Fault Diagnosis of Aircraft Power Starting System Based on MTBF-SVM

In order to solve the fault diagnosis of aircraft power starting system, the fault diagnosis method through constructing binary tree SVM (support vector machines) is researched in this paper. Consider that the components which have high fault rate have priority to be isolated, the method trains the classifier depend on MTBF from small to big and uses the basic structure of binary tree-SVM to generate the leaf nodes gradually, each leaf node represents a fault mode. The method is applied to the identify the common nine fault mode of aircraft power starting system. Introduction The power starting system is the important airborne equipment of aircraft and the key system which starts the aircraft engine timely and reliably in the ground and air[1]. Therefore, locate the fault of aircraft power starting system fast, accurately and reliably is of importance to guarantee the safety of aircraft flight and improve the combat effectiveness of the army. Literature [2] establishes the Petri net model of aircraft power starting system to reasoning the fault expert system, the results prove that it improve the diagnosis efficiency. Through fusing the data of different sensors, Literature [3] uses the DS evidence theory to research the fault diagnosis problems of aircraft power system. The above methods use many prior knowledge, they usually don’t take the promotion of the method into consideration. Because the support vector machine(SVM)[4] which has been widely applied to the fault diagnosis field in recent years follows the basic principle of structure risk minimization and has strong generalization ability, it can solve many practical problems such as small sample problems, non-linear problems, high dimension problems, local minimum problems and so on[5]. Apart from this, it overcomes the shortcut of other self-learning methods(neural net method) such as long learning time, over-learning easily and so on. The method provides a new theoretical way and technology realization mean for modern complicated large system. This paper take a research on fault diagnosis of aircraft power starting system based on SVM and the method can improve the fault diagnosis efficiency. The Basic Principle of MTBF-SVM Since 1995 Vapnik proposed SVM it has always been taken attention in the theoretical research and engineering application. The initial SVM algorithm is used to construct an optimal hyper which distinguished two classes of samples. But multi-classification problems occur in practical application, the basic idea of the current common algorithm is that divide a multi-classification problem into two classis classification problem, then classifies the sub-classifier through combination in a certain way. Nowadays one-against-one algorithm[6], one-against-rest algorithm[7] and once for all algorithm is widely used and their performance is well. However, there exist a large number of regions which can’t be separate in these algorithms, and their training time and test time is very long. Aim at the problem, this paper puts forward to a binary tree-SVM algorithm based on average fault rate, and applies to fault diagnosis of aircraft power starting system. International Conference on Manufacturing Engineering and Intelligent Materials (ICMEIM 2017) Copyright © 2017, the Authors. Published by Atlantis Press. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). Advances in Engineering, volume 100