Ambiguity groups and testability

Stenbakken, Gerard N.; Souders, T. M.; Stewart, Gemma

doi:10.1109/19.39034

Cited by 69 publications

(32 citation statements)

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“…The simplest algorithm is the Naïve Bayes Classifier (NBC), although Bayesian Networks (BN), used mainly in the analysis of industrial systems [7] and Hidden Markov Models (HMM) [8] are applied as well. Finally, distance-based approaches make decision about the fault of the SUT by calculating the distance between the symptoms' vectors forming the dictionary (data set L containing n labeled vectors of m symptoms -see (2)) and the actual set of measured symptoms s a .…”

Section: Diagnostic Architecturementioning

confidence: 99%

“…how the premises were used to draw conclusions) is required. The most popular diagnostic methods, exploited in practice, are Artificial Neural Networks (ANN) [1,2]. This paper presents the taxonomy, structural and operational details of ANN used for the diagnostic purposes.…”

mentioning

confidence: 99%

“…<ǔŘǷȭɁʊǜǔƋʊ Ɂǌ ŘȭŘǚɁǷ ʊ˿ʊǜƬȧʊ ˁʊǔȭǷ ÄʁǜǔǞƋǔŘǚ ÂƬˁʁŘǚ ÂƬǜ˹Ɂʁǘʊ which are subsets of SUT parameters hardly distinguishable from each other [2]. Elimination of AG is done by increasing the number of accessible nodes or introducing additional symptoms, which allow for separating various faults.…”

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confidence: 99%

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Diagnostics of analog systems using Artificial Neural Networks

Bilski¹

2017

PAR

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ŎƬ˘˹ŘǚŘ ʊǔǄ ȭŘ ǘɁʁ˘˿ʊǜŘȭǔƬ ˘ Řʁǜ˿ǘˁȦˁ ȭŘ ˹ŘʁˁȭǘŘƋǒ ǚǔƋƬȭƋǖǔ ,ʁƬŘǜǔ˸Ƭ ,ɁȧȧɁȭʊ Ę˘ȭŘȭǔƬ ŘˁǜɁʁʊǜ˹Ř ʯƖ1 . IntroductionDiagnostics of analog systems is currently a wide domain with numerous applications, closely related to the types of analyzed objects. With the increasing number of such systems (including mechanical and electronic elements), the set of usable approaches also increases. Contrary to the digital object, its analog counterpart is characterized by the continuous transmission and processing of information. Therefore the system may also be in the infinite number of states, generating continuous signals. This makes the diagnostics of analog systems especially difficult. The most popular approaches used in the domain belong to Artificial Intelligence (AI). Their advantages include the autonomous operation (without the input of the human operator), high accuracy with generalization abilities and (in most cases) the ability to update knowledge through the additional learning procedure. Disadvantages include the dependency of the acquired diagnostic knowledge on the available data (use cases) presented during the training of the diagnostic module. Sometimes explanation of the reasoning process (i.e. how the premises were used to draw conclusions) is required. The most popular diagnostic methods, exploited in practice, are Artificial Neural Networks (ANN) [1,2]. This paper presents the taxonomy, structural and operational details of ANN used for the diagnostic purposes. The structure of the paper is as follows. Section 2 presents diagnostic principles important from the AI-application point of view. In Section 3 the generic diagnostic architecture is introduced with the focus on internal modules. Section 4 covers the ANN work regime from the diagnostic point of view. In Section 5 the main architectures of ANN with their applications are presented. Section 6 presents diagnostics of the 5 th order lowpass filter using the RBF ANN-based classifier. In Section 7 summary and future prospects of the discussed ANN are provided. Aims of the diagnosticsDiagnostics is aimed at determining the actual state of the analyzed System Under Test -SUT, based on the observations of the measured signals y(t) being responses to the known excitation x(t). Similarly to the medical and veterinary diagnostics, in technical sciences non-invasive approaches are preferred, where the information about the system's behavior is available only through the input and output (accessible and partially accessible) nodes (with the maximum set of nodes being hidden, i.e. inaccessible). Therefore the diagnostic system is expected to establish relations (in the form of the unknown function f()) between the observable features of output signals and configuration of parameters p defining the SUT's work regime [1]. The latter can be based on the human expert's experience or the mathematical description of the system. Alternatively, simulations of the SUT model may provide the information about how it works and how changes in parameters influence behavior o...

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Section: Diagnostic Architecturementioning

confidence: 99%

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confidence: 99%

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Diagnostics of analog systems using Artificial Neural Networks

Bilski¹

2017

PAR

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“…1 Therefore, the normalized error plots for these devices, shown in Fig. 4, are also identical; equivalently, both devices belong to the same ambiguity group [7], [8].…”

Section: Examplementioning

confidence: 99%

On the robustness of R-2R ladder DACs

Kennedy

2000

IEEE Trans. Circuits Syst. I

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“…For a CUT with components (1 ≤ ≤ ), fault modeling is to determine the model parameters , , and for each according to (15), where parameters , , and are corresponding to , , and , respectively. In most actual analog cases, (14) is hard to derive from transfer function directly.…”

Section: Introductionmentioning

confidence: 99%

Fault Modeling and Testing for Analog Circuits in Complex Space Based on Supply Current and Output Voltage

Tian²,

Guo³

2015

Journal of Applied Mathematics

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This paper deals with the modeling of fault for analog circuits. A two-dimensional (2D) fault model is first proposed based on collaborative analysis of supply current and output voltage. This model is a family of circle loci on the complex plane, and it simplifies greatly the algorithms for test point selection and potential fault simulations, which are primary difficulties in fault diagnosis of analog circuits. Furthermore, in order to reduce the difficulty of fault location, an improved fault model in threedimensional (3D) complex space is proposed, which achieves a far better fault detection ratio (FDR) against measurement error and parametric tolerance. To address the problem of fault masking in both 2D and 3D fault models, this paper proposes an effective design for testability (DFT) method. By adding redundant bypassing-components in the circuit under test (CUT), this method achieves excellent fault isolation ratio (FIR) in ambiguity group isolation. The efficacy of the proposed model and testing method is validated through experimental results provided in this paper.

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Ambiguity groups and testability

Cited by 69 publications

References 3 publications

Diagnostics of analog systems using Artificial Neural Networks

Diagnostics of analog systems using Artificial Neural Networks

On the robustness of R-2R ladder DACs

Fault Modeling and Testing for Analog Circuits in Complex Space Based on Supply Current and Output Voltage

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