Comprehensive reliability evaluation of multistate complex electromechanical systems based on similarity of cloud models

2020 ◽  
Vol 36 (3) ◽  
pp. 1048-1073 ◽  
Author(s):  
Rongxi Wang ◽  
Xu Gao ◽  
Zhiyong Gao ◽  
Shiqiang Li ◽  
Jianmin Gao ◽  
...  
Keyword(s):  
Reliability Evaluation ◽  
Electromechanical Systems ◽  
Cloud Models ◽  
Comprehensive Reliability ◽  
Complex Electromechanical Systems

A Comprehensive Reliability Evaluation Model of Turbine Runner Blades under Complex Conditions

2020 ◽  
Vol 20 (6) ◽  
pp. 2097-2105
Author(s):  
Zhaojun Li ◽  
Xijun Mao ◽  
Fuxiu Liu ◽  
Yuyu Huang ◽  
Xing Heng
Keyword(s):  
Evaluation Model ◽  
Reliability Evaluation ◽  
Turbine Runner ◽  
Comprehensive Reliability

scholarly journals A BRB Based Fault Prediction Method of Complex Electromechanical Systems

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Bangcheng Zhang ◽  
Xiaojing Yin ◽  
Zhanli Wang ◽  
Xiaoxia Han ◽  
Zhi Gao
Keyword(s):  
Optimal Algorithm ◽  
Quantitative Information ◽  
Numerical Control ◽  
Fault Prediction ◽  
Rule Base ◽  
Cnc Milling ◽  
Electromechanical Systems ◽  
Belief Rule Base ◽  
Qualitative Knowledge ◽  
Complex Electromechanical Systems

Fault prediction is an effective and important approach to improve the reliability and reduce the risk of accidents for complex electromechanical systems. In order to use the quantitative information and qualitative knowledge efficiently to predict the fault, a new model is proposed on the basis of belief rule base (BRB). Moreover, an evidential reasoning (ER) based optimal algorithm is developed to train the fault prediction model. The screw failure in computer numerical control (CNC) milling machine servo system is taken as an example and the fault prediction results show that the proposed method can predict the behavior of the system accurately with combining qualitative knowledge and some quantitative information.

Reliability Evaluation of Fast-Charging EV Station Entry to Generation and Transmission Electrical System

2013 ◽  
Vol 860-863 ◽  
pp. 1096-1100 ◽  
Author(s):  
Hang Du ◽  
Chun Lin Guo
Keyword(s):  
Power Generation ◽  
Simulation Method ◽  
Reliability Evaluation ◽  
Transmission Power ◽  
Monte Carlo Simulation Method ◽  
Electrical System ◽  
Charging Time ◽  
Fast Charging ◽  
Comprehensive Reliability ◽  
The Impact

This paper predicts the future of rapid charging technology for EV, including its corresponding vehicle types and charging time. We choose the Monte Carlo simulation method to choose the initial charging time, initial SOC for each EV randomly, so as to get the fast charging EV load. After all these, we could have a comprehensive reliability evaluation of the generation and transmission power system includes fast charging EV load. In the end, through a matlab test example, a reliability evaluation on the impact of fast charging EV load on power generation and transmission system is made.

scholarly journals Deep-Learning-Based Methodology for Fault Diagnosis in Electromechanical Systems

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3949 ◽  
Author(s):  
Francisco Arellano-Espitia ◽  
Miguel Delgado-Prieto ◽  
Victor Martinez-Viol ◽  
Juan Jose Saucedo-Dorantes ◽  
Roque Alfredo Osornio-Rios
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Manufacturing Systems ◽  
Production Systems ◽  
Operating Conditions ◽  
Smart Manufacturing ◽  
Learning Technology ◽  
Electromechanical Systems ◽  
Monitoring Strategies ◽  
Complex Electromechanical Systems

Fault diagnosis in manufacturing systems represents one of the most critical challenges dealing with condition-based monitoring in the recent era of smart manufacturing. In the current Industry 4.0 framework, maintenance strategies based on traditional data-driven fault diagnosis schemes require enhanced capabilities to be applied over modern production systems. In fact, the integration of multiple mechanical components, the consideration of multiple operating conditions, and the appearance of combined fault patterns due to eventual multi-fault scenarios lead to complex electromechanical systems requiring advanced monitoring strategies. In this regard, data fusion schemes supported with advanced deep learning technology represent a promising approach towards a big data paradigm using cloud-based software services. However, the deep learning models’ structure and hyper-parameters selection represent the main limitation when applied. Thus, in this paper, a novel deep-learning-based methodology for fault diagnosis in electromechanical systems is presented. The main benefits of the proposed methodology are the easiness of application and high adaptability to available data. The methodology is supported by an unsupervised stacked auto-encoders and a supervised discriminant analysis.

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