New acoustic monitoring method using cross-correlation of primary frequency spectrum

The acoustic data remotely measured by microphones are widely used to investigate monitoring and diagnose integrity of ball bearing in rotational machines. Early fault diagnosis is very difficult for acoustic emission. We propose a new method using a cross-correlation of frequency spectrum to classify various faults with fine grit. Principal component analysis (PCA) is used to separate the primary frequency spectrum into main frequency and residual frequency. Different with conventional classification using the PCA eigenvalue, we introduce the general cross-correlation (GCC) of main frequency and residual frequency spectrums between a basic signal vector and monitoring signal. Multiclassification strategy based on binary-tree support vector machine (SVM) is applied to perform faults diagnosis. In order to remove noise interference and increase robustness, a normalization method is proposed during time generation. Experiment results show that PCA–GCC–SVM method is able to diagnose various faults with high sensitivity.