Big data sensitive characteristic optimization selecting based equipment failure early warning method and system

Abstract

The invention provides a big data sensitive characteristic optimization selecting based equipment failure early warning method and system. The method includes the following steps: collecting vibrationdata of equipment under a normal working condition, and performing time and frequency domain index feature extraction on the vibration data to form vibration-like feature vectors; applying a compensation distance assessment technique to perform optimization selecting on the vibration-like feature vectors, and commonly forming a sensitive vector set by the optimally selected vibration-like featurevectors and the process data of the equipment under the normal working condition to be a training sample which supports a vector data description model so that a SVDD hypersphere model of the equipment under the normal working condition can be formed through training; and processing testing vibration data by adopting the above same steps, and forming a testing sensitive vector set with the obtained optimized selection feature vectors and the process data under a testing working condition to input to the SVDD hypersphere model under the normal working condition, and performing early warning analysis on output results when the equipment is abnormal or is about to be abnormal. Thus, the intelligent maintenance of the equipment can be realized.

Description

technical field

[0001] The invention relates to the technical field of equipment failure early warning, in particular to an equipment failure early warning method and system based on optimal selection of sensitive features of big data. Background technique

[0002] Equipment maintenance costs account for the majority of equipment management, and traditional equipment maintenance models have problems such as insufficient maintenance or excessive maintenance: the former may cause major accidents, while the latter will increase unnecessary maintenance costs. Realizing early warning of equipment can take corresponding measures in advance in the early stage of equipment failure to avoid major accidents, and change passive maintenance into active maintenance, thereby effectively reducing enterprise equipment management costs.

[0003] Conventional early warning methods for equipment based on big data often use the original monitoring data of equipment or unoptimized extracted feat...

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