Intelligent fault diagnosis for rotating machinery using deep Q-network based health state classification: A deep reinforcement learning approach

2019 ◽  
Vol 42 ◽  
pp. 100977 ◽  
Author(s):  
Yu Ding ◽  
Liang Ma ◽  
Jian Ma ◽  
Mingliang Suo ◽  
Laifa Tao ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Fault Diagnosis ◽  
Rotating Machinery ◽  
Learning Approach ◽  
Health State ◽  
Intelligent Fault Diagnosis ◽  
State Classification ◽  
Health State Classification

Construction of a deep sparse filtering network for rotating machinery fault diagnosis

2021 ◽  
pp. 095440702110148
Author(s):  
Chun Cheng ◽  
Wei Zou ◽  
Weiping Wang ◽  
Michael Pecht
Keyword(s):  
Fault Diagnosis ◽  
Back Propagation ◽  
Feature Learning ◽  
Back Propagation Neural Network ◽  
Rotating Machinery ◽  
Fine Tuning ◽  
Local Optimum ◽  
Back Propagation Algorithm ◽  
Intelligent Fault Diagnosis ◽  
Sparse Filtering

Deep neural networks (DNNs) have shown potential in intelligent fault diagnosis of rotating machinery. However, traditional DNNs such as the back-propagation neural network are highly sensitive to the initial weights and easily fall into the local optimum, which restricts the feature learning capability and diagnostic performance. To overcome the above problems, a deep sparse filtering network (DSFN) constructed by stacked sparse filtering is developed in this paper and applied to fault diagnosis. The developed DSFN is pre-trained by sparse filtering in an unsupervised way. The back-propagation algorithm is employed to optimize the DSFN after pre-training. Then, the DSFN-based intelligent fault diagnosis method is validated using two experiments. The results show that pre-training with sparse filtering and fine-tuning can help the DSFN search for the optimal network parameters, and the DSFN can learn discriminative features adaptively from rotating machinery datasets. Compared with classical methods, the developed diagnostic method can diagnose rotating machinery faults with higher accuracy using fewer training samples.

Methods for obtaining health state utility values: generic preference-based measures of health

2016 ◽  
Author(s):  
John Brazier ◽  
Julie Ratcliffe ◽  
Joshua A. Salomon ◽  
Aki Tsuchiya
Keyword(s):  
Health State Utility ◽  
Health State ◽  
Multiattribute Utility ◽  
Health States ◽  
Utility Values ◽  
Published Evidence ◽  
State Classification ◽  
Health State Utility Values ◽  
Generic Measures ◽  
Health State Classification

This chapter describes the six most widely used generic preference-based measures of health (GPBMs) (also known as multiattribute utility scales): EQ-5D, SF-6D, HUI, AQoL, 15D, and QWB. GPBMs have become the most widely used method for obtaining health state utility values. They contain a health state classification with multilevel dimensions that together describe a universe of health states and a set of values (where full health = 1 and dead = 0) for each health state obtained by eliciting the preferences (typically) of members of the general population. These measures are reviewed in terms of their content, methods of valuation, the scores they generate, and the possible reasons for the differences found. Their performance is reviewed using published evidence on their validity across conditions, and the implications for their use in policy making discussed. The chapter also reviews the generic measures available for use in populations of children and adolescents.

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