Undecimated Lifting Wavelet Packet Transform with Boundary Treatment for Machinery Incipient Fault Diagnosis

Abstract

Effective signal processing in fault detection and diagnosis (FDD) is an important measure to prevent failure and accidents of machinery. To address the end distortion and frequency aliasing issues in conventional lifting wavelet transform, a Volterra series assisted undecimated lifting wavelet packet transform (ULWPT) is investigated for machinery incipient fault diagnosis. Undecimated lifting wavelet packet transform is firstly formulated to eliminate the frequency aliasing issue in traditional lifting wavelet packet transform. Next, Volterra series, as a boundary treatment method, is used to preprocess the signal to suppress the end distortion in undecimated lifting wavelet packet transform. Finally, the decomposed wavelet coefficients are trimmed to the original length as the signal of interest for machinery incipient fault detection. Experimental study on a reciprocating compressor is performed to demonstrate the effectiveness of the presented method. The results show that the presented method outperforms the conventional approach by dramatically enhancing the weak defect feature extraction for reciprocating compressor valve fault diagnosis.

Ensemble Dilated Convolutional Neural Network and Its Application in Rotating Machinery Fault Diagnosis

“…Fortunately, with the rapid development and integration of sensor technology in the modern industry, condition monitoring and fault diagnosis have become the most effective methods to avoid damage using the measured monitoring vibration signals [ 3 , 4 ]. As a result, prognostics and health management (PHM) of rotating machinery under changeable working circumstances has emerged as a critical technique for economic efficiency and a hot topic of various research studies [ 5 ].…”