Study On Online Detection And Intellectual Diagnosis Of Failures Of PM DC Motor

Permanent-magnetic DC motors are widely used in automobile and military or civil airplanes due to their lots of merits such as simple configuration, reliable running, small size, light weight, high efficiency, etc. To ensure these motors work safely, this paper studies a simple and efficient method to monitor the condition of permanent-magnetic DC motor and diagnose the possible online faults intellectualized.In order to meet the requirement of real-time and simplicity in online faults detection,a universal diagnosing method was given to detect the typical faults of permanent-magnetic DC motor based on current signal analyzing and processing. Several signal analysis methods were synthetically used to extract the characteristic of failure from the armature current which is easy to be detected. Then the fault pattern recognization was made by artificial intelligence method.After analyzing faults mechanisms and the changing characteristic of dynamic or stable current in cases of both failure and no failure, mathematical model of faults diagnosing based on armature current was created. Parameters: average ia v of stable currenti , pulsant frequency f , pulsant range is td, peak amplitude im of starting current and its gradient k near the peak, were defined as characteristics for faults diagnosis of permanent-magnetic DC motor. Differences of the five partners in case of failure and no failure made up the characteristic vector Te = [ΔfΔi_avΔi_stdΔi_mΔk ].Several failed experiments in no loading or loading condition are done on motors with four common faults in production or application: open circuit of components, brush wear, short circuit between coils and loose weld coils. And their fault mechanism was also analyzed in theory. The consistency between experimental results and the failed mechanism analysis shows that this method is feasible and universal to diagnose the faults of permanent-magnetic DC motor.Pattern recognition is the key to realize fault intelectual diagnosis. The application of Expert System in field of fault diagnosis realized fault diagnosis intellectually. Due to the decentralization, randomicity and fuzziness of the fault character of permanent-magnetic DC motor, it is hard to get complete and efficient knowledge for creating the expert system for fault diagnosis of permanent-magnetic DC motor. The main approach to solve problem of knowledge acquisition is Machine Learning.The Statistical Pattern Recognition and the Artificial Neural Network are the basic means which are most widely used in fault diagnosis based on machine learning at present. But the theoretical basis of Statistical Pattern Recognition and Artificial Neural Network is traditional statistics. This kind of sort algorithm is valid only in the case of the number of trained sample being infinity and the results from this kind of sort algorithm is invalid when the number of trained sample is small. However, it is difficulty to gain a great deal of representative fault samples because it is impossible to let the motor long running with failure in engineering application especially used in airplanes.Fault diagnosis method based on SVM (Support Vector Machine) is developed for permanent-magnetic DC motor according to the mathematical model of SVM. The fault diagnosis results of this method in cases where only limited training samples are available is compared with that of another classification algorithm BP ANN. It shows that SVM have better performance than ANN both in training speed and recognition rate. SVM can also avoid over-fitting and trapping in local extreme which often happened in the neural networks algorithm, especially in case of limited trained samples.To overcome the problems existing in the online fault diagnosis of permanent-magnetic DC motor, such as non-symmetry of dataset, different loss by misjudgments and interference of noisy or outliers, the recognition algorithms of SVM is improved in following two ways. Firstly, a weighted support vector machine algorithm is developed through weighting error punishing factor of SVM. Both results of several experiments and analysis in theory show that this weighted support vector machines improve classification accuracy for class with small size, and reduce the different loss by misjudgments in fault diagnosis. Secondly, an improved support vector machine algorithm based on fuzzy C-means is proposed. The online data are clustered by the fuzzy C-means and the outliers are recognized according to the membership grade calculated from the fuzzy C-means. And then the data which removed the outliers are trained and tested by the support vector machine algorithm above mentioned. The results from experiments shows that support vector machine algorithm based on fuzzy C-means have better tolerance of noise and anti-noise performance. It enhances fault recognition precision in the complex case and extends the application field of fault diagnosis method based on SVM.This method needs no more messages than measured voltage and current of armature. Therefore, the advantages are lower request for hardware like sensor, more simple realization, less influence on system and lower cost. It is practical and of good perspective in online monitoring online the condition of a working permanent-magnetic DC motor in an automobile or airplane or space probe, and in controlling the quality of motor manufacture line.