Fault Detection of Gearbox from Inverter Signals Using Advanced Signal Processing Techniques

The gear faults are time-localized transient events so time-frequency analysis techniques (such as the Short-Time Fourier Transform, Wavelet Transform, motor current signature analysis) are widely used to deal with non-stationary and nonlinear signals. Newly developed signal processing techniques (such as empirical mode decomposition and Teager Kaiser Energy Operator) enabled the recognition of the vibration modes that coexist in the system, and to have a better understanding of the nature of the fault information contained in the vibration signal. However these methods require a lot of computational power so this paper presents a novel approach of gearbox fault detection using the inverter signals to monitor the load, rather than the motor current. The proposed technique could be used for continuous monitoring as well as on-line damage detection systems for gearbox maintenance

Coherent network analysis technique for discriminating gravitational-wave bursts from instrumental noise

Existing coherent network analysis techniques for detecting gravitational-wave bursts simultaneously test data from multiple observatories for consistency with the expected properties of the signals. These techniques assume the output of the detector network to be the sum of a stationary Gaussian noise process and a gravitational-wave signal, and they may fail in the presence of transient non-stationarities, which are common in real detectors. In order to address this problem we introduce a consistency test that is robust against noise non-stationarities and allows one to distinguish between gravitational-wave bursts and noise transients. This technique does not require any a priori knowledge of the putative burst waveform.Comment: 18 pages, 11 figures; corrected corrupted figur