Research on prediction model and method for equipment state and fault in test range

Wang, Jiongqi; Hou, Bowen; He, Zhangming; Zhou, Hongyan; Zhou, Xuanying

doi:10.1109/phm.2016.7819922

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The Theory Of Neural Network -Markov1

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2023

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Cited by 2 publications

(2 citation statements)

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“…The Markov chain can be described by the following parameters [7] and the concrete are expressed as formula (1): ( , , , )…”

Section: The Theory Of Neural Network -Markovmentioning

confidence: 99%

Fault Prediction Method of the Marine Gas Turbine Based on Neural Network-Markov

Cui

Zhang

Wang³

et al. 2014

AMM

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Since the fault of marine gas turbine is difficult to predict accurately, making the rolling bearing as the specific object, a fault prediction model of the marine gas turbine based on Neural Network and Markov method is built through the data analysis, preprocessing and feature extraction for the rolling bearing history test data. First, it uses the neural network method to realize the health state recognition of the marine gas turbine. Then, the fault of the marine gas turbine is predicted by taking advantage of the fault prediction which is based on the Markov model. The results show that the efficiency of fault prediction for the marine gas turbine can be realized better through the fault prediction model constructed in view of the Neural Network and Markov. And it also has a significant practical value in project item.

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“…The Markov chain can be described by the following parameters [7] and the concrete are expressed as formula (1): ( , , , )…”

Section: The Theory Of Neural Network -Markovmentioning

confidence: 99%

Fault Prediction Method of the Marine Gas Turbine Based on Neural Network-Markov

Cui

Zhang

Wang³

et al. 2014

AMM

View full text Add to dashboard Cite

show abstract

“…At present, most countries are encouraging the development of artificial intelligence and big data technology. In the direction of power equipment fault prediction, a large number of researches on the application of big data have been carried out and achieved fruitful results [3][4][5][6][7] . Compared with the traditional empirical knowledge and basic mathematical model for power equipment quality prediction, some scholars have applied neural network to data prediction of power equipment [8][9] .…”

Section: Introductionmentioning

confidence: 99%