scholarly journals Test Generation Algorithm for Fault Detection of Analog Circuits Based on Extreme Learning Machine

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Jingyu Zhou ◽  
Shulin Tian ◽  
Chenglin Yang ◽  
Xuelong Ren
Keyword(s):  
Extreme Learning Machine ◽  
Analog Circuits ◽  
Test Generation ◽  
Cost Effective ◽  
Fault Classification ◽  
The Novel ◽  
Analog Device ◽  
Generation Algorithm ◽  
New Process ◽  
Learning Machine

This paper proposes a novel test generation algorithm based on extreme learning machine (ELM), and such algorithm is cost-effective and low-risk for analog device under test (DUT). This method uses test patterns derived from the test generation algorithm to stimulate DUT, and then samples output responses of the DUT for fault classification and detection. The novel ELM-based test generation algorithm proposed in this paper contains mainly three aspects of innovation. Firstly, this algorithm saves time efficiently by classifying response space with ELM. Secondly, this algorithm can avoid reduced test precision efficiently in case of reduction of the number of impulse-response samples. Thirdly, a new process of test signal generator and a test structure in test generation algorithm are presented, and both of them are very simple. Finally, the abovementioned improvement and functioning are confirmed in experiments.

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 Fault Classification System for Switchgear CBM from an Ultrasound Analysis Technique Using Extreme Learning Machine

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6279
Author(s):  
Sanuri Ishak ◽  
Chong Tak Yaw ◽  
Siaw Paw Koh ◽  
Sieh Kiong Tiong ◽  
Chai Phing Chen ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Classification System ◽  
Fault Classification ◽  
Surface Discharge ◽  
Test Results ◽  
Analysis Technique ◽  
Artificial Neural Network Ann ◽  
Learning Machine ◽  
Interface Screen ◽  
Wrong Interpretation

Currently, the existing condition-based maintenance (CBM) diagnostic test practices for ultrasound require the tester to interpret test results manually. Different testers may give different opinions or interpretations of the detected ultrasound. It leads to wrong interpretation due to depending on tester experience. Furthermore, there is no commercially available product to standardize the interpretation of the ultrasound data. Therefore, the objective is the correct interpretation of an ultrasound, which is one of the CBM methods for medium switchgears, by using an artificial neural network (ANN), to give more accurate results when assessing their condition. Information and test results from various switchgears were gathered in order to develop the classification and severity of the corona, surface discharge, and arcing inside of the switchgear. The ultrasound data were segregated based on their defects found during maintenance. In total, 314 cases of normal, 160 cases of the corona, 149 cases of tracking, and 203 cases of arcing were collected. Noise from ultrasound data was removed before uploading it as a training process to the ANN engine, which used the extreme learning machine (ELM) model. The developed AI-based switchgear faults classification system was designed and incorporated with the feature of scalability and can be tested and replicated for other switchgear conditions. A customized graphical user interface (GUI), Ultrasound Analyzer System (UAS), was also developed, to enable users to obtain the switchgear condition or classification output via a graphical interface screen. Hence, accurate decision-making based on this analysis can be made to prioritize the urgency for the remedial works.

Multi-fault classification based on the two-stage evolutionary extreme learning machine and improved artificial bee colony algorithm

2013 ◽  
Vol 228 (10) ◽  
pp. 1797-1807 ◽  
Author(s):  
Jian Xiao ◽  
Jianzhong Zhou ◽  
Chaoshun Li ◽  
Han Xiao ◽  
Weibo Zhang ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Artificial Bee Colony ◽  
Fault Classification ◽  
Feed Forward Neural Network ◽  
Two Stage ◽  
Node Number ◽  
Generalization Performance ◽  
Bee Colony ◽  
Hidden Biases ◽  
Learning Machine

Extreme Learning Machine (ELM) is a novel single-hidden-layer feed forward neural network with fast learning speed and better generalization performance compared with the traditional gradient-based learning algorithms. However, ELM has two issues: the hidden node number of ELM needs to be predefined and the random determination of the input weights and hidden biases lead to ill-condition problem. In this paper, a two-stage evolutionary extreme learning machine (TSE-ELM) algorithm was proposed to overcome the drawbacks of original ELM, which used an improved artificial bee colony (ABC) algorithm to optimize the input weights and hidden biases. The proposed TSE-ELM algorithm was applied on the UCI benchmark datasets and rolling bearing fault diagnosis. The numerical experimental results demonstrated that TSE-ELM had an improved generalization performance than traditional ELM and other evolutionary ELMs.

scholarly journals Intelligent Fault Classification and Location Identification Method for Microgrids Using Discrete Orthonormal Stockwell Transform-Based Optimized Multi-Kernel Extreme Learning Machine

Energies ◽  
2019 ◽  
Vol 12 (23) ◽  
pp. 4504 ◽  
Author(s):  
Teke Gush ◽  
Syed Basit Ali Bukhari ◽  
Khawaja Khalid Mehmood ◽  
Samuel Admasie ◽  
Ji-Soo Kim ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
High Performance ◽  
Test System ◽  
Operating Conditions ◽  
Fault Classification ◽  
Identification Method ◽  
Kernel Extreme Learning Machine ◽  
Location Identification ◽  
Learning Machine ◽  
Stockwell Transform

This paper proposes an intelligent fault classification and location identification method for microgrids using discrete orthonormal Stockwell transform (DOST)-based optimized multi-kernel extreme learning machine (MKELM). The proposed method first extracts useful statistical features from one cycle of post-fault current signals retrieved from sending-end relays of microgrids using DOST. Then, the extracted features are normalized and fed to the MKELM as an input. The MKELM, which consists of multiple kernels in the hidden nodes of an extreme learning machine, is used for the classification and location of faults in microgrids. A genetic algorithm is employed to determine the optimum parameters of the MKELM. The performance of the proposed method is tested on the standard IEC microgrid test system for various operating conditions and fault cases, including different fault locations, fault resistance, and fault inception angles using the MATLAB/Simulink software. The test results confirm the efficacy of the proposed method for classifying and locating any type of fault in a microgrid with high performance. Furthermore, the proposed method has higher performance and is more robust to measurement noise than existing intelligent methods.

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