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Mining drill fault intelligent identification method

An intelligent identification and drilling rig technology, applied in the field of intelligent diagnosis, can solve problems such as the inability to meet the needs of modern production of mining rigs, and achieve the effects of good real-time performance, high diagnosis accuracy and comprehensive diagnosis.

Inactive Publication Date: 2013-09-25
XIAN RES INST OF CHINA COAL TECH& ENG GROUP CORP
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Problems solved by technology

These methods can no longer meet the needs of modern production of mining drilling rigs

Method used

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  • Mining drill fault intelligent identification method
  • Mining drill fault intelligent identification method
  • Mining drill fault intelligent identification method

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Embodiment Construction

[0024] The present invention will be further described below in conjunction with the accompanying drawings.

[0025] Such as figure 1 As shown in Fig. 1, an intelligent fault identification method for mining rigs mainly includes two parts: primary diagnosis based on signal processing and secondary diagnosis based on hybrid intelligence. In the primary diagnosis based on signal processing, the state data of the drilling rig is first collected as input, and then the noise signal in the state data is eliminated by wavelet packet analysis and empirical mode decomposition, the fault information is extracted, and the time-domain statistical characteristics of the original signal and the decomposed signal are calculated and frequency domain statistical characteristics, and preliminarily judge whether the drilling rig is faulty or not according to the characteristic values. In the secondary diagnosis based on hybrid intelligence, all the features of the primary diagnosis are used to ...

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Abstract

The invention discloses a mining drill fault intelligent identification method which mainly comprises a primary diagnosis based on signal processing and a secondary diagnosis based on hybrid intelligence. According to the primary diagnosis based on signal processing, signal processing methods such as time domain and frequency domain analysis, wavelet packet analysis and empirical mode decomposition are utilized to extract a drill fault feature, a characteristic value is obtained through calculating and doing statistics, and a drill fault is judged preliminarily. According to the secondary diagnosis based on hybrid intelligence, the feature of the primary judgment is taken as an input, a support vector machine, an expert system, a fault tree analysis are used to diagnose the drill fault, and a diagnosis result is obtained by using voting technology. According to the mining drill fault intelligent identification method, a plurality of signal processing technology and intelligent diagnosis methods are integrated, the accuracy of drill fault diagnosis can be effectively raised, the examination and repair times are reduced, and the drilling cost is reduced.

Description

technical field [0001] The invention belongs to the field of intelligent diagnosis, and in particular relates to an intelligent fault identification method for a mining drilling rig. Background technique [0002] Mining drilling rigs are complex in structure, harsh in working environment, and require frequent handling, which is prone to various failures. The consequence of mining drilling rig failure is generally serious, which directly affects the mining efficiency of coal and even the safety of underground production. The existing routine fault diagnosis methods for drilling rigs only statically check the faults of the drilling rig, and then take corresponding maintenance measures, without monitoring the working status of the drilling rig. These methods can no longer meet the needs of modern production of mining rigs. For this reason, the present invention intends to use a variety of state parameters of the drilling rig operation process as input, and use a variety of si...

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Application Information

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IPC IPC(8): G01M13/00E21B44/00
Inventor 董洪波姚亚峰申中杰李晓鹏宋昱播杜小山
Owner XIAN RES INST OF CHINA COAL TECH& ENG GROUP CORP
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