Automatic fault diagnosis of internal combustion engine based on spectrogram and artificial neural network

Authors:
Sandeep Kumar Yadav;Prem Kumar Kalra
Affiliations:
Indian Institute of Technology Kanpur, Department of Electrical Engineering, Kanpur, India;Indian Institute of Technology Kanpur, Department of Electrical Engineering, Kanpur, India
Venue:
ROCOM'10 Proceedings of the 10th WSEAS international conference on Robotics, control and manufacturing technology
Year:
2010

Citing 6
Cited 0

Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy—a survey and some new results

Automatica (Journal of IFAC)
Time-frequency analysis: theory and applications

Time-frequency analysis: theory and applications
Discrete-time signal processing (2nd ed.)

Discrete-time signal processing (2nd ed.)
Acoustic Emission, Cylinder Pressure and Vibration: A Multisensor Approach to Robust Fault Diagnosis

IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
Investigation of engine fault diagnosis using discrete wavelet transform and neural network

Expert Systems with Applications: An International Journal
Discrete wavelet transform and back-propagation neural networks algorithm for fault location on single-circuit transmission line

ROBIO '09 Proceedings of the 2008 IEEE International Conference on Robotics and Biomimetics

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a signal analysis technique for internal combustion (IC) engine fault diagnosis based on the spectrogram and artificial neural network (ANN). Condition monitoring and fault diagnosis of IC engine through acoustic signal analysis is an established technique for detecting early stages of component degradation. The location dependent characteristic fault frequencies make it possible to detect the presence of a fault and to diagnose on what part of the engine the fault is. The difficulty of localized fault detection lies in the fact that the energy of the signature of a faulty engine is spread across a wide frequency band and hence can be easily buried by noise. To solve this problem, the spectrogram for an integrated time frequency pattern extraction of the engine vibration is proposed. The method offers the advantage of good localization of the acoustic signal energy in the time frequency domain. Statistical parameters like, kurtosis, shape factor, crest factor, mean, median, variance etc. are used for feature extraction in time-frequency domain, and artificial neural network (ANN) was employed to identify the faults in IC engine. Experimental results show that the proposed method is very effective.