The Research Of Remote Condition Monitoring And Fault Diagnosis System Of Excavator

In the view of the phenomenon in the field of fault diagnosis of excavator such as fault deterioration, parts of fatigue damage, production stagnation and production accidents caused by the inaccuracy of artificial diagnosis. In this paper, the method of building excavator remote condition monitoring and fault diagnosis has been studied based on the study of the industry at home and abroad. The system not only includes the basic excavator operation state monitoring, at the same time can realize the remote intelligent fault diagnosis,which can improve the accuracy of fault diagnosis, and make excavator’s "passive service" into "active" maintenance, realize cluster management, reduce the maintenance cost sand the economic loss, ensure excavator working efficiency.This paper has designed the overall scheme and determined the overall architecture to the system of excavator remote condition monitoring and fault diagnosis firstly. This system mainly consists of three parts: the on-board data acquisition platform, data remote transmission network, remote monitoring and diagnosis center. The monitoring and fault diagnosis object is diesel engine and hydraulic system. The monitoring parameters and observation points has been chosed according to the structure and fault mechanism of excavator. The vehicle data acquisition hardware platform based on NI Compact RIO and GPS and the remote data transmission based on GPRS network has been set up. The LabVIEW software programs of the on-board data acquisition and transmission platform has been written which can realize the collection of data, local storage and remote transmission. And then the software of remote monitoring center has been designed which includs the monitoring object information management, send request, data receiving and parsing, excavator map location, database storage and management, monitoring and diagnosis, fault report generation diesel engine and hydraulic system, fault information returned and historical fault query function module. The method of excavator vibration signal feature extraction based on EEMD and the method of excavator intelligent fault diagnosis based on SVM has been studied, which implements the key parts of diesel engine and hydraulic system condition monitoring and fault diagnosis.