Design of a progressive fault diagnosis system for hydropower units considering unknown faults Article Swipe
Jinbao Chen
,
Yang Zheng
,
Xiaoqin Deng
,
Yunhe Wang
,
Wenqing Hu
,
Zhihuai Xiao
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1088/1361-6501/ad01cf
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1088/1361-6501/ad01cf
To address the misidentification problem of signals containing unknown faults for hydropower units, a progressive fault diagnosis system is designed. Firstly, in view of the non-stationary and nonlinear vibration signals of hydropower units, the method of complementary ensemble empirical mode decomposition is used to process the normal and fault vibration signal samples, and the intrinsic mode function (IMF) and residual components with different frequencies are obtained. Then the IMF energy moment is calculated and used as the feature vector. Furthermore, a classifier (IMF-K1) is constructed based on the feature vector samples of the normal vibration signals of hydropower units, fault symptom indicators, and K-means algorithm to determine whether the hydropower unit is faulty; a classifier (IMF-K2) is constructed based on the feature vector samples of the fault vibration signals of hydropower units, fault symptom indicators, and K-means algorithm to determine whether the hydropower unit has the known fault; a classifier (IMF-bidirectional long short-term memory neural network (BiLSTMNN)) is constructed to distinguish the fault type of hydropower units by combining the eigenvector samples of known fault vibration signals, fault symptom indicators, and BiLSTMNN. Finally, a progressive fault diagnosis system for hydropower units is constructed using IMF-K1, IMF-K2, and IMF-BiLSTMNN, and comparative experiments are designed using the sample data from the rotor test bench and actual hydropower unit. The results show that the designed progressive fault diagnosis system has greater effectiveness in mining signal features and high fault diagnosis accuracy.
Related Topics
Concepts
Hydropower
Hilbert–Huang transform
Fault (geology)
Vibration
Computer science
Control theory (sociology)
Support vector machine
Classifier (UML)
Feature vector
Pattern recognition (psychology)
Algorithm
Artificial intelligence
Engineering
Mathematics
Statistics
Energy (signal processing)
Geology
Acoustics
Seismology
Physics
Electrical engineering
Control (management)
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1361-6501/ad01cf
- OA Status
- hybrid
- Cited By
- 13
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387484004
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4387484004Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1088/1361-6501/ad01cfDigital Object Identifier
- Title
-
Design of a progressive fault diagnosis system for hydropower units considering unknown faultsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-10-10Full publication date if available
- Authors
-
Jinbao Chen, Yang Zheng, Xiaoqin Deng, Yunhe Wang, Wenqing Hu, Zhihuai XiaoList of authors in order
- Landing page
-
https://doi.org/10.1088/1361-6501/ad01cfPublisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1088/1361-6501/ad01cfDirect OA link when available
- Concepts
-
Hydropower, Hilbert–Huang transform, Fault (geology), Vibration, Computer science, Control theory (sociology), Support vector machine, Classifier (UML), Feature vector, Pattern recognition (psychology), Algorithm, Artificial intelligence, Engineering, Mathematics, Statistics, Energy (signal processing), Geology, Acoustics, Seismology, Physics, Electrical engineering, Control (management)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
13Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 6, 2024: 7Per-year citation counts (last 5 years)
- References (count)
-
27Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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