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Tuesday, July 28, 2020 | History

2 edition of Simulation sampling for a neural based IC parametric fault diagnosis system found in the catalog.

Simulation sampling for a neural based IC parametric fault diagnosis system

Kwokming Angus Wu

Simulation sampling for a neural based IC parametric fault diagnosis system

by Kwokming Angus Wu

  • 51 Want to read
  • 25 Currently reading

Published .
Written in English

    Subjects:
  • Integrated circuits -- Testing.,
  • Neural networks (Computer science) -- Simulation methods.

  • Edition Notes

    Statementby Kwokming Angus Wu.
    The Physical Object
    Paginationix, 81 leaves, bound :
    Number of Pages81
    ID Numbers
    Open LibraryOL14708833M

    Residual Generation for Fault Diagnosis Erik Frisk Department of Electrical Engineering methods also provide the diagnosis system designer with a set of tools with well speci ed and intuitive design freedom. ii. iii Introduction to model based diagnosis .. 7 Fault modeling .. 8 Residuals and residual generators File Size: 1MB. The book presents the application of neural networks to the modelling and fault diagnosis of industrial processes. The first two chapters focus on the funda-mental issues such as the basic definitions and fault diagnosis schemes as well as a survey on ways of using neural networks in different fault diagnosis strategies.

    a rule-based fuzzy inference system leads to knowledge extraction. This mapping makes explicit the knowledge implicitly captured by the neural network during the learning stage, by transforming it into a set of rules. This method is applied to transformer fault diagnosis using dissolved gas-in-oil!!Author: Nisha Barle, Manoj Kumar Jha, M. F. Qureshi. Statistical parametric speech synthesis with neural networks Deep neural network (DNN)-based SPSS Deep mixture density network (DMDN)-based SPSS !HMM-based speech synthesis system (HTS) [4] Heiga Zen Statistical Parametric Speech Synthesis June 9th, 6 of Outline Background HMM-based statistical parametric speech synthesis (SPSS File Size: 4MB.

    simulation results for some standard circuits. Section 5 de-scribes fault diagnosis based on sensitivity of polynomial coefficients to circuit parameters and we conclude in sec-tion 6. 2. Problem Description and Sketch of Solution We shall first give an illustrative example of calculation of limits for polynomial coefficients for a simple. A Qualitative Event-based Approach to Multiple Fault Diagnosis in Continuous Systems using Structural Model Decomposition M. Daigle, A. Bregon, X. Koutsoukos, G. Biswas, and B. Pulido, Engineering Applications of Artificial Intelligence, Vol. 53, , August


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Simulation sampling for a neural based IC parametric fault diagnosis system by Kwokming Angus Wu Download PDF EPUB FB2

It describes a discriminant sampling method for analog IC parametric faults simulation for a neural-based diagnostic system to reduce the computational overhead of the neural network training phase.

Analog circuit fault simulation has not achieved the same degree of success as its digital counterpart owing to the difficulty in modeling the more complex analog : A Wu. Abstract. This article presents experimental results which show feedforward neural networks are well-suited for analog IC fault diagnosis.

Boundary band data (BBD) measurement selection is used to reduce the computational overhead of the FFN training phase. We compare the diagnostic accuracy between traditional statistical classifiers Cited by: 6.

It describes a discriminant sampling method for analog IC parametric faults simulation for a neural-based diagnostic system to reduce the computational overhead of the neural network training phase.

Abstract. This article presents experimental results which show feedforward neural networks are well-suited for analog IC fault diagnosis. Boundary band data (BBD) measurement selection is used to reduce the computational overhead of the FFN training phase. We compare the diagnostic accuracy between traditional statistical classifiers Cited by: 6.

A new method of analog IC fault diagnosis is proposed in this paper, which is based on wavelet neural network ensemble (WNNE) technique and Adaboost algorithm.

This makes the way of the directory be of use in fault, and enhances the validity of the fault by: 1. A Neural-Based Analog IC Parametric Fault Diagnostic System with Discriminant Simulation Sampling.

Wu A. Page Count: Published: 01 November View Abstract Buy PDF (K) $ DOI: /JTEJ. Measurement and Interpretation of Fatigue Crack Growth in Aluminum Alloy Using Acoustic Emission Monitoring.

Gong Z., DuQuesnay D. An evolutionary method for analogue integrated circuits diagnosis is presented in this paper. The method allows for global parametric faults localization at the prototype stage of life of an analogue integrated circuit. The presented method is based on the circuit under test response base and the advanced features classification.

A global parametric fault (GPF) is a multiple and correlat- ed parametric fault affecting a large part of or even a whole chip [5].

The cause of GPF is usually the effect of manu- facturing process incorrect parameters, seldom – the effect of process parameters natural variation or Cited by: 9. Section 2 introduces the basic idea of fault diagnosis based on nonlinear system modelling and frequency analysis.

Section 3 is dedicated to the derivation of a new algorithm for the determination of NOFRFs, which is the key technique to the new fault diagnosis method proposed in the present by: 4. Fault diagnosis is crucial for ensuring the safe operation of complex engineering systems. These systems often exhibit hybrid behaviors, therefore, model-based diagnosis methods have to be based on hybrid system models.

Most previous work in hybrid systems diagnosis has focused either on parametric or discrete faults. Embedding Neural Networks in Expert Systems The key to successful fault diagnosis using the combined methodology is the integration of the neural networks and expert systems.

Embedding a neural network within an expert system appears to be an effective architecture for a process fault diagnostic system (Figure 2).

Based on the construction of complex network, this paper proposes a novel approach for minimizing ambiguity in parametric fault diagnosis of analog circuits. The fault features corresponding to redundant fault samples are removed, and hence abundant fault features are Cited by: 9.

In this paper, a defect-oriented parametric test method for analog integrated circuits based on neural network analysis of power supply current using wavelet decomposition preprocessing is : Sergey Mosin.

techniques, such as neural networks and fuzzy logic [1], [2]. The other approach uses models of engine performance and is known as model-based fault diagnostics [3], [4]. Model-based diagnostics mainly consists of combining theoretical knowledge with test/flight data. Here, an estimated system model is compared to a nominal system model.

The. ACUT based on inserted faults and component tolerances. The main objective of this paper is to design, analyze, evaluate, and verify the parametric fault detection approach for analogue circuits using a simulation environment.

The proper ATPG is designed to sweep the applying sinusoidal. In order to solve the problem of fault diagnosis method for analog IC diagnosis, the method based on Adaptive Neural-fuzzy Inference System (ANFIS) is proposed.

Using subtractive clustering and Particle Swarm Optimization (PSO)-hybrid algorithm as a tool for building the fault diagnosis model, then, the model of fault diagnosis system was used to the circuit fault by: 5.

In this paper, an Artificial Neural Network (ANN) based system was used to solve the problem of intelligent big-end bearing knock fault diagnosis in Internal Combustion (IC) engine.

In such systems, hybrid models have to be employed for correct tracking and diagnosis. The majority of hybrid systems diagnosis work, however, has focused on either discrete or parametric fault diagnosis. In contrast, we present an integrated model-based approach to diagnosing both parametric and discrete faults in hybrid by: 6.

For BPNN-based analog fault diagnosis, the most important work is selection of training samples of the circuit under test (CUT). The usual approaches use node voltages or frequency response of the circuit as fault features; however, it is totally not the intrinsic feature of the fault by: Afterwards, these features are calculated for various fault cases.

A fault table is constructed, which consists of the feature vector values under different fault cases. This table is further used to train the neural network and this trained neural network is used for fault detection and diagnosis. Proposed system block diagram is shown in Cited by: 4.

Now she is a Ph.D. candidate in Electronics at Tsinghua University. Her current research interests include analog circuit fault diagnosis, neural networks and fault tolerant computing.

Yang Shiyuan was born in Shanghai, China, on Nov. 15, He received his B.S. degree and his M.S. degree from Tsinghua University in and : Naihong Wei, Shiyuan Yang, Shibai Tong.In this paper, a novel voltage fault detection method is proposed based on the service and management center for electric vehicles system of electric vehicles.

The concept of the interclass correlation coefficient is first introduced and then the interclass correlation coefficient is applied to analyze battery short circuit fault by capturing Cited by: More, a neural network is designated for the purpose of diagnosis of the single [17] and global parametric [22, 25] faults.

Another technique for fault diagnosis has been proposed in [18], where measurements are transformed in multi-dimentional space. An algorithm for multiple fault diagnosis has been described in.