Diagnostic signal analysis using distribution of frequency gaps between spectral peaks

Many monitoring and diagnostics algorithms are based on analyzing the spectra of signals, such as vibrations, electrical currents and voltages, torques, etc. Often the analysis involves determining exact peak locations and looking at relations between various peak groups, such as harmonic trains or sidebands. These relationships can be more telling than the actual peak locations. Manual examination is aided by graphic tools for plotting the spectra, with features such as harmonic and sideband cursors. Automated analysis often relies on methods such as cepstra, or autocorrelation. This paper presents advantages of using another method: histograms of inter-peak gaps. In contrast to methods that operate on the whole spectrum (such as autocorrelation), this method relies on finding a discrete number of peaks and exploring the distribution of gaps between them. In particular, a histogram of frequency differences between all peak pairs is constructed. The paper presents details of this method, discusses its strong and weak points, and provides examples of its application to actual data: vibration and acoustic signals involving bearing defects, as well as a motor supply current.