A Noise Detection Method of Rotating Machinery Monitoring Data Based on Ses-lof

Abstract

A SES-LOF-based noise detection method for rotating machinery monitoring data, first obtain a section of rotating machinery monitoring signal as the original signal, and calculate the local anomaly factor value of each signal point according to the amplitude; all signals with a local anomalous factor value greater than 1 Points are marked as suspicious noise points, and a set of suspicious noise points is obtained; then, the suspicious noise points are selected from the set, and the suspicious noise points are removed from the original signal to obtain a new signal; and then Hilbert transform is performed on the new signal successively and Fourier transform, and calculate the Shannon entropy of the new signal; then judge whether the suspicious noise is a real noise according to the 3σ criterion; after traversing all the suspicious noises in the collection, finally complete the retrieval of the real noise; the inventive method improves the Based on the traditional LOF algorithm, the ability to detect the noise of the rotating machinery monitoring data has an ideal effect on the noise detection of the rotating machinery monitoring data.

Description

technical field [0001] The invention belongs to the technical field of improving the quality of monitoring data of rotating machinery, and in particular relates to a noise point detection method of monitoring data of rotating machinery based on SES-LOF. Background technique [0002] Rotating machinery is widely used in fields such as electric power, petrochemical, metallurgy and transportation, and is an important equipment in industrial production. In order to ensure the safe and reliable operation of rotating mechanical equipment, it is necessary to monitor its health status and obtain a large amount of equipment operation monitoring data in real time. By analyzing and processing the monitoring data, equipment failures can be detected in time or even in advance. However, the working environment of rotating machinery equipment is often very harsh, resulting in a large number of drifting, distorted, and incomplete dirty data mixed in the monitoring big data. These dirty data...

AI Technical Summary

Problems solved by technology

LOF (Local Outlier Factor) is a commonly used algorithm for outlier detection. When the traditional LOF method is directly applied to the noise detection of rotating machinery monitoring data, the impact component in the rotating machinery monitoring signal is often mistakenly identified as noise. Noise elimination will destroy the typical fault information in the original data, resulting in the quality of monitoring data not improving but degrading

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