Multi-mode fusion motor intelligent maintenance system

Abstract

The invention relates to the field of motor management, in particular to a multi-mode fusion motor intelligent maintenance system. The system acquires three-phase voltage and current signals, vibration signals and temperature signals of the motor through the signal acquisition module, and estimates the actual rotating speed of the motor through the motor rotating speed estimation module without additionally installing a rotating speed sensor. The motor self-learning multi-source diagnosis module in the system creatively combines three technologies, the three technologies are mutually complemented and coordinated, and when a big data analysis mode reaches the reliability level set by the system, the three modes are mutually matched, so that the reliability of the system is improved. A multi-sensor multi-element fusion diagnosis strategy with the D-S evidence theory as the core is creatively used for comprehensive diagnosis, the fault evolution process of equipment parts is continuously tracked, operation and maintenance personnel are reminded of the current health state of the equipment in time, and the current working state of the motor is comprehensively and objectively reflected.

Description

technical field [0001] The invention relates to the field of motor management, in particular to a multi-mode fusion motor intelligent maintenance system. Background technique [0002] With the improvement of industrial automation level, various motors have been widely used in various projects, and their safety, high efficiency and high reliability have become the focus of people's attention. Once a fault occurs, it will stop work and stop production, and it will cause a lot of financial losses and casualties. Therefore, the real-time monitoring of the motor is particularly important. The domestic technology for fault monitoring of electric motors started late. For a long time, DCS and other production control systems have been relied on to ensure the safety of plant equipment. This maintenance strategy of post-event maintenance has caused considerable economic and social effects for a long time. With the continuous advancement and progress of predictive maintenance strateg...

AI Technical Summary

Problems solved by technology

However, this technology does not have enough sample libraries for learning in the early stage of equipment monitoring, and a large number of parameter selections greatly affect the performance of model analysis, and its practicability is also greatly reduced

At the same time, for the motor, its complex structure makes some faults obvious in the current signal, and some fault characteristics are obvious in the vibration signal. If the two kinds of signal online monitoring technologies are used to monitor the motor at the same time, it will cause enterprises Substantial increase in maintenance costs and excessive investment of front-line operation and maintenance personnel

However, judging faults only from vibration signals or current signals has a certain degree of contingency and randomness. At the same time, insufficient samples and low data quality lead to poor model diagnosis results. These types of problems are also the main factors that cause the bottleneck of fault diagnosis technology

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