gearbox fault diagnosis

Fast convolution sparse filtering and its application on gearbox fault diagnosis

A Stacked Autoencoder-Based Deep Neural Network for Achieving Gearbox Fault Diagnosis

An intelligent fault diagnosis method of rotating machinery based on deep neural networks and time-frequency analysis

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