Bearing Fault Diagnosis Based On Reinforcement Learning And Kurtosis

Dai, Wenxin; Mo, Zhenling; Luo, Chong; Jiang, Jia; Miao, Qiang

doi:10.1109/phm-qingdao46334.2019.8942977

Cited by 5 publications

(2 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The echo signal, reflected from the defect, and the so-called bottom signals that run around a full circle, are recorded by the transceiver head. In this case, the useful signal is amplified, passes through an electronic filter, and then enters the micro-processor of the module [31][32]. Here it is assessed according to various criteria, and the ratio of the amplitudes of the signals reflected from the defect and the bottom signals serves as a measure for assessing the depth of the crack in the metal layer adjacent to the wheel tread (Figures 4 and 5).…”

Section: Findings and Discussionmentioning

confidence: 99%

Increasing the Traffic Safety Level of Rolling Stock by Wheel Condition Monitoring Using an Automated Measuring Complex

Kliuiev,

Semenov,

Mikhailov

et al. 2024

Communications

View full text Add to dashboard Cite

Section: Findings and Discussionmentioning

confidence: 99%

Increasing the Traffic Safety Level of Rolling Stock by Wheel Condition Monitoring Using an Automated Measuring Complex

Kliuiev,

Semenov,

Mikhailov

et al. 2024

Communications

View full text Add to dashboard Cite

“…Other important details are shown in Table 9. The researchers used the RL system with kurtosis as an index in [139] for MFD. The dataset used for carrying out this research was other than the CWRU dataset.…”

Section: F Reinforcement Learningmentioning

confidence: 99%

Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review

2020

View full text Add to dashboard Cite

A smart factory is a highly digitized and connected production facility that relies on smart manufacturing. Additionally, artificial intelligence is the core technology of smart factories. The use of machine learning and deep learning algorithms has produced fruitful results in many fields like image processing, speech recognition, fault detection, object detection, or medical sciences. With the increment in the use of smart machinery, the faults in the machinery equipment are expected to increase. Machinery fault detection and diagnosis through various deep learning algorithms has increased day by day. Many types of research have been done and published using both open-source and closed-source datasets, implementing the deep learning algorithms. Out of many publicly available datasets, Case Western Reserve University (CWRU) bearing dataset has been widely used to detect and diagnose machinery bearing fault and is accepted as a standard reference for validating the models. This paper summarizes the recent works which use the CWRU bearing dataset in machinery fault detection and diagnosis employing deep learning algorithms. We have reviewed the published works and presented the working algorithm, result, and other necessary details in this paper. This paper, we believe, can be of good help for future researchers to start their work on machinery fault detection and diagnosis using the CWRU dataset. INDEX TERMS Bearing, deep learning, machine learning, machinery fault detection and diagnosis, CWRU dataset.

show abstract

A Review of Predictive Maintenance of Bearing Failures in Rotary Machines by Predictive Analytics Using Machine-Learning Techniques

Aldeoes,

Gokhale,

Sondkar

2023

Signals and Communication Technology

View full text Add to dashboard Cite

Bearing Fault Diagnosis Based On Reinforcement Learning And Kurtosis

Cited by 5 publications

References 13 publications

Increasing the Traffic Safety Level of Rolling Stock by Wheel Condition Monitoring Using an Automated Measuring Complex

Increasing the Traffic Safety Level of Rolling Stock by Wheel Condition Monitoring Using an Automated Measuring Complex

Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review

A Review of Predictive Maintenance of Bearing Failures in Rotary Machines by Predictive Analytics Using Machine-Learning Techniques

Contact Info

Product

Resources

About