A mining hydraulic drilling rig monitoring method and system

By individually monitoring the core components of the hydraulic drill rig, combined with real-time data of the relative components, and using a combined abnormality determination method, the problem that traditional monitoring methods cannot accurately determine the cause of the fault is solved, and efficient and accurate monitoring effects are achieved.

CN118940195BInactive Publication Date: 2025-03-25YANTAI MOUJIN MINING CO LTD
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Patent Information

Application Number
CN202411418756.8
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-10-12
Publication Date
2025-03-25
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

Traditional hydraulic drilling rig monitoring methods cannot accurately determine the cause of the fault, resulting in safety hazards, and the massive redundant data generated by all-round monitoring increases monitoring costs and reduces the efficiency of fault determination.

Method used

A mining hydraulic drill rig monitoring method and system is adopted. By individually monitoring the core components of the hydraulic drill rig, combining the real-time data of the relative components, a combined abnormality determination method is used to troubleshoot the causes of failures one by one, and the monitoring accuracy is improved.

Benefits of technology

It realizes accurate judgment of the causes of hydraulic drilling rig failure, reduces monitoring costs, improves monitoring efficiency, and reduces the generation of redundant data.

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Abstract

The present invention belongs to mining hydraulic drilling rig monitoring technology, and in particular to a mining hydraulic drilling rig monitoring method and system, including collecting monitoring data of mining hydraulic drilling rigs within a period; preprocessing the collected monitoring data and inputting them into a monitoring model; the monitoring model performs approximate threshold analysis on the monitoring data, and if abnormal data is output, the abnormal data is retained for precise threshold analysis, normal data before the abnormal data node is deleted, and relative monitoring data relative to the abnormal data node is actively obtained; the present application adopts a combined abnormality judgment method to eliminate the causes of the faults one by one in a targeted manner, thereby improving the accuracy of monitoring, eliminating the need for real-time monitoring of global data, reducing monitoring costs, and improving monitoring efficiency.
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