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

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%.

Research on Fault Prediction Technology of Complicated Equipment

2013 ◽  
Vol 347-350 ◽  
pp. 448-452 ◽  
Author(s):  
Sai Sai Jin ◽  
Kao Li Huang ◽  
Guang Yao Lian ◽  
Bao Chen Li
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Least Square ◽  
Fault Prediction ◽  
Support Vector ◽  
Hydraulic Pump ◽  
Fault Information

For the problems of not enough fault information for the complicated equipment and difficult to predict the fault, we apply Support Vector Machine (SVM) to build the fault prediction model. On the basis of analyzing regression algorithm of SVM, we use Least Square Support Vector Machine (LS-SVM) to build the fault prediction model.LS-SVM can effectively debase the complication of the model. Finally, we take the fault data of a hydraulic pump to validate this model. By selecting appropriate parameters, this model can make better prediction for the fault data, and it has higher prediction precision. It is proved that the fault prediction model which based on LS-SVM can make better prediction for fault trend of complicated equipment.

A threshold sensitive failure prediction method using support vector machine

2017 ◽  
Vol 13 (2) ◽  
pp. 97-111 ◽  
Author(s):  
Ahmad Fadaei Tehrani ◽  
Faramarz Safi-Esfahani
Keyword(s):  
Support Vector Machine ◽  
Failure Prediction ◽  
Prediction Method ◽  
Support Vector

A Study on Failure Prediction of Airborne Electronic Equipment

2007 ◽  
Vol 353-358 ◽  
pp. 2892-2895
Author(s):  
Hong Peng Li ◽  
Yu Ting He ◽  
Rong Shi ◽  
Heng Xi Zhang ◽  
Feng Li
Keyword(s):  
Support Vector Regression ◽  
Failure Probability ◽  
Failure Prediction ◽  
Working Time ◽  
Electronic Equipment ◽  
Support Vector ◽  
Airborne Radar ◽  
Equipment Failure ◽  
Maintenance System ◽  
Auto Regressive

The mostly working time of airborne electronic equipment is under preliminary depletion failure phase, and inspection & maintenance at intervals can’t lower the failure probability. In this paper, the law of airborne electronic equipment failure is introduced firstly. Then, methods for failure prediction are summarized and analyzed. Finally, an example for predicting the airborne radar failure using the Auto-Regressive (AR) and Support Vector Regression (SVR) model is presented. On this basis, it is possible to achieve the goal that increases the reliability in working phase and establish a more scientific maintenance system and to assure the safety of airborne electronic equipment.

Multi-model and multi-level aluminum electrolytic fault diagnosis method

2019 ◽  
Vol 41 (15) ◽  
pp. 4409-4423
Author(s):  
Jiejia Li ◽  
Tianhao Gao ◽  
Xinyang Ji
Keyword(s):  
Prediction Method ◽  
Principal Component ◽  
Aluminum Electrolysis ◽  
Decision Fusion ◽  
Fault Prediction ◽  
Dual Model ◽  
Effective Manner ◽  
Diagnosis Method ◽  
Multi Level ◽  
Fault Information

A multi-model and multi-level aluminum electrolytic fault prediction method is proposed. In this method, it innovatively uses the image recognition technology to predict aluminum electrolytic faults, and superimposes the chaotic neural network model to form a dual-model parallel fault prediction system for aluminum electrolysis, which can obtain more faults information from different angles. Then, it designs the decision fusion layer, which combines the prediction results of the above two models to output the final prediction results and enhances the credibility of the prediction results. In addition, the data processing stage also uses principal component analysis (PCA) to extract the main features of fault information, which reduces the data dimension and speeds up the processing. Experimental results suggest that the proposed algorithm can predict faults in an effective manner, and outperform other algorithms in terms of accuracy, sensitivity and stability.

scholarly journals Fault Prediction for Nonlinear System Using Sliding ARMA Combined with Online LS-SVR

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Shengchao Su ◽  
Wei Zhang ◽  
Shuguang Zhao
Keyword(s):  
Time Series ◽  
Nonlinear System ◽  
External Disturbance ◽  
Time Series Prediction ◽  
Prediction Method ◽  
Arma Model ◽  
Estimation Algorithm ◽  
Nonlinear Time Series ◽  
Fault Prediction ◽  
Support Vector

A robust online fault prediction method which combines sliding autoregressive moving average (ARMA) modeling with online least squares support vector regression (LS-SVR) compensation is presented for unknown nonlinear system. At first, we design an online LS-SVR algorithm for nonlinear time series prediction. Based on this, a combined time series prediction method is developed for nonlinear system prediction. The sliding ARMA model is used to approximate the nonlinear time series; meanwhile, the online LS-SVR is added to compensate for the nonlinear modeling error with external disturbance. As a result, the one-step-ahead prediction of the nonlinear time series is achieved and it can be extended ton-step-ahead prediction. The result of then-step-ahead prediction is then used to judge the fault based on an abnormity estimation algorithm only using normal data of system. Accordingly, the online fault prediction is implemented with less amount of calculation. Finally, the proposed method is applied to fault prediction of model-unknown fighter F-16. The experimental results show that the method can predict the fault of nonlinear system not only accurately but also quickly.

Trend Prediction of Hydraulic Liquid Leakage Based on ES-LSSVM

2011 ◽  
Vol 137 ◽  
pp. 440-444 ◽  
Author(s):  
Zhi Yong Wu ◽  
Zeng Bing Xu ◽  
Ming Gao
Keyword(s):  
Support Vector Machine ◽  
Hydraulic System ◽  
Prediction Method ◽  
Least Square ◽  
Fault Prediction ◽  
Evolutionary Strategy ◽  
Support Vector ◽  
Short Term ◽  
Trend Prediction ◽  
Internal Parameters

A novel prediction method which combined evolutionary strategy with least-square support vector machine is presented and applied to the trend prediction of hydraulic liquid leakage in this paper. In order to improve the prediction performance, the evolutionary strategy is employed to optimize the internal parameters of least-square support vector machine. Through the experiment study, the result validated the effectiveness of the prediction method, and it is also demonstrated that the method is able to do the short-term fault prediction for the hydraulic system.

Maintenance Automation Architecture and Electrical Equipment Fault Prediction Method in Tanzania Secondary Distribution Networks

2021 ◽  
Vol 47 (3) ◽  
pp. 1138-1153
Author(s):  
Hadija Mbembati ◽  
Kwame Ibwe ◽  
Baraka Maiseli
Keyword(s):  
Support Vector Machine ◽  
Power Systems ◽  
Network Architecture ◽  
Prediction Method ◽  
Distribution Networks ◽  
Fault Prediction ◽  
Machine Learning Techniques ◽  
Fault Identification ◽  
Support Vector ◽  
Secondary Distribution

Distribution networks remain the most maintenance-intensive parts of power systems. The implementation of maintenance automation and prediction of equipment fault can enhance system reliability while reducing the overall costs. In Tanzania, however, maintenance automation has not been deployed in secondary distribution networks (SDNs). Instead, traditional methods are used for condition prediction and fault identification of power assets (transformers and power lines). These (manual) methods are costly and time-consuming, and may introduce human-related errors. Motivated by these challenges, this work introduces maintenance automation into the network architecture by implementing effective maintenance and fault identification methods. The proposed method adopts machine learning techniques to develop a novel system architecture for maintenance automation in the SDN. Experimental results showed that different transformer prediction methods, namely support vector machine, kernel support vector machine, and multi-layer artificial neural network, give performance values of  96.72%, 97.50%, and 97.53%, respectively. Furthermore, oil based performance analysis was done to compare the existing methods with the proposed method. Simulation results showed that the proposed method can accurately identify up to ten transformer abnormalities. These results suggest that the proposed system may be integrated into a maintenance scheduling platform to reduce unplanned maintenance outages and human maintenance-related errors. Keywords: Predictive maintenance; fault identification; fault prediction; maintenance automation; secondary electrical distribution network

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