Bayesian optimized deep convolutional network for bearing diagnosis

Lu, Yanfei; Wang, Zengyan; Xie, Rui; Zhang, Jialin; Pan, Zhipeng; Liang, Steven Y.

doi:10.1007/s00170-020-05390-y

Cited by 9 publications

(6 citation statements)

References 38 publications

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“…By randomly sampling the search range, random search is generally faster than grid search, but the results cannot be guaranteed. The Bayesian optimization method is the process of updating the posterior distribution of a given optimization objective function by continuously adding sample points [67,68] as shown in figure 4. A simple summary is to consider the information from the previous parameter in order to better adjust the current parameter.…”

Section: Hyperparameter Optimization Of Cnnmentioning

confidence: 99%

A review on convolutional neural network in rolling bearing fault diagnosis

Li,

Ma,

Yuan

et al. 2024

Meas. Sci. Technol.

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The health condition of rolling bearings has a direct impact on the safe operation of high-speed trains. And their working environment is harsh and the working condition is complex, which brings challenges to fault diagnosis. With the development of computer technology, deep learning has been applied in the field of fault diagnosis and has rapidly developed. Among them, convolutional neural network (CNN) has received great attention from researchers due to its powerful data mining ability and feature adaptive learning ability. Based on recent research hotspots, the development history and trend of CNN is summarized and analyzed. Firstly, the basic structure of CNN is introduced and the important progress of classical CNN models for rolling bearing fault diagnosis in recent years is studied. The problems with the classic CNN algorithm have been pointed out. Secondly, to solve the above problems, combined with recent research achievements, various methods and principles for optimizing CNN are introduced and compared from the perspectives of deep feature extraction, hyperparameter optimization, network structure optimization. Although significant progress has been made in the research of fault diagnosis of rolling bearings based on CNN, there is still room for improvement and development in addressing issues such as low accuracy of imbalanced data, weak model generalization, and poor network interpretability. Therefore, the future development trend of CNN networks is discussed finally. And transfer learning models are introduced to improve the generalization ability of CNN and interpretable CNN is used to increase the interpretability of CNN networks.

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Section: Hyperparameter Optimization Of Cnnmentioning

confidence: 99%

A review on convolutional neural network in rolling bearing fault diagnosis

Li,

Ma,

Yuan

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…electric motor) can be considered a good result, but if we extend this to fleets of 300+ bearings it is no longer acceptable. In the last decade, many scientific papers have been published on the application of complex statistical methods to machine diagnostics [10][11][12]. In particular, they focus on Bayesian inference that updates the probability of an event (or hypothesis) as more evidence or information becomes available [13].…”

Section: Critical Issues and Future Prospectsmentioning

confidence: 99%

Future Challenges in Condition Monitoring from an Industrial Perspective: The Case of the Independent Carts Systems

Borghi¹,

Cocconcelli

2021

Acoust Aust

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In this paper, the authors discuss about the future challenges of condition monitoring in an industrial context. One of the authors is Line Manager at Data Processing & Analytics for Equipment, Industry 4.0 & MES Products division of Tetra Pak Packaging Solutions S.p.A., a multinational company that approached condition monitoring and diagnostics fifteen years ago. So far, they have gained experience and have a clear idea of what the Industrial field expects in the coming years. In this paper, the analysis of a specific case study is an opportunity to suggest more general research themes on condition monitoring.

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“…Literature [5] solved monitoring a system is often not an easy task, a hybrid method for diagnosing single and multiple simultaneous faults is proposed. Literature [6] presented an innovative diagnosis model using the deep convolutional network with Bayesian optimization to diagnose the defect severity of bearings. Literature [7] proposed a novel fault diagnosis scheme for planetary gearbox using multi-criteria fault feature selection, and classifications are performed by the SVM and sparse Bayesian extreme learning machine.…”

Section: Introductionmentioning

confidence: 99%

Petrochemical Gearbox Fault Location and Diagnosis Method Based on Distributed Bayesian Model and Neural Network

Su¹,

Su²,

Zhang³

et al. 2022

Journal of Compurters

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<p>Increasing attention has been paid to the economic losses and personnel injuries caused by petrochemical gearbox faults. As a result, petrochemical enterprises started to pay huge attention on fault diagnosis technology to solve the fault diagnosis problem. Petrochemical gearboxes are characterized by many fault types, feature variables, and many-to-many relationships between the various fault parameters, which pose huge challenges in the fault diagnosis of petrochemical units. This paper proposes a petrochemical gearbox fault location and diagnosis method based on a distributed Bayesian model and neural network. The proposed approach is based on sample feature information and Bayesian network prior probability to construct a basic framework for petrochemical gearbox fault location. Neural network technology is used to to diagnose fault types. It is helpful to build a long-term fault diagnosis and monitoring system for rotating machinery of petrochemical units.</p> <p> </p>

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Bayesian optimized deep convolutional network for bearing diagnosis

Cited by 9 publications

References 38 publications

A review on convolutional neural network in rolling bearing fault diagnosis

A review on convolutional neural network in rolling bearing fault diagnosis

Future Challenges in Condition Monitoring from an Industrial Perspective: The Case of the Independent Carts Systems

Petrochemical Gearbox Fault Location and Diagnosis Method Based on Distributed Bayesian Model and Neural Network

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