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Cited by 35Machine Fault Diagnosis TechniquesGear and Bearing Dynamics AnalysisStructural Health Monitoring Techniques

Wind Turbine Planetary Gearbox Fault Diagnosis Based on Self-Powered Wireless Sensor and Deep Learning Approach

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Lu, Li, He, Yigang, Wang, Tao, Shi, Tiancheng, Ruan, Yi·2019·IEEE Access
DOI 10.1109/access.2019.2936228
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Abstract

In this paper, a novel and more effective fault diagnosis approach for wind turbine planetary gearbox (PGB) is proposed. In order to better detect the faults in the early stage of the faults of the wind turbine PGB, the corresponding maintenance measures can be carried out to prevent the faults from becoming more serious, so as to seriously affect the normal operation of the fan gearbox. The gear with lighter fault degree is used to simulate the early fault signal. Compared with the fault which has seriously affected the normal working condition, the fault characteristics of the early fault signal are more difficult to detect. So in this design, deep belief network (DBN) optimized by quantum particle swarm optimization (QPSO) algorithm is used to extract deeper and more identifiable features of slight fault signal. After optimization by QPSO algorithm, DBN can get a most suitable structure according to the actual working signal of fan gearbox. Then these extracted features are input into the least squares support vector machine (LSSVM) optimized by QPSO for fault diagnosis test. At the same time, the wireless sensor nodes using self-energy in vibration state are optimized. By using microcontroller unit (MCU) MSP430F149 and nRF24L01 radio frequency (RF) chip with lower energy consumption, the normal dormant state can be maintained, the power requirement of transmission mode can be met, the stability of the whole node can be improved, and the phenomenon of energy shortage caused by short-term fluctuation can be prevented. The comparative experiments in this paper show that this method has good effect on the fault diagnosis of wind turbine PGB.

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Cited by

Sorted by citations

Authors
Lu, L., He, Y., Wang, T., Shi, T., & Ruan, Y.
Affiliations
  • Wuhan University (CN)
  • Hefei University of Technology (CN)
ORCID
Venue
IEEE Access
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2019
Volume
7
Pages
119430-119442
ISSN
2169-3536 (ISSN-L)
DOI
10.1109/access.2019.2936228
OpenAlex
W2969314380
Cited by
35
References
0
Citations by year
2026: 2 · 2025: 5 · 2024: 2 · 2023: 5 · 2022: 8 · 2021: 9 · 2020: 3 · 2019: 1
FWCI
2.91
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Access (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Natural Science Foundation of China (51577046)
  • State Key Program of National Natural Science Foundation of China (51637004)
  • National Key Research and Development Plan, Important Scientific Instruments and Equipment Development (2016YFF0102200)
  • Equipment Research Project in Advance (41402040301)
Topics
  • Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
  • Physical Sciences › Engineering › Mechanical Engineering › Gear and Bearing Dynamics Analysis
  • Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
Keywords
fault diagnosis, early fault detection, deep belief network, quantum-behaved particle swarm optimization, least squares support vector machine, self-powered wireless sensors, vibration signal feature extraction, low-power sensor nodes, wind turbine maintenance

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