Methodology for fault detection in induction motors via sound and vibration signals (original) (raw)
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Induction motors are critical components for most industries and the condition monitoring has become necessary to detect faults. There are several techniques for fault diagnosis of induction motors and analyzing the startup transient vibration signals is not as widely used as other techniques like motor current signature analysis. Vibration analysis gives a fault diagnosis focused on the location of spectral components associated with faults. Therefore, this paper presents a comparative study of different time-frequency analysis methodologies that can be used for detecting faults in induction motors analyzing vibration signals during the startup transient. The studied methodologies are the time-frequency distribution of Gabor (TFDG), the time-frequency Morlet scalogram (TFMS), multiple signal classification (MUSIC) and Fast Fourier transform (FFT). The analyzed vibration signals are: one broken rotor bar, two broken bars, unbalance and bearing defects. The obtained results shown the feasibility of detecting faults in induction motors using the time-frequency spectral analysis applied to vibration signals, and the proposed methodology is applicable when it does not have current signals and only has vibration signals. Also, the methodology has applications in motors that are not fed directly to the supply line, in such cases the analysis of current signals is not recommended due to poor current signal quality.
Identification of significant intrinsic mode functions for the diagnosis of induction motor fault
The Journal of the Acoustical Society of America, 2014
For the analysis of non-stationary signals generated by a non-linear process like fault of an induction motor, empirical mode decomposition (EMD) is the best choice as it decomposes the signal into its natural oscillatory modes known as intrinsic mode functions (IMFs). However, some of these oscillatory modes obtained from a fault signal are not significant as they do not bear any fault signature and can cause misclassification of the fault instance. To solve this issue, a novel IMF selection algorithm is proposed in this work.
Detection of electrical faults in induction motors using vibration analysis
Journal of Quality in Maintenance Engineering, 2013
Fault in induction motor is crucial problem in industrial processes. This paper presents the system for electrical fault detection in induction motor fed by inverter. Current spectrum with different frequency is used to fault monitoring. Faults observed includes variation of frequency, unbalance voltage, and inter turn short circuits. Through an experiment, the fault was fired and the current spectrum recorded at steady state condition. Preprocessing is performed before the identification process. It includes noise reduction using wavelet analysis and feature extraction with Principal Component Analysis (PCA). Both processes are intended to eliminate the noise, reducing the dimension of feature, and retrieve components of the optimal features for classification. Strength of identification capability using Support Vector Machine (SVM) is 83.51%. Based on the ROC (Receiver Operating Characteristic) analysis, the SVM classifier has a good enough performance. This is indicated by the sensitivity is 74.31%, specificity is 47.30% and G-Mean is 1.1028.
International Journal of Engineering Research and Technology (IJERT), 2015
https://www.ijert.org/vibration-and-acoustic-emission-signal-monitoring-for-detection-of-induction-motor-bearing-fault https://www.ijert.org/research/vibration-and-acoustic-emission-signal-monitoring-for-detection-of-induction-motor-bearing-fault-IJERTV4IS050969.pdf Vibration and acoustic emission (AE) signal monitoring are popular techniques for detecting bearing fault, the main cause in induction motor failure which can lead to catastrophic damage. This paper presents comparison between vibration and AE signal monitoring as a tool for induction motor bearing fault detection. The effectiveness of time-domain analysis is compared with frequency-domain. Statistical parameters used in time-domain include RMS, crest factor, and kurtosis whereas for frequency-domain, normal spectrum and envelope spectrum using Hilbert transform are applied. The results reveal that vibration and AE signals are effective measurement to detect bearing fault in both time-and frequency-domain.
Strojniški vestnik – Journal of Mechanical Engineering, 2011
In this paper, we propose an approach for vibration signal-based fault detection and diagnosis system applying for induction motors. The approach consists of two consecutive processes: fault detection process and fault diagnosis process. In the fault detection process, significant features from vibration signals are extracted through the scale invariant feature transform (SIFT) algorithm to generate the faulty symptoms. Consequently, the pattern classification technique using the faulty symptoms is applied to the fault diagnosis process. Hence, instead of analyzing the vibration signal to determine the induction motor faults, the vibration signal can be classified to the corresponding faulty category, which presents the induction motor fault. We also provide a framework for the pattern classification technique that is applicable to SIFT patterns. Moreover, a comparison with two other approaches in our previous work is also carried out. The testing results show that our proposed approach provides significantly high fault classification accuracy and a better performance than previous approaches.
Journal européen des systèmes automatisés/Journal européen des systèmes automaitsés, 2024
The main aim of this article is to achieve predictive maintenance by proposing reliable residuals specific to outer ring bearing faults. Our work falls within the general context of maintenance, with particular emphasis on vibration analysis techniques. For an in-depth study of the fault, we use spectral analysis of electromagnetic torque, which gives very satisfactory results compared with work based on line current or neutral voltage fault signatures due to the similarity in the mechanical nature of the fault and the signal to be studied. In fact, an analytical calculation of the electromagnetic torque has been developed to obtain the specific frequencies of the fault under consideration. To highlight our results, the simulation of the analytical calculation of this fault was implemented in MATLAB/SIMULINK using the Fast Fourier Transform method to extract fault signatures. The induction motor's performance was analyzed under various operating conditions, including both healthy and faulty states. Finally, an experimental study will support our analytical developments.
IEEE Transactions on Industrial Electronics, 2000
Induction motors are critical components for most industries. Induction motor failures may yield an unexpected interruption at the industry plant. Several conventional vibration and current analysis techniques exist by which certain faults in rotating machinery can be identified; however, they generally deal with a single fault only. Instead, in real induction machines, the case of multiple faults is common. When multiple faults exist, vibration and current are excited by several fault-related frequencies combined with each other, linearly or nonlinearly. Different techniques have been proposed for the diagnosis of rotating machinery in literature, where most of them are focused on detecting single faults and few works deal with the diagnosis and identification of multiple combined faults. The contribution of this paper is the development of a condition-monitoring strategy that can make accurate and reliable assessments of the presence of specific fault conditions in induction motors with single or multiple combined faults present. The proposed method combines a finite impulse response filter bank with high-resolution spectral analysis based on multiple signal classification for an accurate identification of the frequency-related fault. Results show the methodology potentiality as a deterministic detection technique that is suited for detecting multiple features where the fault-related frequencies are very close to those analytically reported in literature.
Induction motors are usually considered as one of the key components in various applications. To maintain the availability of induction motors, it calls for a reliable condition monitoring and prognostics strategy. Among the common induction motor faults, stator winding faults are usually diagnosed with current and voltage signals. However, if the same performance can be achieved, the use of vibration signal is favorable because the winding fault diagnostic method can be integrated with bearing fault diagnostic method which has been successfully proven with vibration signal. Existing work concerning vibration for winding faults often takes it either as auxiliary to magnetic flux, or is not able to detect the winding faults unless severity is already quite significant. This paper proposes a winding fault diagnostic method based on vibration signals measured on the mechanical structure of an induction motor. In order to identify the signature of faults, time synchronous averaging was firstly applied on the raw vibration signals to remove discrete frequency components originating from the dynamics of the shaft and/or gears, and the spectral kurtosis filtering was subsequently applied on the residual signal to emphasize the impulsiveness. For the purpose of enhancing the residual signal in practice, a demodulation technique was implemented with the help of kurtogram. A series of experiments have been conducted on a three-phase induction motor test bed, where stator inter-turn faults can be easily simulated at different loads, speeds and severity levels. The experimental results show that the proposed method was able to detect inter-turn faults in the induction motor, even when the fault is incipient.
2006 IEEE/PES Transmission & Distribution Conference and Exposition: Latin America, 2006
The paper aims to analyze three different spectral decomposition methods applied to the stator current of induction machines to detect rotor broken bars, namely Welch, Burg and Multiple Signal Classification (MUSIC). Each of these methods is based on different concepts of power spectral estimation: nonparametric, parametric and eigenvalue decomposition, respectively. The frequency resolution, variance and detection capability are different for each method according to the set of parameters used. The paper also aims to determine which method is best suited for the implementation in automated fault detection systems. The evaluation is based on the sampled current taken on a prototype machine running under different load and faulty conditions. The effect of the main parameters of each method on the capacity to detect faults is also evaluated and compared. The comparison is performed considering the ability to discriminate fault related frequencies in the corresponding power spectrum. Different window types, window length, overlap and sampling frequency are analyzed and compared.
IEEE Transactions on Power Electronics, 1999
The reliability of power electronics systems is of paramount importance in industrial, commercial, aerospace, and military applications. The knowledge about fault mode behavior of an induction motor drive system is extremely important from the standpoint of improved system design, protection, and faulttolerant control. This paper addresses the application of motor current spectral analysis for the detection and localization of abnormal electrical and mechanical conditions that indicate, or may lead to, a failure of induction motors. Intensive research effort has been for some time focused on the motor current signature analysis. This technique utilizes the results of spectral analysis of the stator current. Reliable interpretation of the spectra is difficult since distortions of the current waveform caused by the abnormalities in the induction motor are usually minute. This paper takes the initial step to investigate the efficiency of current monitoring for diagnostic purposes. The effects of stator current spectrum are described and the related frequencies determined. In the present investigation, the frequency signature of some asymmetrical motor faults are well identified using advanced signal processing techniques, such as high-resolution spectral analysis. This technique leads to a better interpretation of the motor current spectra. In fact, experimental results clearly illustrate that stator current high-resolution spectral analysis is very sensitive to induction motor faults modifying main spectral components, such as voltage unbalance and single-phasing effects.