scholarly journals Rotor failure detection of induction motors by wavelet transform and Fourier transform in non-stationary condition

2015 ◽  
Vol 1 ◽  
pp. 15-26 ◽  
Author(s):  
Cesar da Costa ◽  
Masamori Kashiwagi ◽  
Mauro Hugo Mathias
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Induction Motors ◽  
Failure Detection ◽  
Stationary Condition

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.

scholarly journals Broken Rotor Bars Fault Detection Based on Envelope Analysis Spectrum and Neural Network in Induction Motors

2018 ◽  
Vol 3 (3) ◽  
pp. 106-116
Author(s):  
Saddam BENSAOUCHA ◽  
Sid Ahmed BESSEDIK ◽  
Aissa AMEUR ◽  
Abdellatif SEGHIOUR
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fourier Transform ◽  
Induction Motors ◽  
Current Signal ◽  
Broken Rotor Bars ◽  
Stator Current ◽  
The Neural Networks ◽  
Modulus Spectrum ◽  
Load Conditions

In this paper, a study has presented the performance of a neural networks technique to detect the broken rotor bars (BRBs) fault in induction motors (IMs). In this context, the fast Fourier transform (FFT) applied on Hilbert modulus obtained via the stator current signal has been used as a diagnostic signal to replace the FFT classic, the characteristics frequency are selected from the Hilbert modulus spectrum, in addition, the different load conditions are used as three inputs data for the neural networks. The efficiency of the proposed method is verified by simulation in MATLAB environment.

scholarly journals Image analysis of the tree test using wavelet transform, singular value decomposition, and Fourier transform

2019 ◽  
Vol 90 (3) ◽  
pp. 284-293
Author(s):  
Keita Kawasugi ◽  
Kazuhisa Takemura ◽  
Yumi Iwamitsu ◽  
Hitomi Sugawara ◽  
Sakura Nishizawa ◽  
...  
Keyword(s):  
Image Analysis ◽  
Fourier Transform ◽  
Wavelet Transform ◽  
Singular Value Decomposition ◽  
Singular Value ◽  
Value Decomposition

The Study of Tire Pattern Noise by Using Wavelet Transform

2011 ◽  
Vol 105-107 ◽  
pp. 267-270 ◽  
Author(s):  
Sung Wook Hwang ◽  
Jin Hyuk Han ◽  
Ki Duck Sung ◽  
Sang Kwon Lee
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
The Other ◽  
Road Noise ◽  
Signal Process ◽  
The Road ◽  
Pattern Noise ◽  
On The Road ◽  
Non Stationary Signal ◽  
The Relationship

Tire noise is classified by pattern noise and road noise in a vehicle. Especially pattern noise has impulsive characteristics since it is generated by impacting of tire’s block on the road. Therefore, a special signal process is needed other than traditional Fourier Transform, because the characteristic of signal is varying with time. On the other hand, the pattern noise is a kind of non-stationary signal and is related to the impulsive train of pitch sequence of a block. In this paper, Wavelet Transform is applied to verify the impulse signal caused by impact of block and groove and to verify the relationship between the pattern noise and the train of pitch sequence.

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