Degradation State Partition and Compound Fault Diagnosis of Rolling Bearing Based on Personalized Multilabel Learning

Ma, Xin; Hu, Yu; Wang, Menghui; Li, Fengying; Wang, Youqing

doi:10.1109/tim.2021.3091504

Cited by 50 publications

(15 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, the compound fault can be decoupled into multiple single faults by the diagnosis model via outputting multiple labels. In recent years, the multilabel learning-based method has attracted increasing attention from related scholars, and various approaches have been proposed based on such ideas [131][132][133][134][135][136]. For instance, Huang et al developed a compound fault diagnosis framework by combining deep CNNs with a multilabel classifier which can output single or multiple labels for a testing sample [131].…”

Section: ) Supervised Learning-based Methodsmentioning

confidence: 99%

“…The essence of the multilabel classifier is to use the Sigmoid function to substitute the Softmax function as the activation function in the last classification layer, in doing so, the output probabilities of each classification neuron are independent, and the number of output labels can be determined by a customized principle. Following such insights, there are many similar methods that have been developed and investigated for the compound fault diagnosis of rotating machinery [132][133][134][135][136]. It can be concluded from the publications that the effectiveness of the compound fault diagnosis method based on multilabel learning has been validated.…”

Section: ) Supervised Learning-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

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: ) Supervised Learning-based Methodsmentioning

confidence: 99%

Section: ) Supervised Learning-based Methodsmentioning

confidence: 99%

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

“…If there is no efficient and unified consensus mechanism among the nodes, it will lead to inefficient node consensus. Yihao Qin et al 16 proposed a recursive algorithm with low computer complexity and a new method combining correlation statistical analysis and sliding window technique to detect initial faults by making full use of the information of process and quality variables.Xin Ma et al 17 proposed two multi-label learning algorithms for PHM of rolling bearings, named personalised binary correlation ( PBR) and hierarchical multi-label K-nearest neighbours (HML-KNN), thus converting the PHM problem into a multi-label learning problem. qing Chen et al 18 combined the advantages of artifificial neural network (ANN) and canonical correlation analysis (CCA) from their respective principles and proposed a novel fault detection and process monitoring method called artificial neural correlation analysis (ANCA).…”

Section: Introductionmentioning

confidence: 99%

Research on PBFT consensus algorithm for grouping based on feature trust

Wang

Zhong²,

Cheng³

2022

Sci Rep

View full text Add to dashboard Cite

The consensus mechanism is the core of the blockchain system, which plays an important role in the performance and security of the blockchain system . The Practical Byzantine Fault Tolerance (PBFT) algorithm is a widely used consensus algorithm, but the PBFT algorithm also suffers from high consensus latency, low throughput and performance. In this paper, we propose a grouped PBFT consensus algorithm (GPBFT) based on feature trust. First, the algorithm evaluates the trust degree of nodes in the transaction process through the EigenTrust trust model, and uses the trust degree of nodes as the basis for electing master nodes and proxy nodes. Then, the algorithm divides the nodes in the blockchain system into multiple groups, and the consensus within each independent group does not affect the other groups, which greatly reduces the communication overhead of the consensus process when the number of nodes in the system is large. Finally, we demonstrate through theoretical and experimental analysis that the GPBFT algorithm has a significant improvement in security and performance.

show abstract

“…Detection of out‐of‐control sample(s) in MSPM is followed by fault classification or fault isolation. The former can only be achieved via supervised models, [ 13 ] while unsupervised models are sufficient for the latter, which aims to determine process variables, which may help in finding the root cause of the fault. Plotting variable contributions is, historically, the first and still the most popular method for fault isolation, presumably due to its simplicity.…”

Section: Introductionmentioning

confidence: 99%

Reducing smearing effect in contribution plots and improving fault detection via polynomial approximated isomap embeddings

Alakent

2022

Can J Chem Eng

View full text Add to dashboard Cite

Dimension reduction is an essential method used in multivariate statistical process monitoring for fault detection and diagnosis. Principal component analysis (PCA) and independent component analysis (ICA) are the most frequently used linear dimensional reduction tools, and the contribution plot is the most popular fault isolation method in the absence of any prior information on the faults. These methods, however, come with their shortcomings.The fault detection capability of linear methods may not be sufficient for nonlinear processes, and smearing effect is known to deteriorate the diagnostics obtained from contribution plots. While the fault detection rate may be increased by kernelized methods or deep artificial neural network models, tuning data-dependent hyperparameter(s) and network structure with limited historical data is not an easy task. Furthermore, the resulting non-linear models often do not directly possess fault isolation capability. In the current study, we aim to devise a novel method named ICA pIso -PCA, which offers non-linear fault detection and isolation in a rather straightforward manner. The rationale of ICA pIso -PCA mainly involves building a non-linear scores matrix, composed of principal component scores and high-order polynomial approximated isomap embeddings, followed by implementation of the ICA-PCA algorithm on this matrix. Applications on a toy dataset and the Tennessee Eastman plant show that the I 2 index from ICA pIso -PCA yields a high fault detection rate and offers accurate contribution plots with diminished smearing effects compared to those from traditional monitoring methods. Easy implementation and the potential for future research are further advantages of the proposed method.

show abstract

Degradation State Partition and Compound Fault Diagnosis of Rolling Bearing Based on Personalized Multilabel Learning

Cited by 50 publications

References 48 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

Research on PBFT consensus algorithm for grouping based on feature trust

Reducing smearing effect in contribution plots and improving fault detection via polynomial approximated isomap embeddings

Contact Info

Product

Resources

About