Time–frequency analysis

About: Time–frequency analysis is a research topic. Over the lifetime, 5407 publications have been published within this topic receiving 104346 citations.

Detection and classification of power quality disturbances based on time–frequency-scale transform

Abstract: Under practical conditions, the power quality disturbances have a complex nature, and are often corrupted with external noise. This calls for robust detection and classification using appropriate signal processing and classification techniques. In this study, a time-frequency-scale transform is presented as a detection tool with high-noise immunity. It is a variant of chirplet transform adapted for power quality studies, which incorporates a Hann window and is capable of shifting and scaling operations. A number of simulated and real power quality disturbances are detected and classified under various noise levels for performance assessment. Performance of the time-frequency-scale transform is dependent on window length, and hence three different classifiers are employed to study the effect of window length variation on classification accuracy. Detection and feature extraction in time-frequency-scale transform is somewhat similar to Stockwell transform; therefore, a suitable comparison is shown wherever required. Results of the proposed methodology are found to be appreciable. Moreover, observations from this work can serve as a foundation for formulation of optimised classification problems involving manoeuvrable window signal processing techniques.

Generalized Horizontal Synchrosqueezing Transform: Algorithm and Applications

Abstract: Time−frequency analysis (TFA) is regarded as an efficient technique to reveal the hidden characteristics of the oscillatory signal. At present, the traditional TFA methods always construct the signal model in the time domain and assume the instantaneous features of the modes to be continuous. Thus, most of these approaches fail to tackle some specific kinds of impulselike signal, including shock and vibration waves, damped tones, or marine mammals. This article introduces a new method called generalized horizontal synchrosqueezing transform (GHST) to process the transient signal. A signal model defined in the frequency domain is used to deduce the GHST. Next, the new synchrosqueezing operator termed as group delay (GD) is proposed based on high-order Taylor expansions of the signal model. Finally, the modulus around ridge curves is rearranged from the original position to the estimated GD. Numerical results of a simulated signal demonstrate the precision of the GHST in terms of both readability of the time−frequency representation and reconstruction accuracy. Additionally, the proposed method is implemented to diagnose the fault in a rotary machine by analyzing the vibration signal. The validation demonstrates that the GHST performs better than other traditional TFA methods, and it is qualified for the online condition monitoring of the industrial mechanical system.

Spectrotemporal Analysis Using Local Binary Pattern Variants for Acoustic Scene Classification

Abstract: In this paper, we present an approach for acoustic scene classification, which aggregates spectral and temporal features We do this by proposing the first use of the variable-Q transform (VQT) to generate the time–frequency representation for acoustic scene classification The VQT provides finer control over the resolution compared to the constant-Q transform (CQT) or short time fourier transform and can be tuned to better capture acoustic scene information We then adopt a variant of the local binary pattern (LBP), the adjacent evaluation completed LBP (AECLBP), which is better suited to extracting features from acoustic time–frequency images Our results yield a 52% improvement on the DCASE 2016 dataset compared to the application of standard CQT with LBP Fusing our proposed AECLBP with HOG features, we achieve a classification accuracy of 855%, which outperforms one of the top performing systems

Improved EEG event classification using differential energy

Abstract: Feature extraction for automatic classification of EEG signals typically relies on time frequency representations of the signal. Techniques such as cepstral-based filter banks or wavelets are popular analysis techniques in many signal processing applications including EEG classification. In this paper, we present a comparison of a variety of approaches to estimating and postprocessing features. To further aid in discrimination of periodic signals from aperiodic signals, we add a differential energy term. We evaluate our approaches on the TUH EEG Corpus, which is the largest publicly available EEG corpus and an exceedingly challenging task due to the clinical nature of the data. We demonstrate that a variant of a standard filter bank-based approach, coupled with first and second derivatives, provides a substantial reduction in the overall error rate. The combination of differential energy and derivatives produces a 24% absolute reduction in the error rate and improves our ability to discriminate between signal events and background noise. This relatively simple approach proves to be comparable to other popular feature extraction approaches such as wavelets, but is much more computationally efficient.

A Novel Fault Diagnosis Approach for Rolling Bearing Based on High-Order Synchrosqueezing Transform and Detrended Fluctuation Analysis

Abstract: Time-frequency analysis always plays an important role in machine health monitoring owing to its advantage in extracting the fault information contained in non-stationary signal. In this paper, we present a novel technique to detect and diagnose the rolling bearing faults based on high-order synchrosqueezing transform (FSSTH) and detrended fluctuation analysis (DFA). With this method, the high-order synchrosqueezing transform is first utilized to decompose the vibration signal into an ensemble of oscillatory components termed as intrinsic mode functions (IMFs). Meanwhile, an empirical equation, which is based on the DFA, is introduced to adaptively determine the number of IMFs from FSSTH. Then, a time-frequency representation originated from the decomposed modes or corresponding envelopes is exhibited in order to identify the fault characteristic frequencies related to rolling bearing. Experiments are carried out using both simulated signal and real ones from Case Western Reserve University. Results show that the proposed method is more effective for the detection of fault characteristic frequencies compared with the traditional synchrosqueezing transform (SST) based fault diagnosis algorithm, which renders this technique is promising for machine fault diagnosis.