Commutation failure prediction method based on characteristic of accumulated energy in inverter

A novel failure prediction method of the rotating machinery is presented in this paper. Firstly, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is applied to decompose the vibration signals of the rotating machinery into a number of intrinsic mode functions (IMFs) and a residual (Res), and the metric of maximal information coefficient (MIC) is used to select eligible IMFs to reconstruct signals. Then, the approximate entropy (ApEn)-weighted energy value of the reconstructed signals are calculated to track the degradation process of the rotating machinery. Furthermore, the Chebyshev inequality is introduced to determine the prediction starting time (PST). Finally, the auto regress (AR) model and unscented Kalman filter (UKF) algorithm are used to predict the remaining useful life (RUL) of the rotating machinery. The method is fully evaluated in a test-to-failure experiment. The obtained results show that the proposed method outperforms its counterparts on failure prediction of the rotating machinery.

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Failure prediction method of gearbox based on BP neural network with genetic optimization algorithm

International Conference on Renewable Power Generation (RPG 2015) ◽

10.1049/cp.2015.0444 ◽

2015 ◽

Author(s):

Rui Jiang ◽

Zhuang Li ◽

Yufeng Ma ◽

Chao Liu ◽

Xiaolong Zhang

Keyword(s):

Neural Network ◽

Optimization Algorithm ◽

Bp Neural Network ◽

Failure Prediction ◽

Prediction Method ◽

Genetic Optimization ◽

Genetic Optimization Algorithm

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Electronic Equipment Combination Fault Prediction Technology Research Based on LSSVM-HMM

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.687-691.978 ◽

2014 ◽

Vol 687-691 ◽

pp. 978-983

Author(s):

Yan Ping Tian ◽

Xiao Hui Ye ◽

Ming Yin

Keyword(s):

Failure Prediction ◽

Prediction Method ◽

Electronic Equipment ◽

Condition Based Maintenance ◽

Fault Prediction ◽

Support Vector ◽

Technology Research ◽

State Prediction ◽

Fault State ◽

Fault Information

In order to solve the problem of complicated electronic equipment structure, inadequate fault information, hard to predict the fault and the existing failure prediction method cannot predict the state of the electronic equipment and other issues directly, we propose a combination failure prediction methods of least squares support vector machine (LSSVM) and hidden Markov model (HMM) based on Condition Based Maintenance (CBM). First, according to sensitivity analysis to determine the circuit elements to be changed to set the circuit by changing the parameters of the different components degraded state; secondly, create a combination failure prediction model; Finally, the circuit state prediction. The results show that the proposed method can directly predict the different states of the circuit, so as to realize the fault state prediction of the electronic equipment directly, the prediction accuracy can reach 93.3%.

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