Fault Diagnosis of Rolling Bearing Based on a Novel Adaptive High‐Order Local Projection Denoising Method

Yuan, Rui; Lv, Yong; Song, Gangbing

doi:10.1155/2018/3049318

Cited by 13 publications

(12 citation statements)

References 43 publications

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“…Next, the alternate direction method of multipliers (ADMM) and iterative search are utilized to obtain the saddle point of Lagrange multipliers [39]. The optimization problems of m k and ω k are formulated as (3) and (4) respectively.…”

Section: Variational Mode Decompositionmentioning

confidence: 99%

“…Owing to the rich information carried by vibration signals, most of fault diagnosis methods for rolling bearings rely on analyzing vibration signals [4,5], the resistance of the bearing can be measured using electrodynamic sensors or laser vibrometers [6]. Considering that vibration signals are commonly non-stationary, it is difficult to extract critical features directly from fault signals.…”

Section: Introductionmentioning

confidence: 99%

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Fault Diagnosis for Rolling Bearing Based on Semi-Supervised Clustering and Support Vector Data Description with Adaptive Parameter Optimization and Improved Decision Strategy

Tan

Wang

et al. 2019

Applied Sciences

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Rolling bearing is of great importance in modern industrial products, the failure of which may result in accidents and economic losses. Therefore, fault diagnosis of rolling bearing is significant and necessary and can enhance the reliability and efficiency of mechanical systems. Therefore, a novel fault diagnosis method for rolling bearing based on semi-supervised clustering and support vector data description (SVDD) with adaptive parameter optimization and improved decision strategy is proposed in this study. First, variational mode decomposition (VMD) was applied to decompose the vibration signals into sets of intrinsic mode functions (IMFs), where the decomposing mode number K was determined by the central frequency observation method. Next, fuzzy entropy (FuzzyEn) values of all IMFs were calculated to construct the feature vectors of different types of faults. Later, training samples were clustered with semi-supervised fuzzy C-means clustering (SSFCM) for fully exploiting the information inside samples, whereupon a small number of labeled samples were able to provide sufficient data distribution information for subsequent SVDD algorithms and improve its recognition ability. Afterwards, SVDD with improved decision strategy (ID-SVDD) that combined with k-nearest neighbor was proposed to establish diagnostic model. Simultaneously, the optimal parameters C and σ for ID-SVDD were searched by the newly proposed sine cosine algorithm improved with adaptive updating strategy (ASCA). Finally, the proposed diagnosis method was applied for engineering application as well as contrastive analysis. The obtained results reveal that the proposed method exhibits the best performance in all evaluation metrics and has advantages over other comparison methods in both precision and stability.

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Section: Variational Mode Decompositionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis for Rolling Bearing Based on Semi-Supervised Clustering and Support Vector Data Description with Adaptive Parameter Optimization and Improved Decision Strategy

Tan

Wang

et al. 2019

Applied Sciences

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“…In the local projection denoising algorithm [46,47], as for each point y i in reconstructed d dimensional phase space, temporarily uncorrelated r neighborhood points y…”

Section: Local Projection Subspacementioning

confidence: 99%

Fault Classification of Rotary Machinery Based on Smooth Local Subspace Projection Method and Permutation Entropy

Xiao

2019

Applied Sciences

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Collected mechanical signals usually contain a number of noises, resulting in erroneous judgments of mechanical condition diagnosis. The mechanical signals, which are nonlinear or chaotic time series, have a high computational complexity and intrinsic broadband characteristic. This paper proposes a method of gear and bearing fault classification, based on the local subspace projection noise reduction and PE. A novel nonlinear projection noise reduction method, smooth orthogonal decomposition (SOD), is proposed to denoise the vibration signals of various operation conditions. SOD can decompose the reconstructed multiple strands to identify smooth local subspace. In the process of projection from a high dimension to a low dimension, a new weight matrix is put forward to achieve a better denoising effect. Afterwards, permutation entropy (PE) is applied in the detection of time sequence randomness and dynamic mutation behavior, which can effectively detect and amplify the variation of vibration signals. Hence PE can characterize the working conditions of gear and bearing under different conditions. The experimental results illustrate the effectiveness and superiority of the proposed approach. The theoretical derivations, numerical simulations and experimental studies, all confirm that the proposed approach based on the smooth local subspace projection method and PE, is promising in the field of the fault classification of rotary machinery.

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“…Owing to rich information carried by vibration signals, most of fault diagnosis methods for rolling bearings rely on analyzing vibration signals [6]. However, vibration signal is commonly nonstationary in actual operation, which restricts the extraction effect for fault features, and thus affects accuracy of fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Blind Parameter Identification of MAR Model and Mutation Hybrid GWO‐SCA Optimized SVM for Fault Diagnosis of Rotating Machinery

Tan

Zhang

et al. 2019

Complexity

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As a crucial and widely used component in industrial fields with great complexity, the health condition of rotating machinery is directly related to production efficiency and safety. Consequently, recognizing and diagnosing rotating machine faults remain to be one of the main concerns in preventing failures of mechanical systems, which can enhance the reliability and efficiency of mechanical systems. In this paper, a novel approach based on blind parameter identification of MAR model and mutation hybrid GWO-SCA optimization is proposed to diagnose faults for rotating machinery. Signals collected from different types of faults were firstly split into sets of intrinsic mode functions (IMFs) by variational mode decomposition (VMD), the decomposing mode number K of which was preset with central frequency observation method. Then the multivariate autoregressive (MAR) model of all IMFs was established, whose order was determined by Schwartz Bayes Criterion (SBC), and all parameters of the model were identified blindly through QR decomposition, where key features were subsequently extracted via principal component analysis (PCA) to construct feature vectors of different fault types. Afterwards, a hybrid optimization algorithm combining mutation operator, grey wolf optimizer (GWO), and sine cosine algorithm (SCA), termed mutation hybrid GWO-SCA (MHGWOSCA), was proposed for parameter selection of support vector machine (SVM). The optimal SVM model was later employed to classify different fault samples. The engineering application and contrastive analysis indicate the availability and superiority of the proposed method.

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Fault Diagnosis of Rolling Bearing Based on a Novel Adaptive High‐Order Local Projection Denoising Method

Cited by 13 publications

References 43 publications

Fault Diagnosis for Rolling Bearing Based on Semi-Supervised Clustering and Support Vector Data Description with Adaptive Parameter Optimization and Improved Decision Strategy

Fault Diagnosis for Rolling Bearing Based on Semi-Supervised Clustering and Support Vector Data Description with Adaptive Parameter Optimization and Improved Decision Strategy

Fault Classification of Rotary Machinery Based on Smooth Local Subspace Projection Method and Permutation Entropy

Blind Parameter Identification of MAR Model and Mutation Hybrid GWO‐SCA Optimized SVM for Fault Diagnosis of Rotating Machinery

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