Research On Fault Diagnosis Of Mine Truck Engine

Since the 1990 s of last century,oil monitoring technology has been used to diagnose the malfunctions of mining automobile engines.At first,it was only a routine physical and chemical analysis of lubricating oil.With the rapid development of Chinese mining industry is keeping expanding the scale of production changing production condition increasing quantity of mining vehicles.the mode of transportation has changed from railway to mining vehicle,which requires higher reliability,economic operation and engine safety.With the development of science and technology,at present mining vehicle is developing rapidly,highly technology and integration and fast automation.Especially the development of artificial intelligence technology,It has caused huge changes in the auto industry and its maintenance industry,making more professional and intelligent.With the requirements of the current industry development,for greatly reducing the failure rate and operating costs of mining vehicle,the mining industry introduce oil monitoring technologies such as atomic emission spectroscopy.At present,the road transportation has used atomic emission spectroscopy to monitor the oil of mining vehicle engine.Atomic emission spectroscopy can accurately analyze the friction pairs abrasion inner machine and the working conditions of mechanical equipment,and so on,it can used in analyzed inner malfunction of mining vehicle.In order to function of atomic emission spectroscopy,this paper uses the three-line value of the limit value method to infer the fault type of mining automobile engines.The particle swarm optimization is used to establish the corresponding fault diagnosis model.Which supports the shortcoming of limit value method.The main content of the paper is as follows:Firstly,for the same type of mining vehicle engine,it is based on the atomic emission spectrometry data and limit value method for long-term monitoring.I use Visual Studio 2017 to draw the spectrum of the mining vehicle engine lubricant,analyze the normal line,warning line and abnormal line chart of the project,then infer the mining vehicle engine’s by observing this area of the newly collected oil spectrum analysis data.Secondly,based on the fault type of the mine vehicle engine and the corresponding fault feature element concentration,a support vector machine fault diagnosis model is based on particle swarm optimization algorithm.The diagnosis model is simulated and tested to realize the fault diagnosis of mining vehicle engine.Finally,according to the needs of current road transportation operation department,this paper uses Visual Studio 2017 and Microsoft Access 2008 database to develop a fault diagnosis system for mining automotive engines.The system includes five functional modules,atomic spectrum analysis module,comprehensive query and printing module,user management center module,limit value calculation module and support vector machine fault diagnosis module based on particle swarm optimization algorithm.Through the results,the system meets the actual operational requirements.It is significance to study the fault diagnosis system in depth,it is not only improved the production efficiency,but also provided more guidance for the arrangement of production tasks,avoiding the waste of resources.