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A new method and application is proposed to characterize intensity and pitch of human heart sounds and murmurs. Using recorded heart sounds from the library of one of the authors, a visual map of heart sound energy was established. Both normal and abnormal heart sound recordings were studied. Representation is based on Wigner-Ville joint time-frequency transformations. The proposed methodology separates acoustic contributions of cardiac events simultaneously in pitch, time and energy. The resolution accuracy is superior to any other existing spectrogram method. The characteristic energy signature of the innocent heart murmur in a child with the S3 sound is presented. It allows clear detection of S1, S2 and S3 sounds, S2 split, systolic murmur, and intensity of these components. The original signal, heart sound power change with time, time-averaged frequency, energy density spectra and instantaneous variations of power and frequency/pitch with time, are presented. These data allow full quantitative characterization of heart sounds and murmurs. High accuracy in both time and pitch resolution is demonstrated. Resulting visual images have self-referencing quality, whereby individual features and their changes become immediately obvious.

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A new gear-fault-detection parameter called NP4 is introduced. This fault-detection parameter utilizes the properties of the joint time-frequency analysis given by the Wigner-Ville distribution (WVD) and kurtosis. With the WVD, the instantaneous power of the gear-vibration signature for one complete rotor revolution can be obtained. The presence of single-gear-tooth damage can be manifested by the existence of an instantaneous power distribution with a peakedness larger than the normal distribution. The normalized kurtosis, a fourth-order statistical parameter calculated for the instantaneous power distribution, provides the gear-fault-detection parameter NP4. The developed fault-detection parameter NP4 is sensitive to gear-tooth damage, especially for damage in a single tooth. The application of this NP4 fault-detection parameter was demonstrated by experimental data obtained from a gear test rig. The results showed that the NP4 parameter, used with the WVD, can provide an accurate fault identi cation of gear-tooth damage. The parameter NP4 would be of help to the practitioners in the eld of machine health monitoring. NomenclatureC = positive real constant E = total energy of the signal f = frequency in hertz h(t ) = window function j = complex number L = length of the data window N = number of data points NK = normalized kurtosis NP4 = normalized kurtosis of instantaneous signal power n, i = index corresponding to the data point P(t ) = instantaneous signal energy or signal power P = mean value of signal power P(t ) s(t ) = analytic signal, complex function T = time sampling interval t = time in seconds W x x (t , f ) = Wigner-Ville distribution, a function of both time and frequency X ( f ) = energy density spectrum x ¤ (t ) = complex conjugate of the analytic signal x(t) x(t ), y(t ) = acquired vibration signal l (t ) = weighting function r = standard deviation r t = positive real constant s = time in seconds

This paper documents the feasibility of using acoustic signals for health monitoring of a gear transmission system. A procedure is presented for eliminating the extraneous background noise through the use of the acoustic intensity measurements. This procedure was applied to the acoustic signals emanating from a helicopter tail gear transmission in an experimental test rig. Once the background noise was eliminated, the acoustic signature was found to be well correlated with the vibration signature derived from accelerators mounted on the transmission housing. Then, when transformed into a joint time-frequency domain representation, the acoustic signatures provided a clear indication of the presence and location of a damaged gear tooth in the transmission. Thus, the procedures for acquisition and processing of the acoustic signals are shown to be suitable for future use in the design and implementation of machine health monitoring systems.