The Fault Prediction of Aerospace Equipment PHM Technology and its Demonstrated Failure Prediction Module Simulation

Abstract:

Prognostics and Health Management Technology (PHM) will make up the deficiencies of current device monitoring and fault diagnosis, especially the lack of fault prediction. Its combination with the neural network can provide a universal theory and technology of the intelligence prediction. Though the experiments, we establish the BP neural network, as well as the most suitable prediction model. After testing data verification, neural networks can accurately predict the status of the equipment, and the health trends in the future. With the network we can accurately predict the system state, remaining life for the aerospace equipment, make it possible to provide maintenance in time, reduce failure losses and improve reliability.

• Periodical: Advanced Materials Research (Volume 505)
• Pages: 239-244
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.505.239
• Online since: April 2012
• Authors: Ni Li, Zhen Hua Li
• Keywords: BP Algorithm, Fault Prediction, L-M Algorithm, Neural Network Prediction, PHM

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