Research On Signal Acquisition And Fault Diagnosis System For Hydraulic Drill Rig

With the increasing industrialization level of China, the demand for coalenergy gradually increased. However, about50%of the whole country’s coalmines are high-gas mines, and gas outburst is a serious threat to coal mine safetyproduction. The fundamental measure to prevent the accidents of gas outburst andexplosion is gas drainage, and the core equipment is hydraulic drill rig. Signalacquisition and fault diagnosis for hydraulic drill rig is an effective method toensure normal operation and avoid production accident. In this paper, the virtualinstrument, particle swarm optimization and RBF neural network are combined toresearch a signal acquisition and fault diagnosis system for hydraulic drill rig.Firstly, the hardware platform is designed. Through the analysis of thehydraulic drill rig operational principle and failure mechanism, the feature signalsare determined; the sensors and data acquisition card are selected. Moreover, thesignal conditioning and anti-interference measures are expounded.Secondly, the hydraulic drill rig signal acquisition software is designed usingvirtual instrument. User interface module, signal acquisition module, signalprocessing module and database module are included in the software. The featuresignals are acquired, filtered, scale transformation, real-time numerical displayed,real-time waveform displayed, frequency-domain analysed, alarm displayed andsaved in database.Thirdly, the hydraulic drill rig feature signals acquisition experiments aredone on the hardware platform using the signal acquisition software. Theexperimental results demonstrated that the hardware platform works properly; allfunctions of the signal acquisition software achieve the design requirements. Finally, the hydraulic drill rig fault diagnosis is designed through RBF neuralnetwork algorithm based on particle swarm optimization. The fault diagnosismodel is designed through analysed the fault types. The algorithm and the faultdiagnosis module are designed using LabVIEW and MATLAB mixed programming.In comparison with the fault diagnosis results of a standard RBF neural networkalgorithm, the particle swarm optimization RBF neural network algorithm not onlyachieves the fault diagnosis and is also higher accuracy.In this paper, the research on signal acquisition and fault diagnosis system forhydraulic drill rig can effectively improve the reliability and safety of thehydraulic drill rig. The method is not only an attempt to combine the virtualinstrument, particle swarm optimization, RBF neural network and hydraulic drillrig, but also lay the foundation for further research.