Compound Fault Diagnosis of Aero-Engine Rolling Element Bearing Based on CCA Blind Extraction

Zhang, Weitao; Ji, Xiaofan; Huang, Ju; Lou, Shun-Tian

doi:10.1109/access.2021.3130637

Cited by 8 publications

(7 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…BSP, also known as Bind Signal Separation (BSS), has been widely developed for solving the problem of compound fault diagnosis. Various effective algorithms have been proposed, such as Independent Component Analysis (ICA) [64][65][66][67], Sparse Component Analysis (SCA) [68][69][70][71], Morphological Component Analysis (MCA) [72], and other methods [73,74]. These algorithms can separate the identification characteristics of each single fault source from the complex monitoring signals, to accurately evaluate the health conditions of rotating machinery.…”

Section: ) Blind Signal Processing-based Methodsmentioning

confidence: 99%

“…Apart from the bearing compound faults, Yu et al proposed an improved MCA method for the compound fault diagnosis of gearboxes under the scenario when a gear fault and a bearing fault occur at the same time [72], in which there are two different components (one is the meshing component caused by gear fault, the other one is the periodic impulse component caused by bearing fault) that coupled in the compound fault signal. Additionally, other methods, such as the Null-Space Pursuit (NSP) [73] and Canonical Correlation Analysis (CCA) [74], have also been applied in the compound fault diagnosis for aero-engine rolling element bearing by scholars.…”

Section: ) Blind Signal Processing-based Methodsmentioning

confidence: 99%

“…The sparse representation-based method has been proven to be a prevalent tool in compound fault diagnosis due to its several advantages including different component matching, signal denoising, and signal separation without mode mixing. Generally, sparse representation theory mainly contains two aspects: overcomplete dictionary construction [74][75][76][77][78][79][80] and sparse coefficient solution [82][83][84][85][86].…”

Section: ) Sparse Representation-based Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Compound Fault Diagnosis for Rotating Machinery: State-of-the-Art, Challenges, and Opportunities

Huang

Xia

Zhang

et al. 2023

JDMD

View full text Add to dashboard Cite

Compound fault, as a primary failure leading to unexpected downtime of rotating machinery, dramatically increases the difficulty in fault diagnosis. To deal with the difficulty encountered in implementing compound fault diagnosis (CFD), researchers and engineers from industry and academia have made numerous significant breakthroughs in recent years. Admittedly, many systematic surveys focused on fault diagnosis have been conducted by reputable researchers. Nevertheless, previous review articles paid more attention to fault diagnosis with several single or independent faults, resulting in that there is still lacking a comprehensive survey on CFD. Therefore, to fulfill the above requirements, it is necessary to provide an in-depth overview of fault diagnosis methods or algorithms for compound faults of rotating machinery and uncover potential challenges or opportunities that would guide and inspire readers to devote their efforts to promoting fault diagnosis technology more effective and practical. Specifically, the backgrounds including the related definitions and a new taxonomy of CFD methods are detailed according to the way of implementing compound fault recognition. Then, the state-of-the-art applications of CFD are overviewed based on relevant publications in the past decades. Finally, the challenges and opportunities associated with implementing CFD are concluded and followed by a conclusion for ending this survey. We believe that this review article can provide a systematic guideline of CFD from different aspects for potential readers and seasoned researchers.

show abstract

Section: ) Blind Signal Processing-based Methodsmentioning

confidence: 99%

Section: ) Blind Signal Processing-based Methodsmentioning

confidence: 99%

Section: ) Sparse Representation-based Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Compound Fault Diagnosis for Rotating Machinery: State-of-the-Art, Challenges, and Opportunities

Huang

Xia

Zhang

et al. 2023

JDMD

View full text Add to dashboard Cite

show abstract

“…The canonical correlation analysis algorithm (CCA) is a common method for principal component extraction among multidimensional variables. This algorithm [24] extracts the principal components t 1 and m 1 from the system data using correlations, where t 1 and m 1 are linear combinations of the input and output data, respectively.…”

Section: Basic Theorymentioning

confidence: 99%

Performance Assessment of MIMO System With Non-Gaussian Disturbances

Jia

Zhou

2022

IEEE Access

View full text Add to dashboard Cite

Performance assessment of control loops is of great importance for industrial production. This paper proposes a novel performance assessment and controller tuning method for non-Gaussian MIMO feedback control systems. First, an algorithm based on minimum entropy and mutual information projection to latent structure (ME-PLS) is proposed to replace the canonical correlation analysis algorithm (CCA). The ME-PLS algorithm decomposes the system data into independent components related to inputs and outputs, and this algorithm applies to both Gaussian and non-Gaussian systems. Each pair of principal components represents a virtual non-Gaussian control loop. Next, the performance of each virtual loop is calculated separately with the non-Gaussian minimum entropy method. Finally, a least absolute deviation iterative algorithm based on the CARMA model (CARMA-LADI) is given to identify the parameters of each virtual loop and to give the actual controller tuning directions based on the relationships obtained by the ME-PLS algorithm.INDEX TERMS control loop performance assessment, independent components, MIMO system, maximum mutual information, minimum entropy

show abstract

“…The successful application of CAF theory in signal processing is presented in [25]. In a word, bearing compound fault diagnosis has numerous research achievements in the aspects of cyclostationarity theory [26,27], blind source separation [28,29], deep learning [30,31] and acoustic emission technology [32,33] The overall framework of the paper is as follows: the algorithm theory of MCKD, VMD and CAF are simply explained in section 2. The process of optimizing MCKD with IPSO algorithm is mainly described in section 3.1.…”

Section: Introductionmentioning

confidence: 99%

Application of IPSO-MCKD-IVMD-CAF in the compound fault diagnosis of rolling bearing

Zhou¹,

Wu²,

Liu³

et al. 2022

Meas. Sci. Technol.

View full text Add to dashboard Cite

The vibration signals of rolling bearings are mixed with a variety of noises and a variety of faults are coupled to each other that makes the fault frequencies interfere with each other, which leads to the difficulty of accurate extraction of the features of composite faults. Therefore, it is necessary not only to suppress the noise of vibration signals but also to separate and extract the features of compound faults. A compound fault feature extraction method based on improved particle swarm optimization (IPSO) algorithm optimized maximum correlation kurtosis deconvolution (MCKD), improved variational mode decomposition (IVMD) and cyclic autocorrelation function (CAF), named IPSO-MCKD-IVMD-CAF is proposed. Firstly, the parameters of MCKD are selected adaptively by the IPSO, and the original signal is pre-processed by the parameterized MCKD. Secondly, the IVMD is utilized to decompose the signal, several intrinsic mode functions (IMFs) are obtained, the kurtosis and correlation coefficients of each IMF component are calculated. Finally, appropriate IMF components are selected by kurtosis and correlation coefficient to be superimposed into the reconstructed signals, and the reconstructed signals are analyzed by CAF. The proposed method has been successfully applied in simulation and measured vibration signal analysis, and the features of compound faults can be separated and extracted with high accuracy. The IPSO algorithm shortens the optimization time, and the IVMD solves the parameter selection problem. The analysis results of simulation case and measured data show the advantages of the proposed method IPSO-MCKD-IVMD-CAF. Compared to existing methods, the proposed method not only increases the accuracy of compound fault feature extraction, but also has a very good performance on the separation and extraction of compound fault features. More importantly, the improved algorithm reduces the computational complexity.

show abstract

Compound Fault Diagnosis of Aero-Engine Rolling Element Bearing Based on CCA Blind Extraction

Cited by 8 publications

References 26 publications

Compound Fault Diagnosis for Rotating Machinery: State-of-the-Art, Challenges, and Opportunities

Compound Fault Diagnosis for Rotating Machinery: State-of-the-Art, Challenges, and Opportunities

Performance Assessment of MIMO System With Non-Gaussian Disturbances

Application of IPSO-MCKD-IVMD-CAF in the compound fault diagnosis of rolling bearing

Contact Info

Product

Resources

About