A data-driven method for evaluating the service quality status of complex electromechanical systems

Abstract

The invention discloses a data-driven complex electromechanical system service quality state evaluation method. Taking DCS state monitoring data and system service management data reflecting the system service quality state as the basis and depending on an information fusion theory, the data-driven complex electromechanical system service quality state evaluation method can perform real-time evaluation and early warning on the complex electromechanical system service quality state of an enterprise, and can perform early warning prevention on possible accidents in advance, and can assist scientific maintenance and repairing so as to improve the safety production level of the enterprise.

Description

technical field [0001] The invention belongs to the field of service quality state monitoring and analysis of complex electromechanical systems, and relates to a system service quality state evaluation method, in particular to a data-driven complex electromechanical system service quality state evaluation method. Background technique [0002] Frequent safety accidents have become a very disharmonious factor in the current construction of a harmonious society in our country. However, due to the complexity of the process and system of the process industry, people cannot accurately grasp the root cause and mechanism of the accident, and cannot understand the production system. Accurate analysis and early warning of service quality status cannot eliminate and prevent potential hazards through scientific maintenance and repair, resulting in the failure of effective containment and improvement of the safety production situation of enterprises, making it necessary and urgent to carr...

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Problems solved by technology

[0004] From the perspective of the system, a complex electromechanical system is a distributed complex electromechanical system that has many large-scale dynamic mechanical devices coupled through multi-media networks such as energy, fluid, electricity, and control signals. Three problems: (1) The system has many points and long lines, and there are many influencing factors, and the coupling relationship between the monitoring variables of the system’s service quality status is complex, making it difficult to accurately evaluate the system’s service quality status; (2) The system’s service quality status monitoring data has very Due to the coexistence of multiple features such as linearity and non-stationarity, there is a lack of effective means to effectively distinguish different service quality states; (3) there is still a lack of mature and effective system service quality state evaluation devices and methods to evaluate the system service quality state. Comprehensive Analysis and Evaluation

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