The Remote Method of Diagnosing the Technical Condition of Complex Electromechanical Systems

2018 ◽  
Author(s):  
D.M. Shprekher ◽  
E. B. Kolesnikov
Keyword(s):  
Technical Condition ◽  
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 Fuzzy cognitive modeling of heterogeneous electromechanical systems

2021 ◽  
Vol 16 (91) ◽  
pp. 32-39
Author(s):  
Vadim V. Borisov ◽  
◽  
Sergey P. Kurilin ◽  
Nikolai N. Prokimnov ◽  
Margarita V. Chernovalova ◽  
...  
Keyword(s):  
Vector Space ◽  
Cognitive Modeling ◽  
Angular Position ◽  
Technical Condition ◽  
Cognitive Models ◽  
Innovative Design ◽  
Electromechanical Systems ◽  
Problem Situations ◽  
Matrix Operations ◽  
Vector Matrix

The article presents a method of fuzzy cognitive modeling for heterogeneous electromechanical systems (HEMSs) in the management of innovative design solutions. During the operation of the HEMSs, as a result of their operational aging, the properties of the windings parametric matrices and the HEMSs vector space properties change. Periodic testing of the HEMSs vector space allows obtaining reliable information about the current technical condition of the HEMSs, about its changes during operation and about the risks of operating capability loss. At the same time (I) the presence of proportional changes in signals during sequential testing indicates the homogeneous operational aging of the HEMSs and its rate; (II) a disproportionate change in one of the signals indicates the damage or the development of a heterogeneous aging process; (III) a change in signals with a change in the angular position of the rotor indicates worn bearings or damage of the HEMSs rotor. The article presents the HEMSs model, describes the method for the topological research of the vector space and the method for forming the diagnostic matrices. The deviations of their elements are fuzzy due to the uncertainty of the load, influencing environmental factors and unstable supply voltages. Therefore, for predictive estimation of the HEMSs state, it is proposed to use fuzzy relational cognitive models that allow implementing a completely fuzzy approach to modeling problem situations in these systems. The presented data confirm the growth of the HEMSs heterogeneity under conditions of uncertainty of external influences. The proposed method for predictive estimation of the HEMSs state, based on fuzzy relational cognitive models, provides resistance to an increase in the uncertainty of the estimation results for various models of system dynamics due to a reasonable set of fuzzy vector-matrix operations.

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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