Support Vector Machine For Fault Diagnosis Research Articles

A Fault Diagnosis Method for Analog Circuits Based on Improved TQWT and Inception Model

A soft fault in an analog circuit is a symptom where the parameter range of a component exists symmetrically to the left and right of its nominal value and exceeds a specific range. The proposed method uses the Grey Wolf Optimization (GWO) optimized tunable Q-factor wavelet transform (TQWT) algorithm for feature refinement, the Inception model for feature extraction, and an SVM for fault diagnosis. First, the Q-factor is optimized to make it more compatible with the signal. Second, the signal is decomposed, and a single-branch reconstruction is performed using the TQWT to extract features adequately. Then, fault feature extraction is conducted using the Inception model to obtain multiscale features. Finally, a Support Vector Machine (SVM) is used to complete the entire fault diagnosis process. The proposed method is comprehensively evaluated using the Sallen–Key bandpass filter circuit and the four-op-amp biquad high-pass filter circuit widely used in electronic systems. The experimental results prove that the proposed method outperforms the existing methods in terms of diagnosis accuracy and reliability.

Support vector machine fault diagnosis based on sparse scaling convex hull

In solving the problems encountered when implementing support vector machine (SVM) fault diagnosis, the convex-hull-covering model of the dataset cannot reflect the effective distribution of the samples, and the fault identification accuracy of the original high-dimensional feature set is low. A sparse scaling convex-hull based SVM classification method is proposed and applied to the fault diagnosis of roller bearings. The dimensionality reduction of the features of the sample set is carried out by the random forest (RF) algorithm. First, the optimized sample subsets are obtained by sparse approximation, and the reduction coefficient of the convex hull of the optimized sample set is adjusted, hence the convex hulls of various sample sets are linearly separable. Second, to solve the problem of low fault recognition accuracy of the original high-dimensional feature set, the importance of features is evaluated by RF, and some redundant features are removed. Finally, the SVM model is constructed by the closest points between the convex hulls. Through fault diagnosis on two different bearing datasets, the experimental results and related theories show that the proposed method has high performance in bearing fault diagnosis.

Statistical Feature Extraction Combined with Generalized Discriminant Component Analysis Driven SVM for Fault Diagnosis of HVDC GIS

Accurately identifying the types of insulation defects inside a gas-insulated switchgear (GIS) is of great significance for guiding maintenance work as well as ensuring the safe and stable operation of GIS. By building a set of 220 kV high-voltage direct current (HVDC) GIS experiment platforms and manufacturing four different types of insulation defects (including multiple sizes and positions), 180,828 pulse current signals under multiple voltage levels are successfully measured. Then, the apparent discharge quantity and the discharge time, two inherent physical quantities unaffected by the experimental platform and measurement system, are obtained after the pulse current signal is denoised, according to which 70 statistical features are extracted. In this paper, a pattern recognition method based on generalized discriminant component analysis driven support vector machine (SVM) is detailed and the corresponding selection criterion of involved parameters is established. The results show that the newly proposed pattern recognition method greatly improves the recognition accuracy of fault diagnosis in comparison with 36 kinds of state-of-the-art dimensionality reduction algorithms and 44 kinds of state-of-the-art classifiers. This newly proposed method not only solves the difficulty that phase-resolved partial discharge (PRPD) cannot be applied under DC condition but also immensely facilitates the fault diagnosis of HVDC GIS.

Research on Fault Diagnosis Model of Rotating Machinery Vibration Based on Information Entropy and Improved SVM

Based on the concept of information entropy, this paper analyzes typical nonlinear vibration fault signals of steam turbine based on spectrum, wavelet and HHT theory methods, and extracts wavelet energy spectrum entropy, IMF energy spectrum entropy, time domain singular value entropy and frequency domain power spectrum entropy as faults. The feature is supported by a support vector machine (SVM) as a learning platform. The research results show that the fusion information entropy describes the vibration fault more comprehensively, and the support vector machine fault diagnosis model can achieve higher diagnostic accuracy.

A Novel Fault Diagnostic Approach for DC-DC Converters Based on CSA-DBN

Effective fault diagnosis for mission-critical and safety-critical systems has been an essential and mandatory technique to reduce failure rate and prevent unscheduled shutdown. In this paper, to realize fault diagnosis for a closed-loop single-ended primary inductance converter, a novel optimization deep belief network (DBN) is presented. First, wavelet packet decomposition is adopted to extract the energy values from the voltage signals of four circuit nodes, as the fault feature vectors. Then, a four-layer DBN architecture including input and output layers is developed. Meanwhile, the number of neurons in the two hidden layers is selected by the crow search algorithm (CSA) with training samples. Not only the hard faults such as open-circuit faults and short-circuit faults but also the soft faults such as the component degradation of power MOSFET, inductor, diode, and capacitor are considered in this study. Finally, these fault modes are isolated by CSA-DBN. Compared with the back-propagation neural network and support vector machine fault diagnosis methods, both simulation and experimental results show that the proposed method has a higher classification accuracy that proves its effectiveness and superiority to the other methods.

Joint Feature and Model Selection for SVM Fault Diagnosis in Solid Oxide Fuel Cell Systems

This paper describes an original technique for the joint feature and model selection in the context of support vector machine (SVM) classification applied as a diagnosis strategy in model-based fault detection and isolation (FDI). We demonstrate that the proposed technique contributes to the solution of an open research problem: to design a robust FDI procedure, correctly functioning with different operating conditions and fault sizes, specifically settled for an electric generation system based on solid oxide fuel cells (SOFCs). By using a quantitative model of the generation system coupled to an optimized SVM classifier, a satisfactory FDI procedure is achieved, which is robust against modeling and measurement errors and is compliant with practical deployment.

The Support Vector Machine Fault Diagnosis Algorithm Based on Cloud Computing

The paper puts forward the way to solve the problem of SVM training on the large scale firstly, Then perform the experiment to verify the feasibility of scheme. In the last section, SVM fault diagnosis method based on the Mapreduce is put forward.