A Humanoid Method for Extracting Abnormal Engine Sounds from Engine Acoustics Based on Adaptive Volterra Filter

The improvement of SNR (Signal-to-Noise Ratio) of abnormal engine sounds is of great help in improving the accuracy of engine fault diagnosis. By imitating the way that human technicians use to distinguish abnormal engine sounds from engine acoustics, a humanoid abnormal sound extracting method is proposed. By implementing adaptive Volterra filter in the canonical Adaptive Noise Cancellation (ANC) system, the proposed method is capable of tracing the engine baseline sound which exhibits an intrinsic nonlinear dynamics. Besides, by introducing a template noise tailored from the records of engine baseline sound and taking it as virtual input of the adaptive Volterra filter, the priori knowledge of engine baseline sound, such as inherent correlation, periodicity or phase information, and stochastic factors, is taken into consideration. The hybrid simulations prove that the proposed method is functional. Since the method proposed is essentially a single-sensor based ANC, hopefully, it may become an effective way to extricate the dilemma that canonical dual-sensor based ANC encounters when it is used in extracting fault-featured signals from observed signals.