Time-frequency representation of earthquake accelerograms and inelastic structural response records using the adaptive chirplet decomposition and empirical mode decomposition

In this paper, the adaptive chirplet decomposition combined with the Wigner-Ville transform and the empirical mode decomposition combined with the Hilbert transform are employed to process various non-stationary signals (strong ground motions and structural responses). The efficacy of these two adaptive techniques for capturing the temporal evolution of the frequency content of specific seismic signals is assessed. In this respect, two near-field and two far-field seismic accelerograms are analyzed. Further, a similar analysis is performed for records pertaining to the response of a 20-story steel frame benchmark building excited by one of the four accelerograms scaled by appropriate factors to simulate undamaged and severely damaged conditions for the structure. It is shown that the derived joint time–frequency representations of the response time histories capture quite effectively the influence of non-linearity on the variation of the effective natural frequencies of a structural system during the evolution of a seismic event; in this context, tracing the mean instantaneous frequency of records of critical structural responses is adopted. The study suggests, overall, that the aforementioned techniques are quite viable tools for detecting and monitoring damage to constructed facilities exposed to seismic excitations

A Comparative Study of Time-Frequency Representations for Fault Detection in Wind Turbine

To reduce the cost of wind energy, minimization and prediction of maintenance operations in wind turbine is of key importance. In variable speed turbine generator, advanced signal processing tools are required to detect and diagnose the generator faults from the stator current. To detect a fault in non-stationary conditions, previous studies have investigated the use of time-frequency techniques such as the Spectrogram, the Wavelet transform, the Wigner-Ville representation and the Hilbert-Huang transform. In this paper, these techniques are presented and compared for broken-rotor bar detection in squirrel-cage generators. The comparison is based on several criteria such as the computational complexity, the readability of the representation and the easiness of interpretatio

Automatic Partial Extraction from the Modal Distribution

The Modal Distribution (MD) is a time-frequency distribution specifically designed to model the quasi-harmonic, multi-sinusoidal, nature of music signals and belongs to the Cohen general class of time-frequency distributions. The problem of signal synthesis from bilinear time-frequency representations such as the Wigner distribution has been investigated [1,14] us-ing methods which exploit an outer-product interpretation of these distributions. Methods of synthesis from the MD based on a sinusoidal-analysis-synthesis procedure using estimates of in-stantaneous frequency and amplitude values have relied on a heuristic search ‘by eye’ for peaks in the time-frequency domain [2,7,8]. An approach to detection of sinusoidal components with the Wigner Distribution has been investigated in [15] based on a comparison of peak magnitudes with the DFT and STFT. In this paper we propose an improved frequency smoothing kernel for use in MD partial tracking and adapt the McCauley-Quatieri sinusoidal analysis procedure to enable a sum of sinusoids synthe-sis. We demonstrate that the improved kernel enhances automatic partial extraction and that the MD estimates of instantaneous amplitude and frequency are preserved. Suggestions for future extensions to the synthesis procedure are given