Gearbox fault diagnosis through quantum particle swarm optimization algorithm and kernel extreme learning machine
Abstrak
Gearbox is the key component of mechanical transmission system. Accurate fault diagnosis of gearbox is of great significance to ensure the operation of rotating machinery. Based on the comprehensive simulation test-bed in the laboratory, a gearbox fault diagnosis method based on QPSO-KELM is proposed. Firstly, the fault pre planting experiments of gear fault, bearing fault and gear bearing mixed fault are carried out on the comprehensive simulation test-bed. Then, the vibration signals collected are preprocessed by TSA to eliminate noise. The time domain, frequency domain and NASA feature parameters of the preprocessed signals are taken as training samples and test samples of QPSO-KELM. The experimental results show that the proposed method can effectively solve the problem of gearbox fault pattern recognition, and the fault diagnosis accuracy is higher than traditional methods, so the research has certain reference significance and engineering application value.
Topik & Kata Kunci
Penulis (4)
Shuo Meng
Jianshe Kang
Kuo Chi
Xupeng Die
Akses Cepat
- Tahun Terbit
- 2020
- Bahasa
- en
- Total Sitasi
- 7×
- Sumber Database
- Semantic Scholar
- DOI
- 10.21595/JVE.2020.21550
- Akses
- Open Access ✓