Internal Combustion Engine Fault Identification Based on FBG Vibration Sensor and Support Vector Machines Algorithm Article Swipe
Faye Zhang
,
Mingshun Jiang
,
Lei Zhang
,
Shaobo Ji
,
Qingmei Sui
,
Chenhui Su
,
Shanshan Lv
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1155/2019/8469868
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1155/2019/8469868
State monitoring and fault diagnosis of an internal combustion engine are critical for complex machinery safety. In the present study, a high‐frequency vibration system was proposed based on Fiber Bragg Grating (FBG) cantilever sensor and intelligent algorithm. Structural vibration signal containing fault information of engine valves and oil nozzle was identified by FBG sensors and preprocessed using wavelet decomposition and reconstruction. Moreover, vibration energy was taken as fault characteristics. Subsequently, a fault identification model was built based on multiclass υ ‐support vector classification ( υ ‐SVC). Experimental tests on the valve fault and fuel injection advance angle fault were performed and presented to verify the efficacy of the proposed approach. The results here reveal that the proposed method exhibits excellent fault detection performance for ICE fault identification. Furthermore, the proposed method can achieve higher performance than other methods in the fault identification accuracy.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/2019/8469868
- http://downloads.hindawi.com/journals/mpe/2019/8469868.pdf
- OA Status
- hybrid
- Cited By
- 15
- References
- 24
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2992408053
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2992408053Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1155/2019/8469868Digital Object Identifier
- Title
-
Internal Combustion Engine Fault Identification Based on FBG Vibration Sensor and Support Vector Machines AlgorithmWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2019Year of publication
- Publication date
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2019-01-01Full publication date if available
- Authors
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Faye Zhang, Mingshun Jiang, Lei Zhang, Shaobo Ji, Qingmei Sui, Chenhui Su, Shanshan LvList of authors in order
- Landing page
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https://doi.org/10.1155/2019/8469868Publisher landing page
- PDF URL
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https://downloads.hindawi.com/journals/mpe/2019/8469868.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
- OA URL
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https://downloads.hindawi.com/journals/mpe/2019/8469868.pdfDirect OA link when available
- Concepts
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Fault (geology), Vibration, Engineering, Support vector machine, Fault detection and isolation, Wavelet, SIGNAL (programming language), Algorithm, Computer science, Acoustics, Artificial intelligence, Actuator, Electrical engineering, Seismology, Geology, Programming language, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
15Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 1, 2023: 5, 2022: 2, 2021: 1Per-year citation counts (last 5 years)
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24Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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