Time Frequency Signal Analysis and Processing covers fundamental concepts, principles and techniques, treatment of specialised and advanced topics, methods and applications, including results of recent research. This book deals with the modern methodologies, key techniques and concepts that form the core of new technologies used in IT, multimedia, telecommunications as well as most fields of engineering, science and technology. It focuses on advanced techniques and methods that allow a refined extraction and processing of information, allowing efficient and effective decision making that would not be possible with classical techniques.

Time-Frequency Signal Analysis and Processing: A Comprehensive Reference

: One use of spectral analysis of time series is to determine if two different time series are realizations from the same process This thesis develops the theory behind Krishnaiah and Schuurmann's theoretical work reported in their report Approximations to the Distributions of the Traces of Complex Multivariate Beta and F Matrices We take the trace of a test statistic calculated from the spectral density matrices of the time series and test it The thesis applies the theory to two small sample simulations (Author)

Spectral Analysis of Time Series.

Condition monitoring and fault diagnosis of planetary gearboxes: A review

As the demand for wind energy continues to grow at exponential rates, reducing operation and maintenance (OM) costs and improving reliability have become top priorities in wind turbine (WT) maintenance strategies. In addition to the development of more highly evolved WT designs intended to improve availability, the application of reliable and cost-effective condition-monitoring (CM) techniques offers an efficient approach to achieve this goal. This paper provides a general review and classification of wind turbine condition monitoring (WTCM) methods and techniques with a focus on trends and future challenges. After highlighting the relevant CM, diagnosis, and maintenance analysis, this work outlines the relationship between these concepts and related theories, and examines new trends and future challenges in the WTCM industry. Interesting insights from this research are used to point out strengths and weaknesses in today’s WTCM industry and define research priorities needed for the industry to meet the challenges in wind industry technological evolution and market growth.

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Wind Turbine Condition Monitoring: State-of-the-Art Review, New Trends, and Future Challenges

A review on data-driven fault severity assessment in rolling bearings

Condition monitoring of machines working under non-stationary operations is one of the most challenging problems in maintenance. A wind turbine is an example of such class of machines. One of effective approaches may be to identify operating conditions and investigate their influence on used diagnostic features. Commonly used methods based on measurement of electric current, rotational speed, power and other process variables require additional equipment (sensors, acquisition cards) and software. It is proposed to use advanced signal processing techniques for instantaneous shaft speed recovery from a vibration signal. It may be used instead of extra channels or in parallel as signal verification.

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Measurement of instantaneous shaft speed by advanced vibration signal processing - Application to wind turbine gearbox

Time-frequency representations (TFRs) are suitable tools for nonstationary signal analysis, but their reading is not straightforward for a signal interpretation task. This paper investigates the use of TFR statistical properties for classification or recognition purposes, focusing on a particular TFR: the spectrogram. From the properties of a stationary process periodogram, we derive the properties of a nonstationary process spectrogram. It leads to transform the TFR to a local statistical features space from which we propose a method of segmentation. We illustrate our matter with first- and second-order statistics and identify the information they, respectively, provide. The segmentation is operated by a region growing algorithm, which does not require any prior knowledge on the nonstationary signal. The result is an automatic extraction of informative subsets from the TFR, which is relevant for the signal understanding. Examples are presented concerning synthetic and real signals.

Spectrogram segmentation by means of statistical features for non-stationary signal interpretation

This paper investigates the circularity of short time Fourier transform (STFT) coefficients noise only, and proposes a modified STFT such that all coefficients coming from white Gaussian noise are circular. In order to use the spectral kurtosis (SK) as a Gaussianity test to check if signal points are present in a set of STFT points, we consider the SK of complex circular random variables, and its link with the kurtosis of the real and imaginary parts. We show that the variance of the SK is smaller than the variance of the kurtosis estimated from both real and imaginary parts. The effect of the noncircularity of Gaussian variables upon the spectral kurtosis of STFT coefficients is studied, as well as the effect of signal presence. Finally, a time-frequency segmentation algorithm based on successive iterations of noise variance estimation and time-frequency coefficients detection is proposed. The iterations are stopped when the spectral kurtosis on nondetected points reaches zero. Examples of segmented time-frequency space are presented on a dolphin whistle and on a simulated signal in nonwhite and nonstationary Gaussian noise.

https://hal.archives-ouvertes.fr/hal-00522190/document

Circularity of the STFT and Spectral Kurtosis for Time-Frequency Segmentation in Gaussian Environment

The installation of a condition monitoring system (CMS) aims to reduce the operating costs of the monitored system by applying a predictive maintenance strategy. However, a system-driven configuration of the CMS requires the knowledge of the system kinematics and could induce a lot of false alarms because of predefined thresholds. The purpose of this paper is to propose a complete data-driven method to automatically generate system health indicators without any a priori on the monitored system or the acquired signals. This method is composed of two steps. First, every acquired signal is analyzed: the spectral peaks are detected and then grouped in a more complex structure as harmonic series or modulation sidebands. Then, a time-frequency tracking operation is applied on all available signals: the spectral peaks and the spectral structures are tracked over time and grouped in trajectories, which will be used to generate the system health indicators. The proposed method is tested on real-world signals coming from a wind turbine test rig. The detection of a harmonic series and a modulation sideband reports the birth of a fault on the main bearing inner ring. The evolution of the fault severity is characterized by three automatically generated health indicators and is confirmed by experts.

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Time-Frequency Tracking of Spectral Structures Estimated by a Data-Driven Method

A method for automatic signal recognition, applied to seismic signals classification, is presented. It is based on the fusion of data derived from the analysis of the signal in three domains: time, time-frequency, and polarization. In the time domain, two techniques are used for envelope shape parametrization. In the time-frequency domain, the autoregressive and Capon (ARCAP) time-frequency method is used on a gliding time-window to estimate the spectral components of the signal versus time. For each window, the frequencies are estimated using AR modelization. The power at each frequency and the corresponding filtered signal are estimated using Capon's (1969) method. A comparison with Fourier's narrow-bandpass filtering shows that Capon's method produces a better filtering. In the polarization domain, two original methods are proposed: one for checking the linear polarization of a signal and one for localizing the linear waves in the time-frequency plane. A system for automatic recognition of seismic signals associated with avalanches is then presented as an application. Signal features are derived from the analysis to sum up the characteristics of the signal in each domain. These features are combined using fuzzy logic and credibility factors, according to rules derived from physical knowledge (generating processes and propagation rules), in order to decide whether a signal comes from an avalanche or not. The global rate of correct recognition is over 90% for that first version of the system.

Nadine Martin

Papers

Measurement of instantaneous shaft speed by advanced vibration signal processing - Application to wind turbine gearbox

Spectrogram segmentation by means of statistical features for non-stationary signal interpretation

Circularity of the STFT and Spectral Kurtosis for Time-Frequency Segmentation in Gaussian Environment

Time-Frequency Tracking of Spectral Structures Estimated by a Data-Driven Method

Three-component signal recognition using time, time-frequency, and polarization information-application to seismic detection of avalanches