A study on transient enhancement for fault diagnosis based on an active noise control system

Active noise control (ANC) is a more effective technique used for acoustic noise cancelation in comparison with passive approaches which are difficult and expensive to implement, especially for cancelling the noise in the low frequency range. In the ANC system, an anti-noise signal is introduced to suppress the primary noise to produce a residual which are used for updating the adaptive filter coefficients. In this paper, a method of transient content enhancement for fault detection and diagnosis is investigated based on a laboratory ANC system. A number of simulation studies are conducted to evaluate the performance of the method using a typical filtered-x least mean square (FXLMS) algorithm under different types of noise signals. In the mean time, the algorithm is also adapted to achieve feature extraction under the condition of maintaining its noise cancelation performance. Moreover, experimental studies were carried out using noise signals from a heavy duty diesel engine to further demonstrate the performance obtained from simulation studies. The simulation and preliminary experimental results show that the investigated ANC algorithm can obtain an effective noise cancellation for sinusoidal signal in low frequency band and provide a residual signal with more non-stationary contents for developing diagnostic features.