Damage Identification of an Automotive Engine Based on Detection of Impact Vibration Using an Adaptive Filter

In an automotive engine, faults induce impulsive vibrations and thereby degrade engine performance, making it important for an automotive engineer to detect and analyze impulsive vibration signals for fault diagnosis. However, detecting and identifying impulsive signals is often difficult because of interfering signals such as those due to engine firing, harmonics of crankshaft speed and broadband noise components. These interferences hinder early fault detection. To overcome this difficulty we present a two-stage ALEF (Adaptive Line Enhancer Filter) that is capable of enhancing impulsive signals embedded in background noise. This method is used to pre-process signals prior to time-frequency analysis via higher order methods such as the combined higher order time-frequency.