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Empirical Mode Decomposition(EMD) is an advanced method for analyzing non-stationary signal, but there is an involved end issue in the course of getting two envelops of the data using spline interpolation. In this paper a novel method based on grey prediction model is proposed to restrain the end effect of empirical mode decomposition. In the grey prediction endpoint extension process, based on the...
In view of the nonlinear and non-stationary characteristics of fault vibration signal in roller bearing, a self-adaptive fault diagnosis method known as LMD (Local mean decomposition) is proposed. Initially the original vibration signal is decomposed into several stationary PF (product function) which possessed physical meaning and a residual component by using of LMD. Subsequently, the main components...
Online fault diagnosis has been a crucial task for industrial processes. Reconstruction-based fault diagnosis has been drawing special attentions as a good alternative to the traditional contribution plot. It identifies the fault cause by finding the specific fault subspace that can well eliminate alarming signals from a bunch of alternatives that have been prepared based on historical fault data...
In order to solve the problem that the excessive dimensions of feature vector will lead to probabilistic neural network (PNN) 's structure becoming complicated and recognition rate slowing down when we take the wavelet energy spectrum of the rolling bearing vibration signal as the feature vector, a novel approach based on wavelet energy spectrum, principal component analysis (PCA) and probabilistic...
The inconsistent diagnostic information often occurs in fault diagnosis of complex equipments. In order to improve the diagnosis precision, an integrated fault diagnosis method is proposed based on variable precision rough set (VPRS) and Naive Bayesian network classifier (NBNC). Firstly, according to the relative discernibility of the original fault diagnosis decision table, the β in VPRS is self-determined...
Rapid and accurate fault detection and diagnosis (FDD) is gaining importance for complex equipments because of the need to increase reliability and to decrease possible loss. In this paper, an intelligent fault diagnosis method is presented by using case-based reasoning (CBR) methodology to infer and classify various failures. Firstly, the case representation and the case base are established according...
For the fault diagnosis problems of the underwater vehicle sensor systems, the solution is combined by the Principal Component Analysis (PCA) and Self-Organizing Fuzzy Cerebellar Model Articulation Controller (SOFCMAC). The signal prediction model approach based on PCA and SOFCMAC is proposed in this paper. According to the history data, it can predict the signal data in the future time using the...
In this paper, we consider the problems of fault detection for heavy-haul trains equipped with electronically controlled pneumatic (ECP) brake systems. A longitudinal dynamical model which has been successfully validated is used to simulate the actual situation. Based on the model, a set of unknown input observers which are adopted to estimate locomotives' state is constructed, and observers can determine...
The traditional principal component analysis (PCA) method divides the variable space into two parts: Principal subspace and Residual subspace by orthogonal decomposition. It has been widely used in fault detection process, but it is difficult to interpret the modes of the fault because of model compound effect, and the ability to distinguish the pattern which is no significant is affected. In industrial...
This paper studies the fault diagnosis of inertia navigation unit which plays an important role in inertia navigation system. The method chosen in the fault diagnosis is combined Genetic Algorithm and wavelet neural network. Wavelet transform will effectively handle the collected inertia navigation unit signal. The characteristic signals extracted will be regarded as inputs to the neural network....
This paper presents a robust adaptive fault estimation design method to solve the problem of actuator fault estimation for CCBII Braking System with disturbances and model uncertainties. The proposed robust adaptive fault estimator which is based on an adaptive observer can improve the speed of fault estimation. By transforming the system model into a special coordinate basis (SCB) form, we can structure...
In this paper, a fault diagnosis method based on support vector machine (SVM) is proposed for gas turbine bearing. Firstly, through analysis and processing of vibration signals, the singular value decomposition related EEMD technique is applied to extract feature vectors of the signals. The results are used as the input of SVM classifier model. Then, by using the SVM network intelligence, the turbine...
This paper proposes an H∞ deconvolution design for linear systems with output state time delay. The basic idea of our study is to eliminate the time delays of system and transform it to a delay-free system by the bicausal change of coordinates approach. Then, we design H∞ deconvolution for the delay-free systems, which is equivalent to the original system with time delays. We analyze the general structure...
This paper presents a nonlinear robust fault tolerant control scheme to force the faulty system to asymptotically track a given desired trajectory via cascade observers. Focusing on the external disturbance and actuator faults of the nonlinear non-minimum phase vertical take-off and landing aircraft, two decoupled reduced order observers are designed to estimate the unknown disturbance and fault....
A fault detection method based on empirical likelihood is presented to deal with the incipient fault in process and equipment. The problem of incipient fault detection is studied in the view of distribution test by a moving window approach. The original fault detection problem is transformed into distribution test, and a set of empirical likelihood values is computed. Based on the likelihood values,...
As one of the most widely used parts and components of rotating machineries, fault detection of rolling bearing is of great significance. In this paper, a new method named EMD-DPCA is proposed based on Empirical Mode Decomposition (EMD) and Dynamic Principal Component Analysis (DPCA). Firstly, the vibration signals are decomposed by EMD and Intrinsic Mode Functions (IMFs) are achieved. Then DPCA model...
A new fault diagnosis method for rolling element bearing is proposed based on empirical mode decomposition (EMD) and fisher discriminant analysis (FDA). First, non-stationary vibration signals are processed by applying EMD technique, and stationary IMF components are obtained. Then, fault feature vectors with the moving time-lagged windows are composed using the absolute values of IMF components of...
A new fault diagnosis method of analog circuits is proposed in this paper. The method is based on Volterra series and SVM. This paper introduces the basic theory of Volterra series and SVM and deduces the proposed identification method in detail. The identification method is applied to the fault diagnosis of a non-linear analog circuit. The simulation result shows that the proposed method can obviously...
The application of the multifractal theory in gearbox fault diagnosis has been studied in the paper, and the fractal characteristics of gearbox vibration signals is shown. Based on using EMD, the improved algorithm of multifractal spectrum is put forward, and is applying to extract the fault feature. At last, the fault diagnosis application in gear box takes as the example to prove the feasibility...
Rolling bearings vibration signal is complex and non-stationary signal. In order to diagnose the bearing failures accurately and quickly, propose an approach about rolling bearing fault diagnosis, which is based on LS-SVM and LMD. Firstly, decompose the original vibration signal by LMD (Local Mean Decomposition LMD) to get a series of PF(Production Function, PF); secondly, establish the AR model of...
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