Fault detection based on signal reconstruction with Auto-Associative Extreme Learning Machines

An Analog Circuit Fault Diagnosis Approach Based on Improved Wavelet Transform and MKELM

A R T I C L E I N F O A B S T R A C T Article history: Received: 08 December, 2016 Accepted: 15 January, 2017 Online: 28 January, 2017 Extreme Learning Machine (ELM) is a high effective learning algorithm for the single hidden layer feed forward neural networks. Compared with the existing neural network learning algorithm it solves the slow training speed and over-fitting problems. It has been used in different fields and applications such as biomedical engineering, computer vision, remote sensing, chemical process and control and robotics. It has better generalization stability, sparsity, accuracy, robustness, optimal control and fast learning rate This paper introduces a brief review about ELM and MLELM, describing the principles and latest research progress about the algorithms, theories and applications. Next, Multilayer Extreme Learning Machine (MLELM) and other state-of-the-art classifiers are trained on this suitable training feature vector for classification of data. Deep learning has the advantage of approximating the complicated function and mitigating the optimization difficulty associated with deep models. Multilayer extreme learning machine is a learning algorithm of an Artificial Neural Network (ANN) which takes to be good for deep learning and extreme learning machine. This review presents a comprehensive view of these advances in ELM and MLELM which may be worthy of exploring in the future.

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Recent Trends in ELM and MLELM: A review

This paper presents parametric fault diagnosis in analog circuit using machine learning algorithm. Single and multiple parametric fault diagnosis based on Simulation before test approach are considered in this study. A benchmark State Variable Filter circuit has been used as an example circuit for experiment validations. Extreme learning machine based autoencoder (ELM-AE) is proposed in this paper for feature reduction and classification. The transfer function of the benchmark circuit is simulated by performing Monte-Carlo analysis, the features are obtained to form fault dictionary for single and double faults in such way that the dictionary contains unique values for faulty and fault free configuration of the circuit. The input test patterns from the fault dictionary which are required for testing are reduced to improve the classification by autoencoder. Autoencoder is used as pre-processor to learn and reduce the features before performing classification by the extreme learning machine (ELM) algorithm. The results obtained by the proposed algorithm is compared with the other ELM empirical models like extreme learning machine, kernel extreme learning machine and an evolutionary algorithm called self-adaptive evolutionary extreme learning machine to analyze the performance of the proposed algorithm. The experimental results show that the ELM-AE contributes the higher diagnosis accuracy than other ELM models referred in this paper.

Multiple parametric fault diagnosis using computational intelligence techniques in linear filter circuit

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.

An Analog Circuit Fault Diagnosis Method Based on Circle Model and Extreme 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 above mentioned improvement and functioning are confirmed in experiments.

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Xuelong Ren

Papers

Test generation algorithm for fault detection of analog circuits based on extreme learning machine