Identification of significant intrinsic mode functions for the diagnosis of induction motor fault (original) (raw)
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An Adaptive EMD Technique for Induction Motor Fault Detection
Journal of Signal and Information Processing
Reliable induction motor (IM) fault detection techniques are very useful in industries to diagnose IM defects and improve operational performance. An adaptive empirical mode decomposition (EMD) technology is proposed in this paper for rotor bar fault detection in IMs. As the characteristic fault frequency will change with operating conditions related to load and speed, the proposed adaptive EMD technique correlates fault features over different frequency bands and intrinsic mode function (IMF) sidebands. The adaptive EMD technique uses the first IMF to detect the fault type and the second IMF as an indicator to predict the fault severity. It can overcome the problems of the sensitivity of sideband frequencies related to the speed and load oscillations. The effectiveness of the proposed adaptive EMD technique is verified by experimental tests under different motor conditions.
Methodology for fault detection in induction motors via sound and vibration signals
Mechanical Systems and Signal Processing, 2017
Nowadays, timely maintenance of electric motors is vital to keep up the complex processes of industrial production. There are currently a variety of methodologies for fault diagnosis. Usually, the diagnosis is performed by analyzing current signals at a steady-state motor operation or during a start-up transient. This method is known as motor current signature analysis, which identifies frequencies associated with faults in the frequency domain or by the time-frequency decomposition of the current signals. Fault identification may also be possible by analyzing acoustic sound and vibration signals, which is useful because sometimes this information is the only available. The contribution of this work is a methodology for detecting faults in induction motors in steady-state operation based on the analysis of acoustic sound and vibration signals. This proposed approach uses the Complete Ensemble Empirical Mode Decomposition for decomposing the signal into several intrinsic mode functions. Subsequently, the frequency marginal of the Gabor representation is calculated to obtain the spectral content of the IMF in the frequency domain. This proposal provides good fault detectability results compared to other published works in addition to the identification of more frequencies associated with the faults. The faults diagnosed in this work are two broken rotor bars, mechanical unbalance and bearing defects.
During the operation process of the high-voltage circuit breaker, the changes of vibration signals reflect the machinery states of the circuit breaker. The extraction of the vibration signal feature will directly influence the accuracy and practicability of fault diagnosis. This paper presents an extraction method based on ensemble empirical mode decomposition) and correlation dimension and a classification method with BP (back propagation) neural network. Firstly, original vibration signals are decomposed into a finite number of stationary intrinsic mode functions (IMFs). Secondly, correlation dimension of the top four IMFs by the G-P algorithm is calculated and the characteristic vector of the vibration signal of the circuit breaker is formed. At last, the classification of characteristic parameter is realized with a simple BP neural network for fault diagnosis. The experimentation without loads indicates that the method can easily and accurately diagnose breaker faults and exploit a new road for diagnosis of high-voltage circuit breakers.
Mathematical Problems in Engineering
This paper proposes an improved Empirical Mode Decomposition (EMD) method by using variable window size median filters during the Intrinsic Mode Functions (IMFs) generation. Compared to the traditional EMD, the improved EMD, namely, Median EMD (MEMD), helps to reduce mode-mixing providing an improvement in terms of separating the fundamental frequencies per IMF. The MEMD method applies the EMD to the signal and then applies a variable window size median filter to the resulting IMFs. A narrow window is used for high frequency components where a broader window is used for the lower frequency components. The filtered IMFs are then summed again and another round of EMD is applied to yield the improved MEMD IMFs. A test setup for accelerated aging of bearings in induction motors is used for the comparison of the traditional and the improved EMD methods with the goal of finding potential bearing defects in an induction motor. The potential defect at the early stage is compared with the fa...
Empirical Mode Decomposition and Neural Networks on FPGA for Fault Diagnosis in Induction Motors
The Scientific World Journal, 2014
Nowadays, many industrial applications require online systems that combine several processing techniques in order to offer solutions to complex problems as the case of detection and classification of multiple faults in induction motors. In this work, a novel digital structure to implement the empirical mode decomposition (EMD) for processing nonstationary and nonlinear signals using the full spline-cubic function is presented; besides, it is combined with an adaptive linear network (ADALINE)-based frequency estimator and a feed forward neural network (FFNN)-based classifier to provide an intelligent methodology for the automatic diagnosis during the startup transient of motor faults such as: one and two broken rotor bars, bearing defects, and unbalance. Moreover, the overall methodology implementation into a field-programmable gate array (FPGA) allows an online and real-time operation, thanks to its parallelism and high-performance capabilities as a system-on-a-chip (SoC) solution. The detection and classification results show the effectiveness of the proposed fused techniques; besides, the high precision and minimum resource usage of the developed digital structures make them a suitable and low-cost solution for this and many other industrial applications.
Electronics, 2018
In the fault diagnosis system using empirical mode decomposition (EMD), it is important to select the intrinsic mode functions (IMFs) which contain as much fault information as possible and to alleviate the problems of mode mixing and spurious modes. An effective solution to these problems in the decomposition process can help to determine significant IMFs and to improve the performance of the fault diagnosis system. This paper describes a novel power-based IMF selection algorithm and evaluates the performance of the proposed fault diagnosis system using improved complete ensemble EMD with adaptive noise and a multi-layer perceptron neural network.
The rolling defect is one of the main causes of dysfunctions in induction machines, resulting in a significant economic loss. The main purpose of this study is an automated diagnosis of bearing defects based on vibratory analysis. The work was carried out on the Case Western Reserve University database, where the signals were acquired in different modes and operating conditions (healthy and faulty cases, speed variations, load and defect severity variations). A vector of indicators known as vector form, composed of statistical and spectral indicators for optimal performance, is extracted from the original vibration signals via processed signals using Empirical Mode Decomposition (EMD). In the classification phase, the Multi-Class Support Vector Machine (SVM) was chosen. A comparison of the results with empirical modal decomposition using one and three relevant Intrinsic Mode Functions (IMFs) was performed; the obtained results show the effectiveness of this approach. Furthermore, a comparative study was achieved on two other types of classifiers, namely, Artificial Neural Network (ANN) and Neuro-Fuzzy Network (NFN). A better efficiency in terms of time and classification rate goes to the multi-class SVM compared to the ANN and the NFN.
Applied Soft Computing, 2011
Order tracking has proved to be effective in dealing with the effects of speed variation in the analysis of rotating machinery vibration signals. To implement traditional order tracking in practice requires rotational speed information. However, it may be difficult in some cases to mount an appropriate monitoring device to obtain reliable speed information. In this paper, a novel empirical re-sampling of intrinsic mode functions obtained from empirical mode decomposition is explored, so that the approximation of order tracking effects without rotational speed is possible. At the same time, the newly introduced intrinsic cycle concept in the intrinsic mode function simplifies linking of the resultant spectra to signal variations, and is therefore beneficial for condition monitoring of rotating machines. In the paper the rationale behind the technique is first explained. Secondly, the effectiveness of the technique is demonstrated on a dynamic gear simulation model. Lastly, the technique is applied to experimental data from a gearbox test rig. Both the simulation and experimental studies corroborate the usefulness of the proposed technique.
Transactions of the Institute of Measurement and Control, 2018
Empirical Mode Decomposition (EMD) and Variational Mode Decomposition (VMD) are data-driven self-adaptive signal processing methods to decompose a complex signal into different modes of separate spectral bands, in to a number of Intrinsic Mode Functions (IMFs). While the EMD extracts modes recursively and empirically, the VMD extracts modes non-recursively and concurrently. In this paper, both the EMD and the VMD have been applied to examine their efficacy in fault diagnosis of rolling element bearing. However, all the IMFs do not contain necessary information regarding fault characteristic signature of the bearing. In order to select the effective IMF, the Dynamic Time Warping (DTW) algorithm has been employed here, which gives a measurement of similarity index between two signals. Also, correlation analysis has been carried out to select the appropriate IMFs. Finally, out of the selected IMFs, bearing characteristic fault frequencies have been determined with the envelope spectrum.
Noise Eliminated Ensemble Empirical Mode Decomposition for Bearing Fault Diagnosis
Journal of Vibration Engineering & Technologies, 2021
Although noise-assisted decomposition methods, ensemble empirical mode decomposition (EEMD) and complementary EEMD (CEEMD) can reduce the drawbacks of empirical mode decomposition (EMD), they cannot fully eliminate the presence of white noise. In this paper, a method named noise eliminated EEMD (NEEEMD) was proposed to reduce further the white noise in the intrinsic functions and keep the ensembles optimum. The NEEEMD algorithm also decomposes the ensemble of white noise signals using EMD and subtracts from the outputs of EEMD. A simulated signal was used to demonstrate the performance of NEEEMD using root-mean-square error (RRMSE) and time & envelope spectrum kurtosis (TESK). A sensitive mode (SM) selection method was proposed to select the most sensitive intrinsic mode functions (IMFs) from NEEEMD which takes multiplication of kurtosis in the time domain and energy-entropy in the frequency domain. Finally, to enhance the signal's fault-related impulses, an advanced filter called MOMEDA was applied to the most sensitive IMF. The significance of the proposed method was illustrated using the envelope spectrum from bearing signals containing different types of faults at various speeds and motor loads. The output of the proposed method, EEMD and CEEMD was compared using the envelope spectrum to identify fault characteristic impulses. Envelope spectrum analysis proved that our proposed method performed better in every case by providing more fault-related impulses.