The Remote Mechanical Fault Diagnosis Research Based On Leach Agreement

Remote fault diagnosis technology is a synthesis technique which combinestraditional fault monitoring, diagnosis, maintenance and some other relatedsubjects such as computer network, database and artificial intelligencetechnology. People can monitor and diagnosis the field device by computer andwireless networks, even in different place. Large engineering vehiclesconstruction is a necessary tool for engineering construction, only safe andreliable operation can effectively ensure the progress and quality of the project.However, the project vehicle always work in poor conditions which easy lead tooccur fault, and the event of failure, can easily result in damage to the equipment,plane crash major accidents have occurred. The serious accident may affect thehealthy development of the economy and social stability. Firstly, the traditionaldetection techniques often use wired cable wiring to transmit, it is difficult forsuch a large-scale equipment or field condition. Secondly, the hydraulic systemas the core of the construction machinery, will directly affect the efficiency of itswork. For the above-mentioned problems, the main contents of the paper are asfollows:1. Wiring is difficult and inflexible in the traditional fault diagnosis, thispaper used wireless sensor network (WSN) technology to replace the cable, theresearch work mainly focused on energy control aspect, aim at the exist problemof these protocols in the high energy consumption, network lifetime short and soon. In this paper, further research of cluster-based routing protocol LEACHwhich have been widely used is made, and put forward an improvement schemeof it, the comparison of LEACH protocol and improved protocol was based onMATLAB platform, and the simulation result shows that the improved protocolreduces the network node energy consumption, prolong the network lifetime andnetwork uptime.2. Aiming at the issues of project that the vehicle hydraulic system has acomplex structure, hard accessible, and the cause of the malfunction is not easy to identify, the key diagnostic technique is how to effectively extract faultfeatures and build accurate model. In order to solve this problem, this paperproposed a fault feature extraction based on local mean decomposition ofapproximate entropy method, and combined them with the LSSVM to diagnosethe mechanical fault. First the fault signal is decomposed by LMD to achieve theseparation of the fault feature, and then calculate the approximate entropy of thePF component to extract more precise fault features, at last input feature featuresinto LSSVM to identify faults. Compare the proposed algorithm with EMDdecomposition and wavelet decomposition methods, the results show that themethod can better extract fault features, to improve the accuracy and speed offault diagnosis.