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Equipment cluster health state evaluation method based on industrial big data

The invention discloses an equipment cluster health state evaluation method based on industrial big data and solves a health state evaluation problem of equipment clusters. Based on the full service period big data environment of the equipment clusters, an entity state information slicing management model is constructed to pre-process data; a condition similarity clustering method is utilized to carry out equipment cluster division, a regression method is utilized to establish a mirror image model, the mirror image model and an entity health state are mutually mapped, and a health state quantification model of the different equipment clusters is acquired; fusion and reconstruction of the health state data of the different equipment clusters are carried out to acquire health degrees of the different pieces of equipment, fitting of an equipment health state degeneration curve is carried out, and residual service life of the equipment is predicted. The method is advantaged in that a big data clustering modeling method is utilized, not only can effectively different evaluation on the equipment cluster health state be carried out, but also model redundancy of cluster modeling can be reduced, the internal structure of the model is simplified, normal operation of the cluster equipment is guaranteed, and use values of the equipment can be mined to a maximum degree.
Owner:XIDIAN UNIV
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