The Ecodesign of Complex Electromechanical Systems: Prioritizing and Balancing Performance Fields, Contributors and Solutions

2013 ◽  
pp. 257-279
Author(s):  
S. Esteves ◽  
M. Oliveira ◽  
F. Almeida ◽  
A. Reis ◽  
J. Pereira
Keyword(s):  
Electromechanical Systems ◽  
Complex Electromechanical Systems

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.

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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