Fault early warning method for hydraulic equipment based on fault frequent pattern

Abstract

The invention discloses a fault early warning method for hydraulic equipment based on a fault frequent pattern, and aims at providing a method which can reduce the occurrence of error reporting and failure in report of early warning and increase the accuracy rate in fault diagnosis. The key point of the technical scheme is that the method comprises the steps of preprocessing the historical monitoring data of equipment, wherein preprocessing comprises removing singular value and normalizing; establishing a fault frequent pattern mining model (FFPMM for short), and mining the historical monitoring data processed in step 1 by utilizing the fault frequent pattern mining model so as to establish a fault mode base; extracting a real-time monitoring data set of the equipment, comparing the real-time monitoring data with the fault mode base in the step 2; if the real-time monitoring data set and the fault mode base fails to be matched successfully, returning the operation to monitor the data of the equipment again; however, if the matching is successful, determining that the detected equipment is in a defect state even if still showing a fault-free state, thereby accomplishing the step 4; obtaining a potential fault occurrence probability value by taking the fault mode base for reference, and then performing early-warning.

Description

technical field [0001] The invention relates to the field of hydraulic equipment failure early warning, in particular to a hydraulic equipment failure early warning method based on frequent failure modes. Background technique [0002] As one of the most important transmission methods in the industry, the hydraulic system is widely used in various mechanical equipment and plays an extremely important role. Modern hydraulic equipment is mostly mechanical, electrical and hydraulic integrated equipment with complex structure and high precision. The equipment has the characteristics of mechanical-hydraulic coupling, time-varying and non-linear, and all components in the hydraulic system work in a closed oil circuit. The flow state of oil in the road and the condition of internal parts cannot be directly observed, so the fault diagnosis of hydraulic equipment is more difficult than that of general mechanical and electrical equipment; It will affect the entire production process, ...

AI Technical Summary

Problems solved by technology

However, due to the particularity of the monitoring data of hydraulic equipment and the application background of fault diagnosis and early warning, the use of classical Apriori algorithms to mine historical data of equipment will not only miss meaningful potential rules and generate false positives; rule, generating false positives

For example, since classic Apriori is looking for the largest frequent itemset among all items, it cannot find potential rules for all fault types, which will generate false positives. At the same time, classic Apriori will also generate a series of rule information consisting only of monitoring status values. These rules do not derive any information about potential failures, leading to false positives for invalid rules

To sum up, using the classic Apriori algorithm to correlate the monitoring data of hydraulic equipment in steel mills will not only take too long to mine due to the huge amount of data, but also cause false positives and missed negatives, which will easily cause data loss for technicians

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