Maximum envelope-based Autogram and symplectic geometry mode decomposition based gear fault diagnosis method

Wang, Xinglong; Zheng, Jianhua; Pan, Haiyang; Liu, Qingyun; Wang, Chengjun

doi:10.1016/j.measurement.2020.108575

Cited by 32 publications

(9 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…1 Therefore, for the sake of operational safety, the incipient faults of gears and bearings in rotating machineries must be identified and monitored at an early stage. Vibrational signals have been widely utilized in the modal analysis, 2,3 dynamic analysis, 4,5 and fault diagnosis [6][7][8][9] of mechanical transmission components due to their high stability and low interference. However, the identification of incipient faults of gears and bearings in rotating machinery based on vibration signals is a difficult task.…”

Section: Introductionmentioning

confidence: 99%

A local spectrum enhancement‐based method and its application in incipient fault diagnosis of rotating machinery

Shi

Wang

et al. 2023

Int Journal of Mech Sys Dyn

Self Cite

View full text Add to dashboard Cite

Incipient faults of gears and rolling bearings in rotating machineries are very difficult to identify using traditional envelope analysis methods. To address this challenge, this paper proposes an effective local spectrum enhancement-based diagnostic method that can identify weak fault frequencies in the original complicated raw signals. For this purpose, a traversal frequency band segmentation technique is first

show abstract

Section: Introductionmentioning

confidence: 99%

A local spectrum enhancement‐based method and its application in incipient fault diagnosis of rotating machinery

Shi

Wang

et al. 2023

Int Journal of Mech Sys Dyn

Self Cite

View full text Add to dashboard Cite

show abstract

“…Rolling bearings has an indispensable role in many large rotating machines; once it works with a local failure, the normal operation of mechanical equipment will be disturbed, and serious economic losses and safety incidents will be caused if the fault cannot be detected in time [ 1 , 2 ]. Hence, it is of significance to implement health monitoring and early fault diagnosis of rolling bearing for a safe production [ 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

Composite Multivariate Multi-Scale Permutation Entropy and Laplacian Score Based Fault Diagnosis of Rolling Bearing

Ying

Tong

Dong

et al. 2022

Entropy

Self Cite

View full text Add to dashboard Cite

As a powerful tool for measuring complexity and randomness, multivariate multi-scale permutation entropy (MMPE) has been widely applied to the feature representation and extraction of multi-channel signals. However, MMPE still has some intrinsic shortcomings that exist in the coarse-grained procedure, and it lacks the precise estimation of entropy value. To address these issues, in this paper a novel non-linear dynamic method named composite multivariate multi-scale permutation entropy (CMMPE) is proposed, for optimizing insufficient coarse-grained process in MMPE, and thus to avoid the loss of information. The simulated signals are used to verify the validity of CMMPE by comparing it with the often-used MMPE method. An intelligent fault diagnosis method is then put forward on the basis of CMMPE, Laplacian score (LS), and bat optimization algorithm-based support vector machine (BA-SVM). Finally, the proposed fault diagnosis method is utilized to analyze the test data of rolling bearings and is then compared with the MMPE, multivariate multi-scale multiscale entropy (MMFE), and multi-scale permutation entropy (MPE) based fault diagnosis methods. The results indicate that the proposed fault diagnosis method of rolling bearing can achieve effective identification of fault categories and is superior to comparative methods.

show abstract

“…However, the decomposition effect of SGMD is affected by end effect and reconstruction constraint condition. 23,24 In the analysis of the flow field, dynamic mode decomposition (DMD) as a data-driven algorithm to extract the dynamic information from unsteady experimental measurement or numerical simulation flow field, which can be used to analyze the main characteristics of complex flow or establish low-order flow field dynamics. 25 DMD have been used to extract the feature of complicated flow field, 26,27 but few literatures applied DMD to analyze nonlinear timeseries data.…”

Section: Introductionmentioning

confidence: 99%

“…However, the decomposition effect of SGMD is affected by end effect and reconstruction constraint condition. 23,24…”

Section: Introductionmentioning

confidence: 99%

Adaptive dynamic mode decomposition and its application in rolling bearing compound fault diagnosis

Zhang

2022

Structural Health Monitoring

View full text Add to dashboard Cite

The decoupling detection of compound faults in rolling bearing is attracting considerable attentions. In recent years, some time-series decomposition methods, such as ensemble empirical mode decomposition (EEMD), variational mode decomposition (VMD), symplectic geometry mode decomposition (SGMD) etc., are used to extract the fault characteristics of bearing fault vibration signal and achieve the purpose of fault diagnosis. However, the fault characteristics of compound fault vibration signals are unevenly distributed, some fault characteristics are weakly disturbed by noise, which limit these methods application in compound fault diagnosis. In this paper, the Koopman operator is introduced from the perspective of flow field dynamics information extraction, an adaptive dynamic mode decomposition (ADMD) is proposed to decompose the nonlinear time-series data into a set of dynamic mode components (DMCs). In ADMD, a high-degree polynomial is applied to fit the data sequence of flow field, and a low dimensional subspace can be obtained. The dominant dynamics of flow field can be represented by the eigenvalues and eigenvectors of the low-dimensional subspace, which can be transformed into reconstructed time series with different frequency, and obtained the frequency-based DMCs. The simulation and experimental analysis results show that the proposed method can effectively decouple different fault components of compound fault of rolling bearing.

show abstract

Maximum envelope-based Autogram and symplectic geometry mode decomposition based gear fault diagnosis method

Cited by 32 publications

References 19 publications

A local spectrum enhancement‐based method and its application in incipient fault diagnosis of rotating machinery

A local spectrum enhancement‐based method and its application in incipient fault diagnosis of rotating machinery

Composite Multivariate Multi-Scale Permutation Entropy and Laplacian Score Based Fault Diagnosis of Rolling Bearing

Adaptive dynamic mode decomposition and its application in rolling bearing compound fault diagnosis

Contact Info

Product

Resources

About