Vibration signal analysis using parameterized time–frequency method for features extraction of varying-speed rotary machinery

Yang, Yixin; Dong, Xingjian; Peng, Zhike; Meng, Guang

doi:10.1016/j.jsv.2014.09.025

Cited by 97 publications

(35 citation statements)

References 45 publications

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“…The timely detection of the mechanical system and taking effective measures for bearing failure are indispensable works in real production process, and also a worthy of the further study of the direction [26]. Because the working condition is complex and the bearing is susceptible to noise interference, it often causes vibration signals show the characteristics of high-dimensional nonlinearity.…”

Section: Fault Diagnosis Model Based On M-lle Algorithmmentioning

confidence: 99%

Modified Local Linear Embedding Algorithm for Rolling Element Bearing Fault Diagnosis

Yao

et al. 2017

Applied Sciences

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Due to the noise accompanied with rolling element bearing fault signal, it can reduce the accuracy of faulty diagnoses. In order to improve the robustness of a faulty diagnosis, this study proposed a fault diagnosis model based on modified local linear embedding (M-LLE) algorithm. Aiming at the characteristics of rolling element bearing fault data, the vibration signal was first analyzed in time domain and frequency domain to construct high dimension eigenvectors. Next, the high-dimensional eigenvectors can be reduced to low-dimensional eigenvectors by M-LLE algorithm. In the M-LLE algorithm, the Mahalanobis distance (MD) metric is adopted to replace Euclidean distance in traditional neighborhood construction and L1-norm is used to standardize weight matrix, which can enhance the anti-noise ability of the Local Linear Embedding (LLE) algorithm. Finally, fault diagnosis results can be obtained when low-dimensional rolling element bearing fault data is classified by K-Nearest Neighbor (KNN) classifier. By simulating the noisy artificial data sets in different degrees, the proposed algorithm can get the perfect local structure of manifolds. The effectiveness of M-LLE algorithm can be proved. In addition, experimental results of real rolling element bearing data which provided by the University of Cincinnati show that the accuracies of all kinds of faults can reach 100%. It can be deemed that the proposed fault diagnosis model can effectively improve the accuracy of fault diagnosis.

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Section: Fault Diagnosis Model Based On M-lle Algorithmmentioning

confidence: 99%

Modified Local Linear Embedding Algorithm for Rolling Element Bearing Fault Diagnosis

Yao

et al. 2017

Applied Sciences

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“…EMD has some problems such as over envelope, end effects, and mode mixing [22][23][24]. The effectiveness of STFT is still hampered by the limitation of single triangular basis [25,26]. WVD can produce interference terms on the time-frequency domain in a critical condition and high computational complexity [27].…”

Section: Introductionmentioning

confidence: 99%

Rolling Bearing Fault Diagnosis Using Modified Neighborhood Preserving Embedding and Maximal Overlap Discrete Wavelet Packet Transform with Sensitive Features Selection

Dong

Ding

et al. 2018

Shock and Vibration

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In order to enhance the performance of bearing fault diagnosis and classification, features extraction and features dimensionality reduction have become more important. The original statistical feature set was calculated from single branch reconstruction vibration signals obtained by using maximal overlap discrete wavelet packet transform (MODWPT). In order to reduce redundancy information of original statistical feature set, features selection by adjusted rand index and sum of within-class mean deviations (FSASD) was proposed to select fault sensitive features. Furthermore, a modified features dimensionality reduction method, supervised neighborhood preserving embedding with label information (SNPEL), was proposed to realize low-dimensional representations for high-dimensional feature space. Finally, vibration signals collected from two experimental test rigs were employed to evaluate the performance of the proposed procedure. The results show that the effectiveness, adaptability, and superiority of the proposed procedure can serve as an intelligent bearing fault diagnosis system.

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“…Therefore, it is important to accurately diagnose bearing faults at the early stage [4,5]. Vibration-based fault diagnosis has been extensively studied to improve existing techniques toward the goal of more accurately dealing with various problems, such as varying load effect and noise contamination [3,4,5,6,7,8]. Especially, the sensitivity of diagnostic features from the vibration signals may vary with different load conditions due to nonlinear effect and non-stationary noise, of which no single-domain processing methods can comprehensively extract the fault features to reflect the condition [9].…”

Section: Introductionmentioning

confidence: 99%

Weighted Kernel Entropy Component Analysis for Fault Diagnosis of Rolling Bearings

Zhou

Shi

Liao

et al. 2017

Sensors

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This paper presents a supervised feature extraction method called weighted kernel entropy component analysis (WKECA) for fault diagnosis of rolling bearings. The method is developed based on kernel entropy component analysis (KECA) which attempts to preserve the Renyi entropy of the data set after dimension reduction. It makes full use of the labeled information and introduces a weight strategy in the feature extraction. The class-related weights are introduced to denote differences among the samples from different patterns, and genetic algorithm (GA) is implemented to seek out appropriate weights for optimizing the classification results. The features based on wavelet packet decomposition are derived from the original signals. Then the intrinsic geometric features extracted by WKECA are fed into the support vector machine (SVM) classifier to recognize different operating conditions of bearings, and we obtain the overall accuracy (97%) for the experimental samples. The experimental results demonstrated the feasibility and effectiveness of the proposed method.

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Vibration signal analysis using parameterized time–frequency method for features extraction of varying-speed rotary machinery

Cited by 97 publications

References 45 publications

Modified Local Linear Embedding Algorithm for Rolling Element Bearing Fault Diagnosis

Modified Local Linear Embedding Algorithm for Rolling Element Bearing Fault Diagnosis

Rolling Bearing Fault Diagnosis Using Modified Neighborhood Preserving Embedding and Maximal Overlap Discrete Wavelet Packet Transform with Sensitive Features Selection

Weighted Kernel Entropy Component Analysis for Fault Diagnosis of Rolling Bearings

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