Study on A Fault Diagnosis Method of Rolling Element Bearing Based on Improved ACO and SVM Model

Deng, Wen; Li, Xiumei; Zhao, Huimin

doi:10.14257/ijfgcn.2016.9.3.16

Cited by 3 publications

(1 citation statement)

References 24 publications

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“…This technique also detects lubricant problems and is perfect for classification. An improved Ant Colony Optimization (ACO) algorithm based on adaptive control parameters and the SVM (support vector machine) model for correct fault bearing diagnosis was proposed [23]. Furthermore, an overview of more classical ML-based research is presented in Table 3.…”

Section: Classical ML Algorithms For Fault Bearing Diagnosismentioning

confidence: 99%

Deep Learning Aided Data-Driven Fault Diagnosis of Rotatory Machine: A Comprehensive Review

2021

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This paper presents a comprehensive review of the developments made in rotating bearing fault diagnosis, a crucial component of a rotatory machine, during the past decade. A data-driven fault diagnosis framework consists of data acquisition, feature extraction/feature learning, and decision making based on shallow/deep learning algorithms. In this review paper, various signal processing techniques, classical machine learning approaches, and deep learning algorithms used for bearing fault diagnosis have been discussed. Moreover, highlights of the available public datasets that have been widely used in bearing fault diagnosis experiments, such as Case Western Reserve University (CWRU), Paderborn University Bearing, PRONOSTIA, and Intelligent Maintenance Systems (IMS), are discussed in this paper. A comparison of machine learning techniques, such as support vector machines, k-nearest neighbors, artificial neural networks, etc., deep learning algorithms such as a deep convolutional network (CNN), auto-encoder-based deep neural network (AE-DNN), deep belief network (DBN), deep recurrent neural network (RNN), and other deep learning methods that have been utilized for the diagnosis of rotary machines bearing fault, is presented.

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Section: Classical ML Algorithms For Fault Bearing Diagnosismentioning

confidence: 99%

Deep Learning Aided Data-Driven Fault Diagnosis of Rotatory Machine: A Comprehensive Review

2021

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Explainable AI for Bearing Fault Detection Systems: Gaining Human Trust

Sinha¹,

Ahmed²,

Das³

2023

2023 IEEE Guwahati Subsection Conference (GCON)

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A fault diagnosis method of rolling element bearing based on improved PSO and BP neural network

Song

Wang

Liu

et al. 2022

IFS

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Aiming at the inherent defects of BP neural network in the field of rolling bearing fault diagnosis, based on the optimization of particle swarm optimization algorithm, this paper uses a variety of optimization strategies to optimize the particle swarm optimization algorithm, and then uses the optimized particle swarm optimization algorithm to optimize the BP neural network. Therefore, a new fault diagnosis method (Dual Strategy Particle Swarm Optimization BP neural network, DSPSOBP) is proposed. DSPSOBP fault diagnosis method is mainly divided into two steps. The first step is EMD decomposition of vibration signal, and the second step is to classify rolling bearing faults by using BP neural network optimized by Double Strategy Particle Swarm Optimization algorithm. Experiments show that DSPSOBP has stronger advantages than BP neural network basic fault diagnosis model.

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Study on A Fault Diagnosis Method of Rolling Element Bearing Based on Improved ACO and SVM Model

Abstract: Abstract

Cited by 3 publications

References 24 publications

Deep Learning Aided Data-Driven Fault Diagnosis of Rotatory Machine: A Comprehensive Review

Deep Learning Aided Data-Driven Fault Diagnosis of Rotatory Machine: A Comprehensive Review

Explainable AI for Bearing Fault Detection Systems: Gaining Human Trust

A fault diagnosis method of rolling element bearing based on improved PSO and BP neural network

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