Fault Diagnosis Of Hoist Brake System Based On Big Data Processing Technology

As an important economic industry in China,energy mining has attracted much attention for its safe production.Mine hoist is one of the important mechanical equipments in coal mining.It undertakes the task of upgrading and decentralizing coal mine meteorites and production workers.Improving the safe operation of equipment has a great impact on the safety production of coal mines,especially the safety of workers.Reliable braking system is the necessary guarantee to improve the safe and normal operation of the equipment.With the continuous improvement of the monitoring technology,the monitoring data of the lifting equipment based on the operating conditions has the characteristics of large data and complex data,and the processing of these data.Reasonable application is the key to the research of brake system fault diagnosis.Based on the mine hoist as the research object,this paper proposes a fault diagnosis method for the hoist brake system based on big data processing technology.Based on the analysis of the failure mechanism and data characteristics of the hoist brake system,the data acquisition and data processing are studied.The method and the fault diagnosis rules generated by the machinelearning algorithm are used to diagnose and predict the fault of the hoist brake system.The main research contents are as follows:First,the fault diagnosis architecture of the brake system based on big data technology was established.The structure and working principle of the mine hoist brake system are analyzed.The common failure mechanism of the brake system is studied.The characteristics of the brake system fault data are analyzed.The fault diagnosis architecture of the brake system based on big data technology is established.Second,the monitoring data is analyzed and processed based on the SPSS data analysis method.The types of faults suitable for the analysis of this paper are analyzed in depth.The relevant monitoring parameters of the online monitoring system of the hoist are selected,and the fault data is filtered and discretized.The monitoring data is analyzed based on SPSS software,and the state information of the hoist is obtained.Relevant attributes and related relationships between attributes.Thirdly,an improved C4.5 fault diagnosis classification algorithm is proposed and a good classification effect is obtained.The common algorithm of decision tree is analyzed in detail,and the construction steps are compared.Then the Kendall harmony coefficient is introduced to improve the C4.5 algorithm,and the algorithm is implemented based on Python language.The algorithm is proved by the example to have a good fault classification effect.Fourthly,based on the above theory and method,the laboratory 2JTP-1.2hoist was used as the test equipment,and the diagnostic model generated by the algorithm was tested and verified.The fault data is collected through simulation test faults,and the diagnostic rules are used for fault diagnosis and prediction,and the diagnostic rules generated by different algorithms are compared and verified.The fault diagnosis method of the hoist brake system based on big data processing technology proposed in this paper can verify that the data volume in the fault diagnosis of the hoist brake system is difficult to handle,the correlation between feature attributes is insufficient,and the data utilization rate is solved.The low-level problem,at the same time,the diagnostic rule generated by the method has a high accuracy rate and can be applied to actual production,thereby effectively protecting coal mine safety.