Research On The Design And Fault Prediction Of The Online Air Gap Detection System For Subway Linear Motors

With the rapid development of urban rail transit systems,linear motors become more and more popular owing to many advantages.However,controlling the air gap spacing of linear motion actuator is extremely difficult to cope with.If the air gap spacing is too large,the traction energy consumption will also be large,which may lead the primary and secondary of the motor to scratch and result in traffic accidents.Therefore,it is of great significance to detect the air gap spacing in a timely and effective manner.In this paper,based on laser triangulation method,the online detection system for the air gap of linear electric motor in train is designed and studied.The density clustering algorithm is used to process the air gap data,and accurately extract the air gap data of each motor tooth.Finally,based on historical passing data,the fault prediction and health management strategies of linear motors in train are studied.First of all,this paper carries out an overall online detection system plan,including the functions and workflows of the system.Then,this paper introduces the hardware components such as laser displacement sensor,wheel axial position sensor and car number identification device.According to the system function module,the system software module is designed,including a control unit,a data transmission unit,a data acquisition unit,a data storage unit,and a web publishing unit.Secondly,in order to accurately extract the air gap data of each motor tooth,the air gap data is processed by algorithm based on density clustering.On the one hand,the original data preprocessing is performed,including eliminating the interference data,angle correction and height correction.On the other hand,the air gap data of each motor in the train is extracted by the threshold difference method,and the air gap data of each tooth in the motor is extracted by the density clustering algorithm and the five-point template matching method.Finally,the accuracy and reliability of the algorithm are verified by passing experiments.Thirldy,the typical PHM system and fault prediction method are introduced.The fault prediction and health management strategy of the linear motor in train are studied.The health assessment scheme based on fuzzy comprehensive evaluation method of the linear motor in train as well as the air gap fault prediction based on time series model(ARIMA)are designed and studied.The accuracy of the model’s prediction of the air gap data in a short time is verified by an example,which provides a scientific basis for the maintenance of linear motion in train.