Fault Detection in Induction Machines Using Power Spectral Density in Wavelet Decomposition (original) (raw)
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Fault Detection in Induction Machines by Using Power Spectral Density on the Wavelet Decompositions
37th IEEE Power Electronics Specialists Conference, 2006
Motor Current Signature Analysis has been successfully used in induction machines for fault diagnosis. The method however does not always achieve good results when the load torque is not constant. This paper proposes a new approach to motor fault detection, by analyzing the spectrogram and further combination of Wavelet and Power Spectral Density techniques. Theoretical development and experimental results are presented to support the research.
Case Studies in Mechanical Systems and Signal Processing, 2015
This case study presents two diagnostic methods for the detection of broken bars in induction motors with squirrel-cage type rotors: FFT method and wavelet method. The FFT method allows detecting broken rotor bar when the motor operates under a load, but if the machine is decoupled from the mechanical load, the side band components associated with broken bars do not appear. The WT is a powerful signal-processing tool used in power systems and other areas. New wavelet-based detection methods that are focused on the analysis of the startup current have been proposed for the detection of broken bars. Since the transient stator current signal is not periodic, it is not amenable to analyze the signal by FFT method. In addition, it is impossible to estimate the time of the fault occurrence using the FFT. In this paper, our main goal is to find out the advantages of wavelet transform method compared to Fourier transform method in rotor failure detection of induction motors.
2008
Motor Current Signature Analysis (MCSA) has been successfully used for fault diagnosis in induction machines. The current spectrum of the induction machine for locating characteristic fault frequencies is used in MCSA. The spectrum is obtained using a Fast Fourier Transformation (FFT) that is performed on the signal under analysis. The fault frequencies occur in the motor current spectra are unique for different motor faults. However FFT does not always achieve good results with non-constant load torque. Other signal processing methods, such as Short-time Fourier Transform (STFT) and Wavelet transforms techniques may also be used for analysis. These techniques are capable of revealing aspects of data like trends, breakdown points, discontinuities in higher derivatives, and self-similarity which are not available in FFT analysis. In the present paper, the comparisons of various techniques are discussed to analyze the experimental results obtained.
Discrete Wavelet Transform Based Rotor Faults Detection Method for Induction Machines
Intelligent Systems at the Service of Mankind, vol. 2., Ubooks, Augsburg (Germany)
The condition monitoring of the electrical machines can significantly reduce the costs of maintenance by allowing the early detection of faults, which could be expensive to repair. In this paper some results on non-invasive detection of rotor faults in wound rotor induction motors are presented. The applied method is the so-called motor current signature analysis (MCSA), an often cited and investigated diagnosis method. The method utilises the results of spectral analysis of the stator currents. Usually the FFT (Fast Fourier Transform) is used to obtain the power density vs. frequency plots to be analysed. In this paper the use of a novel versatile tool of harmonic analysis, of the wavelet transform will be presented. The proposed wavelet based detection method shows a good sensitivity. The theoretical basis of the method is proved by laboratory tests.
Fault Detection (Condition Monitoring) of Induction Motor based on Wavelet Transform
International Journal of Electronics and Electical Engineering
Presently, many condition monitoring techniques that are based on steady-state analysis are being applied to Induction motor. However, the operation of induction motor is predominantly transient, therefore prompting the development of non-stationary techniques for fault detection. In this paper we apply steady-state techniques e.g. Motor Current Signatures Analysis (MCSA) and the Extended Park’s Vector Approach (EPVA), as well as a new transient technique that is a combination of the EPVA, the Discrete Wavelet Transform and statistics, to the detection of turn faults in a induction motor. It will be shown that steady-state techniques are not effective when applied to induction motor operating under transient.
2015
This paper presents two diagnostic methods for the online detection of broken bars in induction motors with squirrel-cage type rotors. The wavelet representation of a function is a new technique. Wavelet transform of a function is the improved version of Fourier transform. Fourier transform is a powerful tool for analyzing the components of a stationary signal. But it is failed for analyzing the non-stationary signal whereas wavelet transform allows the components of a non-stationary signal to be analyzed. In this paper, our main goal is to find out the advantages of wavelet transform compared to Fourier transform in rotor failure diagnosis of induction motors.
Broken rotor bar fault detection in induction motors using Wavelet Transform
2012
The Fast Fourier Transform (FFT) method is successfully used for the broken rotor bar fault detection purpose in the induction machines. It is based on the common-steady state analysis of the motor. This method is successfully used with Motor Current Signature Analysis (MCSA) technique for last three decades. However, this method is suffered from some serious drawbacks such as; it is applicable only in the constant load condition not for the variable load. The frequency-domain methods which are commonly used need accurate slip estimation for frequency components localization in any spectrum. It is also not suitable at the no-load or light load condition of the motor. At light load condition, it is quite difficult to distinguish between healthy and faulty rotors because the characteristic of broken rotor bar fault frequencies are very close to fundamental component and their amplitude are small in comparison. As a result, detection of the fault and classification of the fault severity under light load is almost impossible. In order to overcome the above problems of the FFT based technique, the Short Time Fourier Transform (STFT) Method was proposed. The excellent feature of this method is that it is capable to diagnose broken rotor bar fault in the transient condition. The STFT method also suffered from the drawback that it shows the constant window for all the frequencies. Therefore, it shows poor frequency resolution. In order to overcome all the problems stated so far, the most recent powerful mathematical tool i.e. Wavelet Transform (WT) has been used in the rotor broken bar fault detection purpose at all loading conditions. It shows variable window size for all the frequencies. Therefore, the WT method does not have resolution problem due to its multiresolution feature. This paper investigates the detection of rotor faults in induction machines by analyzing the starting current using a newly developed quantification technique based on the wavelet transform. The- technique applies the wavelet transform to the envelope of the starting current. The envelope extraction is used to remove the strong fundamental component, which overshadows the characteristic differences between a healthy motor and a faulty motor with broken rotor bars.
Diagnostics Of Faults In Induction Motor Via Wavelet Packet Transform
IOSR Journal of VLSI and Signal Processing
This paper deals with fault diagnosis of induction machines based on the discrete wavelet transform. By using the wavelet packet decomposition, the information on the health of a system can be extracted from a signal over a wide range of frequencies. This analysis is performed in both time and frequency domains. The HAAR wavelet is selected for the analysis of the stator current. Wavelet components appear to be useful for detecting different electrical faults. Index Terms-Broken rotor bars, data-dependent selection (DDS) and data-independent selection (DIS) of the decomposition level, fault diagnosis, induction machines (IMs), motor-current signature analysis (MCSA), wavelet transform. I.
Signal Processing based Wavelet Approach for Fault Detection of Induction Motor
Condition monitoring and fault detection of induction motor have been challenging task for engineers and researchers mainly in industries as faults and failures of induction motor can lead to excessive downtimes and generate large losses in terms of maintenance and lost revenue. This motivates motor monitoring, incipient fault detection and diagnosis. Online monitoring of induction motor can lead to diagnosis of electrical and mechanical faults. Most recurrent faults in induction motor are turn to turn short circuit, bearing deterioration, and cracked rotor bar. This paper presents a signal processing based frequency domain approach using wavelet transform and Artificial neural network based algorithm for multiple fault detection in induction motor .Motor line currents are captured under various fault conditions .DWT is used for data processing and this data is then used for testing and training of ANN. Three different types of wavelets are used for signal processing to demonstrate the superiority of Db4 wavelet over other standard wavelets for accurate fault classification of induction motor. Experimentation results obtained proves the suitability of proposed algorithm.