Time-Domain Sparsity-Based Bearing Fault Diagnosis Methods Using Pulse Signal-to-Noise Ratio
Article
Zhang, Chi, Wei, Shaoming, Dong, Ge, Zeng, Yajun, Zhu, Guohun, Zhou, Xujuan and Liu, Feng. 2024. "Time-Domain Sparsity-Based Bearing Fault Diagnosis Methods Using Pulse Signal-to-Noise Ratio." IEEE Transactions on Instrumentation and Measurement. 73. https://doi.org/10.1109/TIM.2024.3375978
Article Title | Time-Domain Sparsity-Based Bearing Fault Diagnosis Methods Using Pulse Signal-to-Noise Ratio |
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ERA Journal ID | 979 |
Article Category | Article |
Authors | Zhang, Chi, Wei, Shaoming, Dong, Ge, Zeng, Yajun, Zhu, Guohun, Zhou, Xujuan and Liu, Feng |
Journal Title | IEEE Transactions on Instrumentation and Measurement |
Journal Citation | 73 |
Number of Pages | 4 |
Year | 2024 |
Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
Place of Publication | United States |
ISSN | 0018-9456 |
1557-9662 | |
Digital Object Identifier (DOI) | https://doi.org/10.1109/TIM.2024.3375978 |
Web Address (URL) | https://ieeexplore.ieee.org/document/10486958 |
Abstract | A fast and automated technique is crucial for bearing faults diagnosis during operation. To circumvent the intricacies of signal spectrum analysis, a diagnostic method named the pulse signal-to-noise ratio (PSNR) test is proposed by exploiting the time-domain sparsity of fault signals under a constant angular rate, which are modeled as periodic pulses with consistent duty cycle and power. The algorithm employs a statistic called PSNR to both identify faults and determine their location. A simplified variant of the PSNR test, named pulse signal-to-noise amplitude ratio (PSNAR) test, is further proposed for near multiplication-free fast diagnosis. Data from Machinery Failure Prevention Technology (MFPT) and Case Western Reserve University (CWRU) were used to verify the algorithm. |
Keywords | Bering fault diagnosis |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 460508. Information retrieval and web search |
Public Notes | The accessible file is the accepted version of the paper. Please refer to the URL for the published version. |
Byline Affiliations | Tsinghua University, China |
Beihang University, China | |
University of Queensland | |
School of Business |
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https://research.usq.edu.au/item/z85x6/time-domain-sparsity-based-bearing-fault-diagnosis-methods-using-pulse-signal-to-noise-ratio
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