Enhancement of oil debris sensor capability by reliable debris signature extraction via wavelet domain target and interference signal tracking

On-line oil debris monitoring is an effective approach to detecting machine component wear through estimating the size and the quantity of metallic debris in the lubricating oil. However, oil debris (particle) signatures are often contaminated by background noise and vibration interference during the operation of the oil debris sensor. As such, the accuracy of debris measurement and counting depends largely on the authenticity of the extracted debris signature. Considering characteristics of both target and interference signals obtained by the oil debris sensor, we propose a novel debris signature extraction technique to improve the oil debris measurement capability based on the wavelet domain information. In each wavelet scale of the oil debris sensor output signal, the debris coefficients are detected based on the singularity of the debris signal. The interference coefficients are estimated by adaptive linear prediction. The overlapped debris and interference coefficients are separated by a new prediction strategy involving alternating applications of forward and backward predictors. The differences between the mixture and the estimated interference coefficients are employed to reconstruct the debris signature. The proposed technique is evaluated using both uni- and bi-excitation experimental data and compared with a recently reported method. The experimental results and comparisons indicate that the proposed new method can extract the debris signature more truthfully, and thus improve the oil debris monitoring accuracy in real applications.