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Vibration monitoring and analysis is a powerful and recommended tool for preventive maintenance and early detection of impending failures in rotary machine. The demand for cost efficient, reliable and safe rotating machinery requires accurate fault diagnosis, classification and prognosis systems. This work presents a study to explore the performances of bearing fault diagnosis by using wavelet neural...
Vertical mill is an important equipment existing in cement industry. In the normal operation process, defect may easily occurs at some components due to the severe working environment. Taking the bearing component as an example, this paper proposed a novel fault diagnosis method based on entropy feature to discriminate the ball fault, outer race fault and inner race fault. This algorithm consists...
Motor current signature analysis (MCSA) provides a non-invasive approach to diagnose the gear fault. The performance of MCSA at different speed and load torque conditions is studied using Fast Fourier Transform (FFT) to extract fault features. The results show that it would be better to perform fault diagnosis under low speed and heavy load conditions. And it fails when adopting FFT to diagnose the...
To extract the impulsive and cyclostationary features of repetitive transients in bearing fault diagnosis simultaneously, an anti-symmetric real Laplace wavelet is optimized by two fitness functions. The first is to maximize kurtosis of the squared envelope of the filtered signal to capture the impulsiveness and the second is to maximize kurtosis of the squared envelope spectrum to capture the cyclostationarity...
As a valid method of time-frequency analysis, Wavelet transform (WT) can offer great help for gearbox fault diagnosis. However, it requires much human expertise and prior knowledge to diagnose the faulty conditions of gearbox according to the time-frequency distribution. In addition, the coupling of different failures and noise makes it hard to accurately diagnose the running conditions of the gearbox...
A fault diagnosis method is proposed, which is based on Empirical Wavelet Transform (EWT), Auto-Regressive (AR) model and Fuzzy C-Mean clustering (FCM) clustering algorithm, in order to solve the problem of fault category is difficult to identify of rolling bearing fault signal. In this method, the original signal of the rolling bearing is decomposed by the EWT, and several AM-FM components are obtained...
In this paper, a fault diagnosis method of three-phase inverters of permanent magnet synchronous motor(PMSM) is proposed, which is based on the complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN) and support vector machine(SVM) according to the measured α — β phase currents. This method can get higher diagnostic accuracy and have preferable against disturbances compared with...
The applications of monitoring the equipment online are often limited by the practical signal processing, limited by storage and transferring capacities. The efficiency is a key problem. Thus, a novel highly efficient feature extraction model for evaluating the equipment performance is proposed, which consists of the probabilistic Principal components analysis and the second generation wavelet analysis...
Monitoring acoustic emission from mechanical systems is an effective non-invasive way of diagnosing both system performance as well as short-term/long-term system failures. A difficulty however in fault detection in such systems is inter-class variability caused by non-uniform or unknown load conditions which decrease the classification accuracy. In this paper, a scattering transform is employed to...
The faults of rolling element bearings can result in the deterioration of machine operating conditions; how to assess the working condition and identify the fault of the rolling element bearing has become a key issue for ensuring the safe operation of modern rotating machineries. This paper presents a novel hybrid approach that detects bearing faults and monitors the operating status of rolling element...
Motor is a kind of imperative driving device, whether a motor can monitor its state precisely and diagnose fault timely have a profound impact. This paper mainly investigates the improvement of the general method of motor defect diagnosis to achieve higher accuracy. Unfortunately, every classifier has their own respective advantages and disadvantages, using the typical machine learning methods separately...
In deployment of smart grid system today's power system infrastructure are changing due to inclusion of large number of distributed generation units which are being integrated into power systems at distribution level. In such a environment of Smart Grid; resilience and self healing property of power system is very crucial. Fault diagnosis plays very important task in achieving these qualities and...
In today's smart grid environment existing fault diagnosis methodology emphasizes the need of more advanced, accurate and sturdy strategies. New modified methodology using latest sensing, communication and controlling devices is essential in order to make system self healing and resilient. The paper presents use of Symlet mother wavelet function for fault diagnosis method using multi resolution analysis...
Correct incipient fault diagnosis is crucial to the health management of the analog circuits, though remaining challenging. This paper presents a novel fault diagnosis method to diagnose ordinary soft fault and incipient soft fault for linear analog circuit. Due to the presence of analog circuit noise stress and the tolerance of the components, the linear circuit responses are considered as a stochastic...
Bearing fault diagnosis has attracted significant attention over the past few decades. In this paper, Alpha-stable distribution was introduced for feature extraction from faulty bearing vibration signals. Such a non-Gaussian model can accurately describe statistical characteristic of bearing fault signals with impulsive behavior. After extracting feature vectors by Alpha-stable distribution parameters,...
Wavelet analysis based analog circuit fault detecting sensitivity and feature extraction optimization methods are studied. Good localization on time-frequency domain of wavelet transformation provides better feature representation for faulty circuits than unitary time or frequency domain analysis. However, different wavelets express diverse resolution for fault recognition. Root mean square (RMS)...
Rotating machinery fault diagnosis is of great importance for preventing catastrophic accidents. Effective signal processing techniques are in urgent demands to extract the fault features contained in the collected vibration signals. In this paper, a new sparsity-assisted feature extraction method is proposed for rotating machinery fault diagnosis. It is implemented using the tunable Q-factor wavelet...
This paper proposes an approach for a 2-D representation of Shannon wavelets for highly reliable fault diagnosis of multiple induction motor defects. Since the wavelet transform is efficient for analyzing non-stationary and non-deterministic vibration signals, this paper utilizes wavelet coefficients deduced from the Shannon mother wavelet function with varying dilation and translation parameters...
In response to the roller bearing fault, the method of fault recognition of rolling bearings based on Morphological Wavelet and Least Squares Support Vector Machine was presented. Morphological Wavelet was applied to signal preprocessing, energy feature method to signal extraction. And then, we carry out the recognition of fault types using Least Square Support Vector Machine algorithm. After testing...
In this paper, in order to improve the precision of rotor vibration fault diagnosis with nonlinear and nonstationary signals, the wavelet correlation feature scale entropy (WCFSE) method is proposed to quantitatively analyze and diagnose the rotor vibration fault feature by fusing wavelet correlation filter method and information entropy theory. Firstly, the formation features of weak faults are picked...
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