基于模型的诊断问题分解及其算法

对诊断问题的分解进行了研究 ,给出了基于模型诊断问题分解的判定定理 ,刻画了利用系统观测值和参量假定例化值分解诊断问题 ,提出了有条件可分解诊断问题的概念 ,进一步刻画了基于模型的诊断问题分解 ,对如何利用参量假定例化值分解诊断问题给出了最可能优先算法 ,并对该算法的正确性、完备性及复杂性进行了证明 .

基于模型诊断的分步求解

对诊断问题的分解进行研究,给出了候选诊断的分解与组合定理.在此基础上,提出了利用分步求解方法实现诊断分解的算法,并对算法的正确性、完备性和复杂性进行了证明.实验结果表明,分步求解方法明显提高了包含多个输出的系统的诊断效率.与利用变量假定例化值分解诊断问题的方法相比,该算法能提高了效率并且扩大了适用范围.

利用元件替换测试求诊断

主要填补了对系统参量不可观测或观测成本高的诊断测试研究这一空白,提出了替换测试作为诊断测试一种新的可选择方法.在提出相关替换测试概念的基础上,利用元件替换对系统观测值的影响刻画了诊断的判定、新冲突的生成.在此基础上,提出了替换分解诊断问题的概念;刻画了诊断问题的替换分解等相关问题;研究了直接利用正常元件替换几个子系统交集元件分解待诊断系统的方法.其结果能够改善诊断与测试的效率、降低诊断成本,并为研究诊断问题分解提供理论依据.

基于SF6气体状态分析的SF6断路器在线监测和故障诊断

在简单介绍SR断路器各状态量监测原理的基础上，通过测量SF6断路器中气体温度、压力、湿度和气体分解物等参数，应用所设计的DSP和红外光谱技术的硬件系统和软件流程，实现对SF6断路器的综合在线监测和故障诊断，对电网运行中的SR断路器综合在线监测和故障诊断具有一定的参考价值。

A Fault Diagnosis Approach for Broken Rotor Bars Based on EMD and Envelope Analysis

Empirical Mode Decomposition (EMD) used to deal with non-linear and non-stable signals,is a time-frequency analytical method that has been developed recently. In this paper the EMD method is used to filter the noise from the stator current signal that arises when rotor bars break. Then a Hilbert Transform is used to extract the envelope from the filtered signal. With the EMD method again,the frequency band containing the fault characteris-tic-frequency components,2sf,can be extracted from the signal's envelope. The last step is to use a Fast Fourier Trans-form (FFT) method to extract the fault characteristic frequency. This frequency can be detected in actual data from a faulty motor,as shown by example. Compared to the Extend Park Vector method this method is proved to be more sen-sitive under light motor load.

Diagnosis of Valve-Slap of Diesel Engine with EEMD-EMD-AGST Approach

A hybrid of ensemble empirical mode decomposition and empirical mode decomposition (EEMD-EMD) is introduced to diagnose the valve-slap vibration signal,which is relative to the dominant combustion knock vibration signal given out by a diesel engine around the top dead center (TDC).The time-frequency representations of intrinsic mode functions (IMFs) decomposed by EEMD-EMD are obtained by adaptive generalized S transform (AGST).A type 493 diesel engine was used for the experiment,and the result indicates that the valve-slap of the diesel engine is serious,and the vibration frequencies are higher than the combustion knock.With EEMD-EMD-AGST approach,the valve-slap can be identified by the vibration analysis of the diesel engine.

Impulsive component extraction using shift-invariant dictionary learning and its application to gear-box bearing early fault diagnosis

The impulsive components induced by bearing faults are key features for assessing gear-box bearing faults.However,because of heavy background noise and the interferences of other vibrations,it is difficult to extract these impulsive components caused by faults,particularly early faults,from the measured vibration signals.To capture the high-level structure of impulsive components embedded in measured vibration signals,a dictionary learning method called shift-invariant K-means singular value decomposition(SI-K-SVD)dictionary learning is used to detect the early faults of gear-box bearings.Although SI-K-SVD is more flexible and adaptable than existing methods,the improper selection of two SI-K-SVD-related parameters,namely,the number of iterations and the pattern lengths,has an adverse influence on fault detection performance.Therefore,the sparsity of the envelope spectrum(SES)and the kurtosis of the envelope spectrum(KES)are used to select these two key parameters,respectively.SI-K-SVD with the two selected optimal parameter values,referred to as optimal parameter SI-K-SVD(OP-SI-K-SVD),is proposed to detect gear-box bearing faults.The proposed method is verified by both simulations and an experiment.Compared to the state-of-the-art methods,namely,empirical model decomposition,wavelet transform and K-SVD,OP-SI-K-SVD has better performance in diagnosing the early faults of a gear-box bearing.

A bearing fault diagnosis method based on sparse decomposition theory

The bearing fault information is often interfered or lost in the background noise after the vibration signal being transferred complicatedly, which will make it very difficult to extract fault features from the vibration signals. To avoid the problem in choosing and extracting the fault features in bearing fault diagnosing, a novelty fault diagnosis method based on sparse decomposition theory is proposed. Certain over-complete dictionaries are obtained by training, on which the bearing vibration signals corresponded to different states can be decomposed sparsely. The fault detection and state identification can be achieved based on the fact that the sparse representation errors of the signal on different dictionaries are different. The effects of the representation error threshold and the number of dictionary atoms used in signal decomposition to the fault diagnosis are analyzed. The effectiveness of the proposed method is validated with experimental bearing vibration signals.

Multi-scale bistable stochastic resonance array: A novel weak signal detection method and application in machine fault diagnosis

Weak signal detection based on stochastic resonance （SR） can hardly succeed when noise intensity exceeds the optimal value of SR. This paper explores a novel parallel bistable SR array mechanism by decomposed multi-scale noises from input signal. A smoother output with lower noise is obtained from the combination of colored noise SR ellect and parallel bistable SR array. The influence of noise intensity and array size on the SR effect and output noise intensity is analyzed through numerical simu- lation. A signal detection method based on the new SR mechanism and normalized scale transform is proposed for the case of heavy background noise. Simulation is conducted to confirm the effectiveness of parameter tuning and amplitude tuning of normalized scale transform on the proposed SR model. The proposed method has three advantages： the input noise intensity of each unit is reduced by wavelet decomposition; the output noise level decreases due to array ensemble average; the SR effect of each unit is optimized by normalized scale transform for high frequency signal. Experiment on bearing inner and outer race fault diagnosis has verified the effectiveness and advantages of the proposed SR model in comparison with traditional SR method and kurlogram.

Nanoparticulate X-ray CT contrast agents

X-ray computed tomography(CT) has been widely used as a powerful diagnostic tool in clinics because it can provide high-resolution 3D tomography of the anatomic structure based on the distinctive X-ray absorptions between different tissues. Currently, CT contrast agents are mainly small iodinated molecules, which suffer from drawbacks such as short blood- retention time, nonspecific in vivo biodistribution, and renal toxicity. Utilization of nanoparticles as potential CT contrast agents to overcome the aforementioned issues has advanced rapidly. In this mini review, we introduce current research efforts in the development of nanoparticulate CT contrast agents and discuss the challenges for additional breakthroughs in this field.

Application of complete ensemble intrinsic time-scale decomposition and least-square SVM optimized using hybrid DE and PSO to fault diagnosis of diesel engines

Targeting the mode-mixing problem of intrinsic time-scale decomposition （ITD） and the parameter optimization problem of least-square support vector machine （LSSVM）, we propose a novel approach based on complete ensemble intrinsic time-scale decomposition （CEITD） and LSSVM optimized by the hybrid differential evolution and particle swarm optimization （HDEPSO） algorithm for the identification of the fault in a diesel engine. The approach consists mainly of three stages. First, to solve the mode-mixing problem of ITD, a novel CEITD method is proposed. Then the CEITD method is used to decompose the nonstationary vibration signal into a set of stationary proper rotation components （PRCs） and a residual signal. Second, three typical types of time-frequency features, namely singular values, PRCs energy and energy entropy, and AR model parameters, are extracted from the first several PRCs and used as the fault feature vectors. Finally, a HDEPSO algorithm is proposed for the parameter optimization of LSSVM, and the fault diagnosis results can be obtained by inputting the fault feature vectors into the HDEPSO-LSSVM classifier. Simulation and experimental results demonstrate that the proposed fault diagnosis approach can overcome the mode-mixing problem of ITD and accurately identify the fault patterns of diesel engines.