Fault-identification-based fan energy consumption monitoring system
A technology of energy consumption monitoring and fault identification, which is applied in the direction of measuring electricity, measuring devices, measuring electrical variables, etc., and can solve problems such as unsuitable all-weather energy efficiency monitoring
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Publication Date
- 2015-10-28
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Abstract
Description
[0001] This application is a divisional application with application number: 201410257423.1, application date: 2014.6.11, titled "Method and System for Monitoring Fan Energy Consumption". technical field
[0002] The invention relates to a method and system for monitoring energy consumption of a fan. Background technique
[0003] Fans, motors, pumps, and compressors are collectively referred to as "industrial motor systems" by the International Energy Agency (IEA). The National Development and Reform Commission's "Eleventh Five-Year" energy-saving plan pointed out that the industrial motor system is the main electricity user in China, accounting for more than 50% of the total electricity consumption, of which the electricity consumption of fans accounts for 10.4% of the national electricity consumption. Therefore, the improvement of fan efficiency is of great significance to saving electric energy.
[0004] The fan system has a large volume and a wide range, and has a huge ...
Examples
Embodiment Construction
[0034] A fan energy consumption monitoring system, comprising three three-axis acceleration sensors 1 respectively placed on a bearing housing shell, a motor shell, and a fan shell, and two mutually perpendicular eddy current sensors 2 in the vertical plane of the rotating shaft, and the three-axis acceleration sensor , The eddy current sensor is connected with the signal processing and feature extraction module, and the signal processing and feature extraction module is connected with the neural network-based classification and identification module. "Signal processing and feature extraction module" and "neural network-based classification and recognition module" all rely on hardware facilities such as PC or high-performance controller (such as FPGA, etc.), and software realizes denoising, quaternion PCA feature extraction, axis Heart trajectory feature extraction, pattern recognition based on multi-weight neural network.
[0035] 1. First, collect offline training samples. T...