Study of prognostics for spacecraft based-on particle swarm optimized neural network

Zou, Kexu; Ma, Haodong; Fang, Hongzheng; Yi, Da-Wei

doi:10.1109/phm.2011.5939479

Cited by 3 publications

(1 citation statement)

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“…temperature) by using an RNN as a first approach, as well as a nonlinear autoregressive model with exogenous input (NARX) neural network architecture. In (Zou, Ma, Fang, & Yi, 2011), Ke-Xu et al designed a particle-swarm optimized NN for spacecraft prognostics. In (X. Li, Ding, & Sun, 2018), Li et.…”

Section: Applications Of Prognostics Data-driven Methods In the Aerospace Industrymentioning

confidence: 99%

A Review: Prognostics and Health Management in Automotive and Aerospace

Nguyen

Kefalas

Yang

et al. 2019

IJPHM

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Prognostics and Health Management (PHM) attracts increasing interest of many researchers due to its potentially important applications in diverse disciplines and industries. In general, PHM systems use real-time and historical state information of subsystems and components of the operating systems to provide actionable information, enabling intelligent decision-making for improved performance, safety, reliability, and maintainability. Every year, a substantial number of papers in this area including theory and practical applications, appear in academic journals, conference proceedings and technical reports. This paper aims to summarize and review researches, developments and recent contributions in PHM for automotive and aerospace industries. It can also be considered as the starting point for researchers and practitioners in general to assist them through PHM implementation and help them to accomplish their work more easily.

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Section: Applications Of Prognostics Data-driven Methods In the Aerospace Industrymentioning

confidence: 99%