The incipient faults in IC engine can be detected by conventional methods using various sensors. In this proposed paper the use of audio signal from engine is employed for fault detection. The use of audio signal for fault diagnosis in Internal Combustion Engine has grown significantly due to advances in the progress of digital signal processing algorithms and Artificial Neural Network. A fault diagnosis in internal combustion engine using digital signal processing &Artificial Neural Network uses MATLAB is proposed. The present paper discusses a methodology where a set of parameters is used to checks the status of an engine as either healthy or faulty This method based on parameter estimation, also signal modal approaches are developed to generate several symptoms indicating difference between normal and faulty status.

1. S. N. Dandare and S. V. Dudul "Consistency of MLP & SVM for Air Filter Fault Detection in an Automobile Engine from Sound Signal" International Journal of Computer Information Systems, Vol. 2, No. 2, 2011
2. KadarsahSuryadi&Eri Ricardo Nurzal, " A Decision Support System for Car Fault Diagnosis Using Expert System" . International Journals of Information Science for Decision Making N02-April 1998
3. ShubhalxmiKher, P. K. Chand, &P. C. Sharma , " AUTOMOBILE ENGINE FAULT DIAGNOSIS USING NEURAL NETWORK" IEEE Intelellignent Transportation Systems Conference Processings- Oakland (CA), USA – August 25-29 2001
4. Jain-Da Wu, Chiu – Hong Liu, "Investigation of engine fault diagnosis using discrete wavelet transform and neural network". Expert System with Applications 35(2008) 1200-1213.
5. Robert. J. Howlett, Simon. D. Walters, Peter. A. Howson, Ian. A. Park, "Air-Fuel Ratio Measurement in an Internal Combustion Engine using a Neural Network.
6. Matthew A. Franchek, Patrick J. Buehler ImadMakki, "Intake Air Path Diagnostics for Internal Combustions Engine. Journal of dynamic Systems, Measurement, and Control. January 2007, Vol. 129.
7. A. Albarbar¹*, F. Gu², A. D. Ball²A. Starr³ ¹Department of Engineering and Technology, Manchester Metropolitan University, Manchester, "Acoustic Monitoring of Engine Fuel Injection Based on Adaptive Filtering Techniques"
8. Rolf Isermann,institute of automatic control, darmstadt university of technology risermann@iat. tu-darmstadt" model-based fault detection and diagnosisstatus and applications"
9. Füssel, D. and R. Isermann (2000). Hierarchical motordiagnosis utilizing structural knowledge and a selflearningneuro-fuzzy-scheme. IEEE Trans. on Ind. Electronics, Vol. 74, No. 5, pp. 1070-1077.
10. Leonhardt, S. (1996). ModellgestützteFehlererkennungmitneuronalenNetzen - Überwachung von Radaufhängungen und Diesel-Einspritzanlagen. Fortschr. Ber. VDI Reihe 12. VDI-Verlag: Düsseldorf.
11. Hush, D. R and Horne, B. G. Progress in supervisedneural networks. IEEE Signal Processing Magazine. January 1993.

Internal Combustion Engine Digital Signal Processing Artificial Neural Network Parameter Estimation