A novel procedure for diagnosing multiple faults in rotating machinery

Wang, Zhijian; Han, Zhiyu; Gu, Fengshou; Gu, Jiahui; Ning, Shaohui

doi:10.1016/j.isatra.2014.09.006

Cited by 50 publications

(28 citation statements)

References 21 publications

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“…In order to reduce the amount of calculation, the optimization range is set for the parameters k and α that need to be optimized. The optimization range of k is set to [2,8], and the optimization range of α is set to [100,5000]. The dimension of permutation entropy is empirical value 6, and the optimization process is shown in Figure 11.…”

Section: Comparison Of the Proposed Methods With Eemd And Ssdmentioning

confidence: 99%

“…(9) Iterate from step (2) to step (8) to update the optimal solution until the maximum number of iterations.…”

Section: Immune Fruit Fly Optimization Algorithmmentioning

confidence: 99%

“…So, composite fault extraction of the gearbox is critical [4][5][6]. At present, the methods used in gearbox fault extraction include Wavelet transform (WT), Empirical mode decomposition (EMD), Local mean decomposition (LMD), and Ensemble empirical mode decomposition (EEMD), these methods successfully extract the fault information, but they will show their own weaknesses when extracting composite faults [7][8][9][10]. When using WT to decompose signals, we need to give a basis function and the number of decomposing layers ahead of time, that is, WT is not adaptive [11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

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Research on Fault Extraction Method of Variational Mode Decomposition Based on Immunized Fruit Fly Optimization Algorithm

Zhou

Guo

Wang

et al. 2019

Entropy

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In recent years, a new method of fault diagnosis, named variational mode decomposition (VMD), has been widely used in industrial production, but the decomposition accuracy of VMD is determined by two parameters, which are respectively the decomposition layer number k and the penalty factor α, if the parameters are not properly selected, there will be over-decomposition or under-decomposition. In order to find an approach to determine the parameters adaptively, a method to optimize VMD by using the immune fruit fly optimization algorithm (IFOA) is proposed in this paper. In this method, permutation entropy is used as the fitness function, firstly, the immune fruit fly optimization algorithm is used to search the combined parameters of k and α in VMD, searching for the best combination parameters of k and α by iteration, and then uses the combined parameters to perform VMD, finally, the center frequency is determined through frequency spectrum analysis. The method mentioned is applied to the fault extraction of a simulated signal and a measured signal of a wind turbine gearbox, and the fault frequency is successfully extracted. Using ensemble empirical mode decomposition (EEMD) and singular spectrum decomposition (SSD) to compare with the proposed method, which validated feasibility of the proposed method.

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Section: Comparison Of the Proposed Methods With Eemd And Ssdmentioning

confidence: 99%

“…(9) Iterate from step (2) to step (8) to update the optimal solution until the maximum number of iterations.…”

Section: Immune Fruit Fly Optimization Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Research on Fault Extraction Method of Variational Mode Decomposition Based on Immunized Fruit Fly Optimization Algorithm

Zhou

Guo

Wang

et al. 2019

Entropy

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“…Figure 2 reports the instantaneous statistical parameters of the database. High-order statistics are also illustrated and can be used as a reference value for adaptive threshold estimation performance [5] (figure 2). At each sample time, we can determine the mean of the variable…”

Section: Ic4m 2016mentioning

confidence: 99%

Varying Load Detection in A Gearbox System Based on Adaptive Threshold Estimation

Kouadri

Ibrahim

Albarbar

2016

MATEC Web of Conferences

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Abstract. This article presents an adaptive threshold computation for varying load detection in gearbox system. This method was applied on the vibration signal which is captured from gearbox housing. The experimental work was carried out using a three-stage gearbox connected with a motor and a generator. The developed adaptive threshold is obtained through several repeated experiments in the healthy mode and under the same operating conditions. An appropriate statistical test is used to examine the validity of the adaptive threshold estimation approach. Besides and for all experiments, a confidence interval is obtained which is closely linked to the distribution frequencies of the gearbox vibration signal as a random variable. In addition, several significance levels are considered to show the performances of the proposed adaptive thresholding technique compared to the limitations of the fixed threshold through the rate of false detection alarms. It is demonstrated from various experimental varying load mode of a gearbox system the effectiveness and accuracy of the adaptive threshold in term of evaluating gearbox operating conditions.

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“…As non-stationary signals, bearing fault signals are extensively dealt with using time-frequency analysis to obtain local characteristic information both in time and frequency domain [15][16][17]. Two or more kinds of signal processing techniques are also combined together for feature extraction [18][19][20]. Some signal analysis methods have been optimized before performing feature extraction [21,22] like flexible analytic wavelet transform [23] by employing fractional and arbitrary scaling and translation factors to match fault component.…”

Section: Introductionmentioning

confidence: 99%

Bearing fault diagnosis via kernel matrix construction based support vector machine

Wu¹,

Chen²,

Jiang³

2017

J. vibroeng.

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A novel approach on kernel matrix construction for support vector machine (SVM) is proposed to detect rolling element bearing fault efficiently. First, multi-scale coefficient matrix is achieved by processing vibration sample signal with continuous wavelet transform (CWT). Next, singular value decomposition (SVD) is applied to calculate eigenvector from wavelet coefficient matrix as sample signal feature vector. Two kernel matrices i.e. training kernel and predicting kernel, are then constructed in a novel way, which can reveal intrinsic similarity among samples and make it feasible to solve nonlinear classification problems in a high dimensional feature space. To validate its diagnosis performance, kernel matrix construction based SVM (KMCSVM) classifier is compared with three SVM classifiers i.e. classification tree kernel based SVM (CTKSVM), linear kernel based SVM (L-SVM) and radial basis function based SVM (RBFSVM), to identify different locations and severities of bearing fault. The experimental results indicate that KMCSVM has better classification capability than other methods.

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A novel procedure for diagnosing multiple faults in rotating machinery

Cited by 50 publications

References 21 publications

Research on Fault Extraction Method of Variational Mode Decomposition Based on Immunized Fruit Fly Optimization Algorithm

Research on Fault Extraction Method of Variational Mode Decomposition Based on Immunized Fruit Fly Optimization Algorithm

Varying Load Detection in A Gearbox System Based on Adaptive Threshold Estimation

Bearing fault diagnosis via kernel matrix construction based support vector machine

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