Wavelet Analysis for Gas Turbine Fault Diagnostics

1997 ◽  
Vol 119 (4) ◽  
pp. 870-876 ◽  
Author(s):  
N. Aretakis ◽  
K. Mathioudakis
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Gas Turbines ◽  
Diagnostic Techniques ◽  
Diagnostic Information ◽  
Practical Application ◽  
Acoustic Measurements ◽  
Time Signals ◽  
Existing Data

The application of wavelet analysis to diagnosing faults in gas turbines is examined in the present paper. Applying the wavelet transform to time signals obtained from sensors placed on an engine gives information in correspondence to their Fourier transform. Diagnostic techniques based on Fourier analysis of signals can therefore be transposed to the wavelet analysis. In the paper the basic properties of wavelets, in relation to the nature of turbomachinery signals, are discussed. The possibilities for extracting diagnostic information by means of wavelets are examined, by studying the applicability to existing data from vibration, unsteady pressure, and acoustic measurements. Advantages offered, with respect to existing methods based on harmonic analysis, are discussed as well as particular requirements related to practical application.

scholarly journals Wavelet Analysis for Gas Turbine Fault Diagnostics

1996 ◽  
Author(s):  
N. Aretakis ◽  
K. Mathioudakis
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Gas Turbines ◽  
Diagnostic Techniques ◽  
Diagnostic Information ◽  
Practical Application ◽  
Acoustic Measurements ◽  
Time Signals ◽  
Existing Data

The application of wavelet analysis to diagnosing faults in Gas Turbines is examined in the present paper. Applying the Wavelet Transform to time signals obtained from sensors placed on an engine, gives information which is in correspondence to their Fourier Transform. Diagnostic techniques based on Fourier analysis of signals can therefore be transposed to the Wavelet analysis. In the paper the basic properties of wavelets, in relation to the nature of turbomachinery signals, are discussed. The possibilities for extracting diagnostic information by means of wavelets are examined, by studying the applicability to existing data from vibration, unsteady pressure and acoustic measurements. Advantages offered, with respect to existing methods based on harmonic analysis, are discussed as well as particular requirements related to practical application.

THE ADVANTAGE OFTHEWAVELET TRANSFORM IN PROCESSING OF SPEECH SIGNALS

2021 ◽  
Vol 4 (3) ◽  
pp. 37-41
Author(s):  
Sayora Ibragimova ◽  
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Speech Processing ◽  
High Performance ◽  
Speech Sound ◽  
Digital Signal ◽  
Speech Signals ◽  
Multi Scale ◽  
Processing Of Speech Signals

This work deals with basic theory of wavelet transform and multi-scale analysis of speech signals, briefly reviewed the main differences between wavelet transform and Fourier transform in the analysis of speech signals. The possibilities to use the method of wavelet analysis to speech recognition systems and its main advantages. In most existing systems of recognition and analysis of speech sound considered as a stream of vectors whose elements are some frequency response. Therefore, the speech processing in real time using sequential algorithms requires computing resources with high performance. Examples of how this method can be used when processing speech signals and build standards for systems of recognition.Key words: digital signal processing, Fourier transform, wavelet analysis, speech signal, wavelet transform

Wavelet Analysis of the Pressure Fluctuations of Bottom Outlet of Kamal-Saleh Dam

2009 ◽  
Author(s):  
M. Liaghat ◽  
A. Abdollahi ◽  
F. Daneshmand ◽  
T. Liaghat
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Fourier Analysis ◽  
Spectral Characteristics ◽  
Pressure Fluctuations ◽  
Transient Phenomena ◽  
Service Gate ◽  
Stationary Signals ◽  
Bottom Outlet

Non-stationary signals are frequently encountered in a variety of engineering fields. The inability of conventional Fourier analysis to preserve the time dependence and describe the evolutionary spectral characteristics of non-stationary processes requires tools which allow time and frequency localization beyond customary Fourier analysis. The spectral analysis of non-stationary signals cannot describe the local transient features due to averaging over the duration of the signal [1]. The Fourier Transform (FT) and the short time Fourier transform (STFT) have been often used to measure transient phenomena. These techniques yield good information on the frequency content of the transient, but the time at which a particular disturbance in the signal occurred is lost [2, 3]. Wavelets are relatively new analysis tools that are widely being used in signal analysis. In wavelet analysis, the transients are decomposed into a series of wavelet components, each of which is a time-domain signal that covers a specific octave band of frequency. Wavelets do a very good job in detecting the time of the signal, but they give the frequency information in terms of frequency band regions or scales [4]. The main objective of this paper is to use the wavelet transform for analysis of the pressure fluctuations occurred in the bottom-outlet of Kamal-Saleh Dam. The “Kamalsaleh Dam” is located on the “Tire River” in Iran, near the Arak city. The Bottom Outlet of the dam is equipped with service gate and emergency gate. A hydraulic model test is conducted to investigate the dynamic behavior of the service gate of the outlet. The results of the calculations based on the wavelet transform is then compared with those obtained using the traditional Fast Fourier Transform.

scholarly journals A Comparative Study of Reassigned Conventional Wavelet Transform for Machinery Faults Detection

2015 ◽  
Vol 773-774 ◽  
pp. 90-94 ◽  
Author(s):  
Ahmed M. Abdelrhman ◽  
M. Salman Leong ◽  
Lim Meng Hee ◽  
Wai Keng Ngui
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Spectrum Analysis ◽  
Vibration Spectrum ◽  
Frequency Resolution ◽  
Transient Faults ◽  
Mother Wavelet ◽  
Time Frequency ◽  
Simulated Signal

Application of Fast Fourier Transform (FFT) in machinery faults detection is known to be only effective if fault is of repetitive in nature and considering severe. While minor and transient faults are usually remain undetected based on vibration spectrum analysis. Wavelet analysis is relatively new technique which is still suffered from inadequately in its time-frequency resolution. In this paper, ahmedrabak_time wavelet is proposed based on the wavelet reassignment technique for Morlet mother wavelet. The proposed wavelet analysis is compared to the conventional wavelet analysis for machinery faults detection based on simulated signal. The results showed that the proposed wavelet has a better resolution than conventional wavelet analysis which could clearly indicate the presence and the location of the fault.

scholarly journals Skull Sex Estimation Based on Wavelet Transform and Fourier Transform

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Wen Yang ◽  
Mingquan Zhou ◽  
Pengfei Zhang ◽  
Guohua Geng ◽  
Xiaoning Liu ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Sex Discrimination ◽  
Frontal Bone ◽  
Sex Estimation ◽  
Practical Application ◽  
Estimation Model ◽  
Accuracy Rate ◽  
Research Topics ◽  
Orbital Margin

Skull sex estimation is one of the hot research topics in forensic anthropology, and has important research value in the fields of criminal investigation, archeology, anthropology, and so on. Sex estimation of skull is crucial in forensic investigations, whether in legal situations that involve living people or to identify mortal remains. The aim of this study is to establish a skull-based sex estimation model in Chinese population, providing a scientific reference for the practical application of forensic medicine and anthropology. We take the superior orbital margin and frontal bone of the skull as the research object and proposed a technology of objective sex estimation of the skull using wavelet transform and Fourier transform. Firstly, the supraorbital margin and frontal bone were quantified by wavelet transform and Fourier transform, and then the extracted features were classified by SVM, and the model was tested. The experimental results show that the accuracy rate of male and female sex discrimination is 90.9% and 94.4%, respectively, which is higher than that of morphological and measurement methods. Compared with the traditional methods, the method has more theoretical basis and objectivity, and the correct rate is higher.

Classification of Acceleration Waveforms during Walking by Wavelet Transform

1997 ◽  
Vol 36 (04/05) ◽  
pp. 356-359 ◽  
Author(s):  
M. Sekine ◽  
M. Ogawa ◽  
T. Togawa ◽  
Y. Fukui ◽  
T. Tamura
Keyword(s):  
Wavelet Transform ◽  
Wavelet Analysis ◽  
Center Of Gravity ◽  
Dynamic Responses ◽  
Sequential Data ◽  
Acceleration Signal ◽  
Continuous Dynamic

Abstract:In this study we have attempted to classify the acceleration signal, while walking both at horizontal level, and upstairs and downstairs, using wavelet analysis. The acceleration signal close to the body’s center of gravity was measured while the subjects walked in a corridor and up and down a stairway. The data for four steps were analyzed and the Daubecies 3 wavelet transform was applied to the sequential data. The variables to be discriminated were the waveforms related to levels -4 and -5. The sum of the square values at each step was compared at levels -4 and -5. Downstairs walking could be discriminated from other types of walking, showing the largest value for level -5. Walking at horizontal level was compared with upstairs walking for level -4. It was possible to discriminate the continuous dynamic responses to walking by the wavelet transform.

scholarly journals Energy Analysis Method Based on Wavelet Transform for Sensor Fault Diagnosis

2011 ◽  
Vol 2-3 ◽  
pp. 117-122 ◽  
Author(s):  
Peng Peng Qian ◽  
Jin Guo Liu ◽  
Wei Zhang ◽  
Ying Zi Wei
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Wavelet Analysis ◽  
Energy Analysis ◽  
Sensor Faults ◽  
Analysis Method ◽  
Sensor Fault ◽  
Fault Type ◽  
Ideal Tool ◽  
Non Stationary Signal

Wavelet analysis with its unique features is very suitable for analyzing non-stationary signal, and it can also be used as an ideal tool for signal processing in fault diagnosis. The characteristics of the faults and the necessary information on the diagnosis can be constructed and extracted respectively by wavelet analysis. Though wavelet analysis is specialized in characteristics extraction, it can not determine the fault type. So this paper has proposed an energy analysis method based on wavelet transform. Experiment results show the method is very effective for sensor fault diagnosis, because it can not only detect the sensor faults, but also determine the fault type.

Identification of Static Unbalance Wheel of Passenger Car Carried out on a Road

2014 ◽  
Vol 214 ◽  
pp. 48-57 ◽  
Author(s):  
Krzysztof Prażnowski ◽  
Sebastian Brol ◽  
Andrzej Augustynowicz
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Constant Velocity ◽  
Fourier Transforms ◽  
Static Condition ◽  
Rotational Frequency ◽  
Acceleration Sensor ◽  
Short Time Fourier Transform ◽  
Road Roughness ◽  
Short Time

This paper presents a method of identification of non-homogeneity or static unbalance of the structure of a car wheel based on a simple road test. In particular a method the detection of single wheel unbalance is proposed which applies an acceleration sensor fixed on windscreen. It measures accelerations cause by wheel unbalance among other parameters. The location of the sensor is convenient for handling an autonomous device used for diagnostic purposes. Unfortunately, its mounting point is located away from wheels. Moreover, the unbalance forces created by wheels spin are dumped by suspension elements as well as the chassis itself. It indicates that unbalance acceleration will be weak in comparison to other signals coming from engine vibrations, road roughness and environmental effects. Therefore, the static unbalance detection in the standard way is considered problematic and difficult. The goal of the undertaken research is to select appropriate transformations and procedures in order to determine wheel unbalance in these conditions. In this investigation regular and short time Fourier transform were used as well as wavelet transform. It was found that the use of Fourier transforms is appropriate for static condition (constant velocity) but the results proves that the wavelet transform is more suitable for diagnostic purposes because of its ability of producing clearer output even if car is in the state of acceleration or deceleration. Moreover it was proved that in the acceleration spectrum of acceleration measured on the windscreen a significant peak can be found when car runs with an unbalanced wheel. Moreover its frequency depends on wheel rotational frequency. For that reason the diagnostic of single wheel unbalance can be made by applying this method.

Wavelet transform to quantify heart rate variability and to assess its instantaneous changes

1999 ◽  
Vol 86 (3) ◽  
pp. 1081-1091 ◽  
Author(s):  
Vincent Pichot ◽  
Jean-Michel Gaspoz ◽  
Serge Molliex ◽  
Anestis Antoniadis ◽  
Thierry Busso ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Nervous System ◽  
Fourier Transform ◽  
Wavelet Transform ◽  
Wavelet Transforms ◽  
Autonomous Nervous System ◽  
Dynamic Changes ◽  
Nonstationary Signals ◽  
Higher Doses

Heart rate variability is a recognized parameter for assessing autonomous nervous system activity. Fourier transform, the most commonly used method to analyze variability, does not offer an easy assessment of its dynamics because of limitations inherent in its stationary hypothesis. Conversely, wavelet transform allows analysis of nonstationary signals. We compared the respective yields of Fourier and wavelet transforms in analyzing heart rate variability during dynamic changes in autonomous nervous system balance induced by atropine and propranolol. Fourier and wavelet transforms were applied to sequences of heart rate intervals in six subjects receiving increasing doses of atropine and propranolol. At the lowest doses of atropine administered, heart rate variability increased, followed by a progressive decrease with higher doses. With the first dose of propranolol, there was a significant increase in heart rate variability, which progressively disappeared after the last dose. Wavelet transform gave significantly better quantitative analysis of heart rate variability than did Fourier transform during autonomous nervous system adaptations induced by both agents and provided novel temporally localized information.

Sign in / Sign up

Export Citation Format

Share Document