Fault diagnosis based on comprehensive geometric characteristic and probability neural network

Chen, Xiaoyue; Zhou, Jianzhong; Xiao, Han; Wang, Ercheng; Xiao, Jian; Zhang, Huifeng

doi:10.1016/j.amc.2013.12.122

Cited by 18 publications

(13 citation statements)

References 27 publications

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“…e corresponding relationship between the faults and the graphs is shown in Table 2 [16,29]. In addition, petal-shaped shaft orbit corresponds to Oil whirl [29], which increases the type of shaft orbit and increases the difficulty of identification compared to many researches [1][2][3][13][14][15][16][17][18][19]. Although both banana-shaped and outer "8" shaft orbit correspond to misalignment faults, they correspond to different severity of misalignment faults [30].…”

Section: Experiments and Analysis On Simulation Datamentioning

confidence: 99%

“…Rotating machines play a critical role in industrial production [1]. Once failure occurs, it may cause the enterprise to suffer huge economic losses, and even catastrophic accidents.…”

Section: Introductionmentioning

confidence: 99%

“…Identification methods of shaft orbits mainly include two fundamental procedures: feature extraction and classification. In the first procedure, the quality of the extracted features could directly determine the accuracy of the identification on shaft orbits [1]. Many scholars have proposed many efficient methods for feature extraction of shaft orbits, such as Fourier descriptors (FD) [13], chain code [14,15], Walsh descriptor (WD) [16], seven improved invariant moment [17], histogram of oriented gradients (HOG) [18], fractal theory [19], and comprehensive geometric characteristic (CGC) [1].…”

Section: Introductionmentioning

confidence: 99%

“…Although the dimension of the feature extracted by the fractal theory is small, the recognition rate is not high enough. Chen et al proposed a method of comprehensive geometric characteristics [1], which extracted features of shaft orbits from three different aspects: structure, region, and boundary. Although it can achieve satisfactory accuracy, its real-time performance is not good enough, extraction time of which is longer than chain code and Walsh descriptor.…”

Section: Introductionmentioning

confidence: 99%

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Identification Method of Shaft Orbit in Rotating Machines Based on Accurate Fourier Height Functions Descriptors

Feng

Sun

et al. 2018

Shock and Vibration

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In this paper, an algorithm based on two novel shape descriptors and support vector machine (SVM) is proposed to improve the recognition accuracy and speed of shaft orbits of rotating machines. Firstly, two novel shape descriptors, respectively, named accurate Fourier height functions 1 (AFHF1) and accurate Fourier height functions 2 (AFHF2) are presented based on height function (HF) and Fourier transformation. Both AFHF1 and AFHF2 shape descriptors are constant to similarity transforms and also have intrinsic invariance to the starting point change and are more compacted than HF. Therefore, they perform well on the global or local features of the contours of shaft orbits. Then, the AFHF1 and AFHF2 shape descriptors are utilized to extract features of shaft orbits in the simulated dataset and measured dataset. Taking extracted feature vectors as the input, SVM is adopted in order to classify the fault types according to the shapes of shaft orbits. Finally, a series of descriptors including shape context (SC), inner-distance shape context (IDSC), triangular centroid distances (TCDs), and HF were compared to verify the performance of the proposed AFHF1 and AFHF2 shape descriptors. The average accuracy of our method in simulated dataset and measured dataset are all higher than 99.83%, the average recognition time of each sample is no more than 19 milliseconds. The experiments demonstrate that the proposed method has the best recognition accuracy and real-time and antinoise performance.

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Section: Experiments and Analysis On Simulation Datamentioning

confidence: 99%

“…Rotating machines play a critical role in industrial production [1]. Once failure occurs, it may cause the enterprise to suffer huge economic losses, and even catastrophic accidents.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Identification Method of Shaft Orbit in Rotating Machines Based on Accurate Fourier Height Functions Descriptors

Feng

Sun

et al. 2018

Shock and Vibration

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show abstract

“…Due to the severe working environment, the rotating machinery is inevitably subject to various faults, which will lead to serious accidents and casualties [1]. Therefore, condition monitoring and fault diagnosis for these key rotating machinery are important for increasing production efficiency, decreasing maintenance charges, and extending service life of the equipment [2,3].…”

Section: Introductionmentioning

confidence: 99%

Fine-grained fault recognition method for shaft orbit of rotary machine based on convolutional neural network

Wu¹,

Feng²,

Sun³

et al. 2019

J. vibroeng.

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In the fault diagnosis of the shaft orbit of rotating machinery, there are few prejudgments about the severity of the faults, which is very important for fault repair. Therefore, a fine-grained recognition method is proposed to detect different severity faults by shaft orbit. Since different shaft orbits represent different type and different severity of faults, the convolutional neural network (CNN) is applied for identifying the shaft orbits to recognize the type and severity of the fault. The recognition rate of proposed fine-grained fault identification method is 97.96 % on the simulated shaft orbit database, and it takes only 0.31 milliseconds for the recognition of single sample. Experimental result indicated that the classification performance of the proposed method are better than the traditional machine learning models. Moreover, the method is applied for the identification of the measured shaft orbits of rotor with different degree of imbalance faults, and the testing accuracy of the experiments in measured shaft orbits is 97.14 %, which has verified the effectiveness of the proposed fine-grained fault recognition method.

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Fault detection for discrete-time linear systems based on descriptor observer approach

2017

Applied Mathematics and Computation

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Fault diagnosis based on comprehensive geometric characteristic and probability neural network

Cited by 18 publications

References 27 publications

Identification Method of Shaft Orbit in Rotating Machines Based on Accurate Fourier Height Functions Descriptors

Identification Method of Shaft Orbit in Rotating Machines Based on Accurate Fourier Height Functions Descriptors

Fine-grained fault recognition method for shaft orbit of rotary machine based on convolutional neural network

Fault detection for discrete-time linear systems based on descriptor observer approach

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