Complex equipment health assessment method and system based on deep learning

Abstract

The invention discloses a complex equipment health assessment method and system based on deep learning, and belongs to the field of mechanical equipment health assessment methods. The operation stateof mechanical equipment is direct reflection of equipment health, and if equipment or parts have faults, the operation parameters of the equipment certainly deviate from normal values. In order to monitor the health state of the equipment in real time, operation parameters capable of reflecting the state of the equipment must be obtained. Based on the reasons, mechanical equipment is divided intoa power system, a transmission system, a control system, a supporting system and the like. The health state of each subsystem is evaluated, and then the health state of the whole equipment is evaluated by integrating the health states of the subsystems.

Description

technical field [0001] The invention relates to the field of mechanical equipment health assessment methods, in particular to a deep learning-based complex equipment health assessment method and system. Background technique [0002] Mechanical equipment plays a vital role in production enterprises. By monitoring the status of mechanical equipment, potential safety hazards of mechanical equipment can be discovered in advance, and decision-making information can be provided for equipment maintenance of enterprises, so as to avoid accidental downtime and ensure personnel safety, thereby Realize cost reduction and efficiency increase. Traditional mechanical equipment condition monitoring relies on the signal of a single sensor for status assessment and early warning, calculates the characteristic value based on a single type of vibration signal collected by a single sensor, and sets the alarm threshold. When the characteristic value exceeds the threshold, an alarm is issued. Se...

AI Technical Summary

Problems solved by technology

General mechanical equipment contains multiple parts, and different parts have different degrees of importance. The traditional state assessment method cannot reflect the overall operating state of the equipment, nor does it reflect the relationship between the local state and the overall state of the equipment. Therefore, the traditional The method of equipment operation status assessment has certain limitations

[0003] The existing health assessment methods and systems for mechanical equipment cannot be effectively assessed, mainly because the performance parameters are not classified during the health assessment, which leads to the inability to effectively analyze the weight of each parameter during the assessment, resulting in the actual operation results often being different from reality. big gap

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