State monitoring device of complex electromechanical system for flow industry and method

Abstract

The invention relates to a state monitoring device of a complex electromechanical system for a flow industry and a method. The device comprises a man-machine interaction module, a data collecting module, a data preprocessing module, a data analyzing module and a failure case library. According to the device and the method provided by the invention, whether the system fails or is in abnormal states or not can be monitored and early warming for tripping accidents or other accidents of the flow industry system can be made. Meanwhile, a KPCA (Kernel Principal Component Analysis) method with double parameter optimization is used to overcome the deficiency that parameters are selected by empirical formula in conventional KPCA methods, thereby improving the state monitoring ability. Furthermore, the failure case database is adequately used in the historical production process so that failures of the system can be monitored more immediately and accurately.

Description

technical field [0001] The invention belongs to the technical field of electromechanical system monitoring, and relates to a state monitoring device and method, in particular to a state monitoring device and method for complex electromechanical systems in the process industry. Background technique [0002] In the process industry, due to the increasing scale and complexity of industrial processes, the safety and reliability requirements of production systems are also increasing. The long-term safe, stable and efficient operation of the production system and the avoidance of vicious safety accidents have become an important task of modern industry. Therefore, during the operation of the system, it is necessary to detect the occurrence of faults or abnormal states in time, and to judge the type of fault and locate the source of the fault to eliminate adverse factors. Traditional condition monitoring methods can be divided into three categories: analysis-based methods, knowled...

AI Technical Summary

Problems solved by technology

In the actual state monitoring process, such a complex electromechanical system faces three problems: (1) The number of monitoring variables is huge, and there is correlation and strong coupling between variables, so it is difficult to monitor all variables at the same time by manual methods

(2) The monitoring data presents the characteristics of slow change, massiveness, nonlinearity and atypicality, etc., and there is no effective means to mine the equipment status characteristic information contained in the data

(3) The modern process industry production system is in a multi-media coupling network environment, and there is still a lack of device systems and methods for effective condition monitoring at the system level

[0006] The traditional KPCA method has the following deficiencies in practical application: (1) The selection of KPCA kernel parameters and the number of pivots is very subjective. At present, there is no unified criterion for the selection of kernel parameters, and most of them adopt the method of empirical formula

The selection of the number of pivots in KPCA generally adopts the simple and commonly used pivot cumulative contribution rate method (Cumulative percent variance, CPV), but there is no uniform standard for the most appropriate contribution rate, and the pivotal cumulative contribution rate is used in KPCA When calculating the number of pivots, it will first be affected by the selection of kernel parameters; (2) The entire monitoring process does not use known fault case data, but establishes a KPCA model based on given parameters to detect all types of faults

However, a fixed system model cannot have a good detection effect on all faults, and can only be very sensitive to one of them, or a certain type of fault.

Images