Stress Wave Analysis Based Prognostic Health Management. Issue 3 (1st May 2018)

• Record Type: Journal Article
• Authors: Wu, Tianshu; Chen, Shuyu; Sal, Tareen; Wu, Peng
• Is Part Of: Chinese journal of electronics. Volume 27:Issue 3(2018)
• Page Start: 565
• Page End: 572
• Publication Date: 2018-05-01
• Subjects: Stress wave analysis -- Feature extraction software -- Polynomial neural network (PNN) -- Data fusion; aircraft maintenance -- condition monitoring -- fault diagnosis -- feature extraction -- maintenance engineering -- neural nets -- sensor fusion; equipment maintenance cost -- stress wave analysis -- prognostic health management -- stress wave sensor -- mechanical shock -- dynamic load -- time domain -- frequency domain feature extraction software -- data fusion technology -- equipment status -- equipment fault -- current adopted other analysis technologies; Electronics -- Periodicals; Electronics -- China -- Periodicals; Electronics; China; Periodicals; 621.38105
• DOI: 10.1049/cje.2018.02.013
• Languages: English
• ISSNs: 1022-4653

Abstract

Abstract : The stress wave sensor detect and process the electronic signal of friction, mechanical shock and dynamic load on equipment moving parts, the stress wave analysis are fulfilled by using the time domain and frequency domain feature extraction software, Polynomial neural network (PNN) and data fusion technology. The equipment status are quantitatively analyzed, the equipment fault are accurately predicted. Compared with the current adopted other analysis technologies, the system can monitor the operation condition of the equipment better in real‐time, predict the fault earlier. The production safety is guaranteed, the equipment maintenance cost is reduced, and the production efficiency is improved.