Using ENN-1 for fault recognition of automotive engine

In the automotive factory, various types of engines are assembled and dispatched. An engine fault not only damages the engine itself but also causes a break in the automobile system. Engine fault diagnosis can produce significant cost saving by scheduling preventive maintenance and preventing extensive downtime periods Caused by extensive failure. Therefore, this paper presents a novel diagnosis method based on the extension neural network type-1 (ENN-1) and applies it in the fault diagnosis of engine malfunction. The proposed ENN-1 has a very simple structure and permits fast adaptive processes for new training data. Moreover, the learning speed of the proposed ENN-1 is shown to be faster than the previous approaches. The proposed method has been tested on practical diagnostic records and compared with the multilayer neural networks (MNN) and k-means classification methods. The test results show that the proposed method is suitable for detecting vibration fault of automotive engine, and it is efficient in dealing with noise in the data. (C) 2009 Elsevier Ltd. All rights reserved.