scholarly journals Parametric Fault Diagnosis of Analog Circuits Based on a Semi-Supervised Algorithm

Symmetry ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 228 ◽  
Author(s):  
Ling Wang ◽  
Dongfang Zhou ◽  
Hui Tian ◽  
Hao Zhang ◽  
Wei Zhang
Keyword(s):  
Fault Diagnosis ◽  
Analog Circuits ◽  
Health Management ◽  
Learning Algorithm ◽  
Training Model ◽  
Supervised Machine Learning ◽  
Fault Classification ◽  
Linear Discriminant ◽  
Diagnosis Rate ◽  
Parametric Fault

The parametric fault diagnosis of analog circuits is very crucial for condition-based maintenance (CBM) in prognosis and health management. In order to improve the diagnostic rate of parametric faults in engineering applications, a semi-supervised machine learning algorithm was used to classify the parametric fault. A lifting wavelet transform was used to extract fault features, a local preserving mapping algorithm was adopted to optimize the Fisher linear discriminant analysis, and a semi-supervised cooperative training algorithm was utilized for fault classification. In the proposed method, the fault values were randomly selected as training samples in a range of parametric fault intervals, for both optimizing the generalization of the model and improving the fault diagnosis rate. Furthermore, after semi-supervised dimensionality reduction and semi-supervised classification were applied, the diagnosis rate was slightly higher than the existing training model by fixing the value of the analyzed component.

Machinery Fault Diagnosis Based on Supervised Locally Linear Embedding

2014 ◽  
Vol 536-537 ◽  
pp. 49-52
Author(s):  
Xiang Wang ◽  
Yuan Zheng
Keyword(s):  
Fault Diagnosis ◽  
Learning Algorithm ◽  
Classification Performance ◽  
Fault Classification ◽  
Locally Linear Embedding ◽  
Diagnosis Method ◽  
Linear Embedding ◽  
Low Dimensional ◽  
Machinery Fault Diagnosis ◽  
Locally Linear

Fault diagnosis is essentially a kind of pattern recognition. In this paper propose a novel machinery fault diagnosis method based on supervised locally linear embedding is proposed first. The approach first performs the recently proposed manifold learning algorithm locally linear embedding on the high-dimensional fault signal samples to learn the intrinsic embedded multiple manifold features corresponding to different fault modes. Supervised locally linear embedding not only can map them into a low-dimensional embedded space to achieve fault feature extraction, but also can deal with new fault samples. Finally fault classification is carried out in the embedded manifold space. The ball bearing fault signals are used to validate the proposed fault diagnosis method. The results indicate that the proposed approach obviously improves the fault classification performance and outperforms the other traditional approaches.

scholarly journals Construction of an Expert System Based on Fuzzy Logic for Diagnosis of Analog Electronic Circuits

2015 ◽  
Vol 61 (1) ◽  
pp. 77-82 ◽  
Author(s):  
Damian E. Grzechca
Keyword(s):  
Fuzzy Logic ◽  
Fault Diagnosis ◽  
Expert System ◽  
Analog Circuits ◽  
Fuzzy Rule ◽  
Sensitivity Matrix ◽  
Fault Dictionary ◽  
Common Strategy ◽  
Parametric Fault ◽  
Rule System

Abstract The paper presents construction of the fuzzy logic system to analog circuits parametric fault diagnosis. The classical dictionary construction is replaced by fuzzy rule system. The first part refers to analog fault diagnosis, its techniques, approaches and goals. It clarifies common strategy and define differences between detecting, locating and identifying a fault in analog electronic circuit. The second part is focused on a creation of fuzzy rule expert system with use of sensitivity functions and known circuit topology. To detect, locate and identify a faulty element in a circuit the sensitivity matrix is used. The advantage of the method is its utilization in all, AC, DC and time domain. The fuzzy system, like the classical fault dictionary, can detect and locate single catastrophic faults and, on the contrary to the classical one, it also detects and locates parametric faults. Moreover, it allows identification of these faults, such that sign of the faulty parameter deviation is designated. The method has deterministic character as well as it can be applied on the verification and production stage

Mahalanobis-ANOVA criterion for optimum feature subset selection in multi-class planetary gear fault diagnosis

2021 ◽  
pp. 107754632110291
Author(s):  
Setti Suresh ◽  
VPS Naidu
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Fault Diagnosis ◽  
Learning Algorithm ◽  
Selection Criterion ◽  
Feature Subset Selection ◽  
Fault Classification ◽  
Support Vector ◽  
Feature Subset ◽  
Gear Fault

The empirical analysis of a typical gear fault diagnosis of five different classes has been studied in this article. The analysis was used to develop novel feature selection criteria that provide an optimum feature subset over feature ranking genetic algorithms for improving the planetary gear fault classification accuracy. We have considered traditional approach in the fault diagnosis, where the raw vibration signal was divided into fixed-length epochs, and statistical time-domain features have been extracted from the segmented signal to represent the data in a compact discriminative form. Scale-invariant Mahalanobis distance–based feature selection using ANOVA statistic test was used as a feature selection criterion to find out the optimum feature subset. The Support Vector Machine Multi-Class machine learning algorithm was used as a classification technique to diagnose the gear faults. It has been observed that the highest gear fault classification accuracy of 99.89% (load case) was achieved by using the proposed Mahalanobis-ANOVA Criterion for optimum feature subset selection followed by Support Vector Machine Multi-Class algorithm. It is also noted that the developed feature selection criterion is a data-driven model which will contemplate all the nonlinearity in a signal. The fault diagnosis consistency of the proposed Support Vector Machine Multi-Class learning algorithm was ensured through 100 Monte Carlo runs, and the diagnostic ability of the classifier has been represented using confusion matrix and receiver operating characteristics.

scholarly journals Fighting the COVID-19 Infodemic in New articles and False Publications: NeoNet, a Text-based Supervised Machine Learning Algorithm

2021 ◽  
Author(s):  
Mohammad AR Abdeen ◽  
Ahmed Abdeen Hamed ◽  
Xindong Wu
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Safety Information ◽  
Training Model ◽  
Supervised Machine Learning ◽  
News Article ◽  
False Information ◽  
Network Training ◽  
Medical Publication ◽  
Real World Datasets

The spread of the Coronavirus pandemic has been accompanied by an infodemic. The false information that is embedded in the infodemic affects people’s ability to have access to safety information and follow proper procedures to mitigate the risks. This research aims to target the falsehood part of the infodemic, which prominently proliferates in news articles and false medical publications. Here, we present NeoNet, a novel supervised machine learning text mining algorithm that analyzes the content of a document (news article, a medical publication) and assigns a label to it. The algorithm is trained by TFIDF bigram features which contribute a network training model. The algorithm is tested on two different real-world datasets from the CBC news network and Covid-19 publications. In five different fold comparisons, the algorithm predicted a label of an article with a precision of 97-99 %. When compared with prominent algorithms such as Neural Networks, SVM, and Random Forests NeoNet surpassed them. The analysis highlighted the promise of NeoNet in detecting disputed online contents which may contribute negatively to the COVID-19 pandemic.

scholarly journals The Method of Communication System Fault Diagnosis Based on Deep Belief Net

2021 ◽  
Vol 15 ◽  
pp. 978-985
Author(s):  
Juan Li ◽  
Bin Chen
Keyword(s):  
Fault Diagnosis ◽  
Communication System ◽  
Health Management ◽  
Bottom Layer ◽  
Original Data ◽  
Fault Classification ◽  
Deep Belief Net ◽  
Communication Experiment ◽  
And Training ◽  
Communication Equipment

To meet the need of fault diagnosis for military communication system, an effective method based on deep belief (DBN) net is proposed. During the fault diagnosis, the bottom layer of DBN model is used to receive the input fault signals to extract the fault features and the fault classification results will be outputted after softmax classified. Accordingly, algorithms for DBN model and training and RBM parameter learning have been designed. To reduce the running time, parallel solutions based on MapReduce framework have been provided. In order to test and verify the effect of DBN fault diagnosis, the communication experiment system is built in the laboratory which the output signals of the transmitter and the receiver are measured and collected as the original data for further learning and training. Compared with the traditional fault diagnosis methods, it can be found that DBN method has high accuracy in fault diagnosis and the process is simple and friendly. It is impossible to realize real-time diagnosis and online diagnosis for the communication system. The research can be applicated to the health management of communication equipment, and it will provide advanced technical support and software program for the health of communication equipment

scholarly journals An Analog Circuit Fault Diagnosis Method Based on Circle Model and Extreme Learning Machine

2020 ◽  
Vol 10 (7) ◽  
pp. 2386
Author(s):  
Sumin Guo ◽  
Bo Wu ◽  
Jingyu Zhou ◽  
Hongyu Li ◽  
Chunjian Su ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Analog Circuits ◽  
Analog Circuit ◽  
Classification Performance ◽  
Fault Classification ◽  
Diagnosis Method ◽  
Circuit Fault Diagnosis ◽  
Learning Machine ◽  
Circle Model

The fault diagnosis of analog circuits faces problems, such as inefficient feature extraction and fault identification. To solve the problems, this paper combines the circle model and the extreme learning machine (ELM) into a fault diagnosis method for the linear analog circuit. Firstly, a circle model for the voltage features of fault elements was established in the complex domain, according to the relationship between the circuit response, element position and circuit topology. To eliminate the impacts of tolerances and signal aliasing, the 3D feature was introduced to make the indistinguishable features in fuzzy groups distinguishable. Fault feature separability is very important to improve the fault diagnosis accuracy. In addition, an effective classier can improve the precision and the time taken. With less computational complexity and a simpler process, the ELM algorithm has a fast speed and a good classification performance. The effectiveness of the proposed method is verified by simulation. The simulation results show the ELM-based algorithm classifier with the circle model can enhance precision and reduce time taken by about 80% in comparison with other methods for analog circuit fault diagnosis. To sum up, this proposed method offers a fault diagnosis method that reduces the complexity in generating fault features, improves the isolation probability of faults, speeds up fault classification, and simplifies fault testing.

scholarly journals New Aspects of Fault Diagnosis of Nonlinear Analog Circuits

2015 ◽  
Vol 61 (1) ◽  
pp. 83-93 ◽  
Author(s):  
Michał Tadeusiewicz ◽  
Stanisław Hałgas ◽  
Andrzej Kuczyński
Keyword(s):  
Fault Diagnosis ◽  
Analog Circuits ◽  
Multiple Solutions ◽  
Analytical Form ◽  
Homotopy Continuation ◽  
Technological Parameters ◽  
Mathematical Algorithms ◽  
Parametric Fault ◽  
Nonlinear Analog Circuits ◽  
Nonlinear Analog

Abstract The paper is focused on nonlinear analog circuits, with the special attention paid to circuits comprising bipolar and MOS transistors manufactured in micrometer and submicrometer technology. The problem of fault diagnosis of this class of circuits is discussed, including locating faulty elements and evaluating their parameters. The paper deals with multiple parametric fault diagnosis using the simulation after test approach as well as detection and location of single catastrophic faults, using the simulation before test approach. The discussed methods are based on diagnostic test, leading to a system of nonlinear algebraic type equations, which are not given in explicit analytical form. An important and new aspect of the fault diagnosis is finding multiple solutions of the test equation, i.e. several sets of the parameters values that meet the test. Another new problems in this area are global fault diagnosis of technological parameters in CMOS circuits fabricated in submicrometer technology and testing the circuits having multiple DC operating points. To solve these problems several methods have been recently developed, which employ different concepts and mathematical tools of nonlinear analysis. In this paper they are sketched and illustrated. All the discussed methods are based on the homotopy (continuation) idea. It is shown that various versions of homotopy and combinations of the homotopy with some other mathematical algorithms lead to very powerful tools for fault diagnosis of nonlinear analog circuits. To trace the homotopy path which allows finding multiple solutions, the simplicial method, the restart method, the theory of linear complementarity problem and Lemke’s algorithm are employed. For illustration four numerical examples are given

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