Multi-feature fusion for fault diagnosis of rotating machinery based on convolutional neural network

Liu, Shaoqing; Ji, Zhenshan; Wang, Yong; Zhang, Zuchao; Xu, Zhanghou; Kan, Chaohao; Jin, Ke

doi:10.1016/j.comcom.2021.04.016

Cited by 29 publications

(10 citation statements)

References 22 publications

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“…Liu et al established a LightGBM-based fault diagnosis model for rotating machinery. Compared with traditional methods, LightGBMbased methods have significantly improved accuracy and computational speed [34]. The LightGBM algorithm offers adjustable parameters, encompassing the learning rate and max_depth.…”

Section: Introductionmentioning

confidence: 99%

Optimised LightGBM-based health condition evaluation method for the functional components in CNC machine tools under strong noise background

Jia,

Jialong,

Wanghao

et al. 2024

Meas. Sci. Technol.

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The accurate health condition evaluation of the functional components in computer numerical control (CNC) machine tools is an important prerequisite for predictive maintenance and fault warning. The vibration signals of the functional components in CNC machine tools often contain substantial noise, impeding the extraction of relevant health condition information from the vibration signals. This work presents an approach that leverages the variational mode decomposition (VMD) enhanced by the Artificial Hummingbird Algorithm (AHA) alongside the Light Gradient Boosting Machine (LightGBM) optimised through particle swarm optimisation (PSO) to evaluate the health condition of the functional components in CNC machine tools amidst pervasive noise. Initially, the AHA optimised the penalty factor (α) and the decomposition layer (K) within the VMD. This optimised VMD was subsequently applied to denoise the original vibration signals. After this denoising process, PSO was employed to optimise the learning rate and maximum tree depth within LightGBM. Health condition evaluation experiments were executed on the feed system and spindle of the CNC machine tool to validate the proposed methodology. Comparative analysis indicates that the proposed method attains paramount accuracy and computational efficiency, which are crucial for accurately evaluating the health condition of the functional components in CNC machine tools.

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Section: Introductionmentioning

confidence: 99%

Optimised LightGBM-based health condition evaluation method for the functional components in CNC machine tools under strong noise background

Jia,

Jialong,

Wanghao

et al. 2024

Meas. Sci. Technol.

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“…Various ML techniques are practiced for different kinds of problems. The most commonly used algorithms are Deep Neural Networks (DNN) algorithms [ [1] , [2] , [3] , [4] , [5] ], Artificial Neural Networks (ANN) [ [6] , [7] , [8] ], AdaBoost (AB) [ 8 , 9 ], Random Forest (RF) [ 10 , 11 ], Support Vector Machines (SVM) [ [12] , [13] , [14] ], K Nearest Neighbors (KNN) [ 12 , 13 , 15 ], and Decision Trees (DT) [ 16 , 17 ]. As the concept of the Fourth Industrial Revolution or Industry 4.0 is introduced, the use of ML becomes even more critical, especially for the industry itself.…”

Section: Introductionmentioning

confidence: 99%

Machine learning for fault analysis in rotating machinery: A comprehensive review

Daş

Birant

2023

Heliyon

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“…This paradigm leverages traditional signal analysis theory as a foundational platform for conceptual innovation, yielding a data-driven framework for prognosticating and diagnosing rotating machinery faults. Despite the notable achievements attained through these strategies, each approach is not without its limitations [1].…”

Section: Introductionmentioning

confidence: 99%

“…However, a comprehensive analysis unveils that while the previously proposed models showcase commendable health state identification prowess under ideal and constant operational conditions, they exhibit limitations when contending with noise interferences and the intricate dynamics of variable operational settings. These limitations can be distilled as follows: (1) The assumption that training samples and the processing of new test samples by the model are independent and identically distributed is not consistently met in practice. Consequently, susceptibility to domain drift emerges as a prevalent concern.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Rotating Machinery under Complex Conditions Based on Multi-Scale Convolutional Neural Networks

Kang,

Luo,

Wang

et al. 2023

J. Phys.: Conf. Ser.

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In complex operational scenarios involving variable speeds, burdens, and noise, rotating machinery necessitates extended maintenance. Extracting stable and effective fault-sensitive features under such intricate conditions presents a significant challenge. To tackle this issue, the paper introduces the Multi-Scale Convolutional Neural Network (MSCNN) model tailored specifically for such complexities. The approach in this paper simultaneously captures multi-scale vibration signal features using the innovative Multi-Scale Bifurcation (MSB) module and subsequently aggregates them through a multi-scale fusion layer. This model effectively addresses the common problem of low CNN accuracy in fault diagnosis. The paper validates methodology using a bearing dataset provided by Case Western Reserve University (CWRU) and demonstrates superior performance compared to classical CNN models and other alternatives, achieving an impressive 99.56% classification accuracy for normal signals.

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Multi-feature fusion for fault diagnosis of rotating machinery based on convolutional neural network

Cited by 29 publications

References 22 publications

Optimised LightGBM-based health condition evaluation method for the functional components in CNC machine tools under strong noise background

Optimised LightGBM-based health condition evaluation method for the functional components in CNC machine tools under strong noise background

Machine learning for fault analysis in rotating machinery: A comprehensive review

Fault Diagnosis of Rotating Machinery under Complex Conditions Based on Multi-Scale Convolutional Neural Networks

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