Adaptive fractional Fourier domain filtering

The fault diagnosis of hydraulic pumps is currently important and significant to ensure the normal operation of the entire hydraulic system. Considering the nonlinear characteristics of hydraulic-pump vibration signals and the mode mixing problem of the original Empirical Mode Decomposition (EMD) method, first, we use the Complete Ensemble EMD (CEEMD) method to decompose the signals. Second, the time-frequency analysis methods, which include the Short-Time Fourier Transform (STFT) and time-frequency entropy calculation, are applied to realize the robust feature extraction. Third, the multiclass Support Vector Machine (SVM) classifier is introduced to automatically classify the fault mode in this paper. An actual hydraulic-pump experiment demonstrates the procedure with a complete feature extraction and accurate mode classification.

/pdf/fault-diagnosis-of-a-hydraulic-pump-based-on-the-ceemd-stft-32eth6bcfw.pdf

Fault Diagnosis of a Hydraulic Pump Based on the CEEMD-STFT Time-Frequency Entropy Method and Multiclass SVM Classifier

http://yoksis.bilkent.edu.tr/pdf/files/10.1016-j.jneumeth.2004.12.003.pdf

Time–frequency component analyser and its application to brain oscillatory activity

In view of continuous time-frequency characteristic of linear frequency modulated (LFM) signals, a detection method based on Short-time Fourier transform (STFT) and wavelet denoising is proposed. Input signals are short-time Fourier transformed into coherent integration of frequency-shift sample sequences with complex envelopes, achieving a time-frequency curve which is to be processed by wavelet, in order to weaken the noise and to detect LFM signals effectively. The scheme remains available in quite a low signal-to-noise (SNR) with low complexity. Simulation results show its fine detection performance in −18dB condition, which meets the demand of electronic reconnaissance to a large extent.

Detection of LFM signals in low SNR based on STFT and wavelet denoising

A new method of order tracking of rotating machinery based on instantaneous frequency estimation (IFE) is proposed in this paper. Compared with traditional methods of order tracking, this method has some outstanding advantages, such as hardware simplified, which is required by order analysis, and only software depended for order tracking. The simulations and actual tests testify the validity of the method. The algorithms of the tracking and estimation about the instantaneous speed of reference shaft are programmed. This method is an effective supplement to traditional order tracking methods and specially satisfies the requirement of Virtual Instruments (VI).

Henry K. Kwok

Papers

Improved instantneous frequency estimation using an adaptive short-time fourier transform