Equipment state comprehensive dynamic alarming method based on multivariate probability model

Abstract

The invention relates to an equipment state comprehensive dynamic alarming method based on a multivariate probability model. Multivariate parameters representing individual equipment state during the equipment operation are used as a target sample set, the mapping of multivariate sample data to one-dimensional conditional probability density is realized through a Gaussian kernel function, contour line mapping of distribution of sample points positioned in a cluster boundary is obtained by using a 3 Sigma, an alarming line is a complex curved surface formed by enveloping contour lines of the boundary points and is updated continuously along with monitoring data, and an alarming model is dynamically adjusted, thereby realizing the self-adaptive adjustment of the alarming line along with the change of the equipment states. In the method, a law is searched in the development history of the equipment, the field conventional equipment states are divided into three levels, namely, normal state, abnormal transient state and failure state, change ranges of alarming thresholds of all states are respectively researched for laying the foundation for the comprehensive judgment of the equipment states.

Description

technical field [0001] The invention belongs to the technical field of mechanical equipment status monitoring and fault diagnosis, and relates to a mechanical equipment operation monitoring and alarm setting technology, in particular to a comprehensive dynamic alarm method for equipment status based on a multivariate probability model. Background technique [0002] Alarm technology is one of the core technologies of equipment condition monitoring and fault diagnosis. It plays an extremely important role in the process of promoting monitoring and diagnosis technology to enterprises and promoting the development and reform of predictive maintenance system. From the perspective of accident prevention, the alarm of the operation status of mechanical equipment is more urgent and effective than fault diagnosis. [0003] At present, the commonly used alarm methods are: 1) Over-limit alarm method, which monitors some key parameters of the equipment in real time. Once the parameters ...

AI Technical Summary

Problems solved by technology

The knowledge-based intelligent alarm method can adaptively judge the equipment status, delineate the dynamic alarm line, and use the deep knowledge contained in the data as the alarm rule to judge the equipment status. sample training, it is difficult to achieve real-time monitoring

In addition, the existing alarm methods mostly monitor the state of the equipment at a single measuring point, rather than grasping the state of the equipment as a whole

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