Fault diagnosis for circuit-breakers using adaptive chirp mode decomposition and attractor’s morphological characteristics

Yang, Qiuyu; Ruan, Jiangjun; Zhuang, Zhijian

doi:10.1016/j.ymssp.2020.106921

Cited by 22 publications

(8 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Chen developed ACMD in 2019 and used it to identify rub-impact fault for rotor-stator systems [ 26 ]. In 2020, Yang proposed a new fault detection method for circuit-breakers by combining ACMD and morphological characteristics [ 27 ]. In 2021, Srivastava developed a rotor rub model which can detect the onset of rotor rub efficiently whilst revealing the properties of IF of rubbing rotor; ACMD is used to detect the rub and establish the properties of IF during model building [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Underwater Acoustic Target Identification Method Based on Spectral Characteristic Extraction via Modified Adaptive Chirp Mode Decomposition

Yang

Zhou

et al. 2023

Entropy

View full text Add to dashboard Cite

As is well-known, ship-radiated noise (SN) signals, which contain a large number of ship operating characteristics and condition information, are widely used in ship recognition and classification. However, it is still a great challenge to extract weak operating characteristics from SN signals because of heavy noise and non-stationarity. Therefore, a new mono-component extraction method is proposed in this paper for taxonomic purposes. First, the non-local means algorithm (NLmeans) is proposed to denoise SN signals without destroying its time-frequency structure. Second, adaptive chirp mode decomposition (ACMD) is modified and applied on denoised signals to adaptively extract mono-component modes. Finally, sub-signals are selected based on spectral kurtosis (SK) and then analyzed for ship recognition and classification. A simulation experiment and two application cases are used to verify the effectiveness of the proposed method and the results show its outstanding performance.

show abstract

Section: Introductionmentioning

confidence: 99%

A Novel Underwater Acoustic Target Identification Method Based on Spectral Characteristic Extraction via Modified Adaptive Chirp Mode Decomposition

Yang

Zhou

et al. 2023

Entropy

View full text Add to dashboard Cite

show abstract

“…Aiming at addressing the above shortcomings of EMD and its improved methods, ACMD is proposed and has been used widely in multiple fields (Chen et al, 2019; Chen et al, 2019; Chen et al, 2020; Ding and Wang, 2022; Wang et al, 2021; Yang et al, 2020), which has the following main virtue: without the prior information of the modal order, it could decompose the multi-modal coupling signal adaptively with high resolution through the filtering of signal spectrum energy value. Considering the multi-modality, non-stationarity and multi-interference characteristics of the vibration signal of faulty bearing, a novel ACMD with sparsity index regrouping scheme is proposed correspondingly and used as the pre-processing method of TDSR to decompose the original complex signal into several modes.…”

Section: Introductionmentioning

confidence: 99%

Weak fault feature extraction using adaptive chirp mode decomposition with sparsity index regrouping scheme and time-delayed feedback stochastic resonance

Zhang

Wang

2022

Journal of Vibration and Control

View full text Add to dashboard Cite

The failure features of rolling bearings are often weak due to the influence of strong background noise. In addition, the vibration signals of faulty rolling bearing often show nonlinear and non-stationary characteristics, and the conventional time-frequency method is no longer suitable for extracting effective fault features. In order to extract the early weak fault characteristics of rolling bearing accurately, a weak fault feature extraction method for rolling bearing by combining adaptive chirp mode decomposition (ACMD) based on sparsity index regrouping scheme with time-delayed feedback stochastic resonance (TDSR) is proposed in the paper. The proposed method comprehensively utilizes the adaptive decomposition characteristics of ACMD for multi-component non-stationary signals and the enhancement effect of TDFSR on low-frequency signals in the fast Fourier transform (FFT) result. Firstly, ACMD is used to decompose the early weak fault signal of rolling bearing into a series of mode signals, then the proposed signal regrouping scheme based on sparsity index is utilized to regroup the obtained series of modes. Secondly, the optimal reconstructed component containing the main fault information is input into the calculation model of TDSR. Finally, FFT is performed on the output signal of TDSR to extract the fault characteristics effectively.

show abstract

“…In the VMD, if the parameter K is wrongly selected, the phenomenon such as overdecomposition or underdecomposition will appear. To overcome these shortcomings, some improved algorithms have been presented such as Simplistic Geometric Mode Decomposition (SGMD), Adaptive Chirped Mode Decomposition (ACMD), and New Spectral Analysis Methods (NSAM) in [5][6][7]. Especially, to address the shortcoming of the EMD, the study in [8] has constructed the EMD envelope correction method using B-spline interpolation and base spline.…”

Section: Introductionmentioning

confidence: 99%

“…represents the spectrum diagram of VMD decomposition when K � 3 under the normal state. From the Fault diagnosis flow chart based on optimized VMD 6. Mathematical Problems in Engineering spectrum diagram obtained, VMD avoids the defects of modal aliasing and endpoint effect of decomposition methods such as EMD.Figure 5shows the characteristic signals extracted from the signal components under the normal state.…”

mentioning

confidence: 99%

A Classification Algorithm of Fault Modes-Integrated LSSVM and PSO with Parameters’ Optimization of VMD

Huang

Qin

2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

To overcome the shortcomings that the early fault characteristics of rolling bearing are not easy to be extracted and the identification accuracy is not high enough, a novel collaborative diagnosis method is presented combined with VMD and LSSVM for incipient faults of rolling bearing. First, the basic concept of VMD was introduced in detail, and then, the adaptive selection principle of parameter K in VMD was constructed by instantaneous frequency mean. Furthermore, we used Lagrangian polynomial and Euclidean norm to verify the value of K accurately. Secondly, we proposed a classification algorithm based on PSO-optimized LSSVM. Meanwhile, the flowchart of the classification algorithm of fault modes may be also designed. Third, the experiment shows that the presented algorithm in this paper is effective by using the existing failure data provided by the laboratory of Guangdong Petrochemical Research Institute. Finally, some conclusions and application prospects were discussed.

show abstract

Fault diagnosis for circuit-breakers using adaptive chirp mode decomposition and attractor’s morphological characteristics

Cited by 22 publications

References 23 publications

A Novel Underwater Acoustic Target Identification Method Based on Spectral Characteristic Extraction via Modified Adaptive Chirp Mode Decomposition

A Novel Underwater Acoustic Target Identification Method Based on Spectral Characteristic Extraction via Modified Adaptive Chirp Mode Decomposition

Weak fault feature extraction using adaptive chirp mode decomposition with sparsity index regrouping scheme and time-delayed feedback stochastic resonance

A Classification Algorithm of Fault Modes-Integrated LSSVM and PSO with Parameters’ Optimization of VMD

Contact Info

Product

Resources

About