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Dantong Ouyang

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7 papers

2 author rows

EAAI Journal 2025 Journal Article

A novel approach to model-based diagnosis with multiple observations

Ran Tai
Dantong Ouyang
Weiting Liu
Luyu Jiang
Liming Zhang

Model-based diagnosis (MBD) with multiple abnormal observations utilizes inconsistencies between actual and expected observations of systems to localize system faults. Current state-of-the-art algorithms still require solvers to consider a substantial number of suspected faulty components. To address this challenge, we introduce the Dual Principles with Decision Node (DPDN) algorithm. The first part of DPDN consists of two innovative principles: the Output Dependency Judgment Principle (ODJP) and the Deep Propagation Dependency Principle (DPDP). These principles are designed to identify and categorize a larger portion of components as ‘normal’, thereby broadening the idea of filtered nodes. By increasing the number of components classified as ‘normal’, the diagnostic process becomes more efficient as fewer components need to be diagnosed. The second part of DPDN integrates a newly defined decision node guided by its Decision Node Principle (DNP). This decision node, along with its corresponding principle, further bolsters the diagnostic process by classifying additional components as normal. With DPDN, complex real-world systems can reduce the number of components considered during the diagnostic process by eliminating those that are functioning normally, thereby decreasing the time required to obtain diagnoses. We conduct comparative experiments to evaluate the efficiency of our algorithm against other prevalent methods. Empirical results distinctly underscore DPDN’s superior performance in relation to other state-of-the-art algorithms.

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AAAI Conference 2022 Conference Paper

Two Compacted Models for Efficient Model-Based Diagnosis

Huisi Zhou
Dantong Ouyang
Xiangfu Zhao
Liming Zhang

Model-based diagnosis (MBD) with multiple observations is complicated and difficult to manage over. In this paper, we propose two new diagnosis models, namely, the Compacted Model with Multiple Observations (CMMO) and the Dominated-based Compacted Model with Multiple Observations (D-CMMO), to solve the problem in which a considerable amount of time is needed when multiple observations are given and more than one fault is injected. Three ideas are presented in this paper. First, we propose to encode MB- D with each observation as a subsystem and share as many system variables as possible to compress the size of encoded clauses. Second, we utilize the notion of gate dominance in the CMMO approach to compute Top-Level Diagnosis with Compacted Model (CM-TLD) to reduce the solution space. Finally, we explore the performance of our model using three fault models. Experimental results on the ISCAS-85 benchmarks show that CMMO and D-CMMO perform better than the state-of-the-art algorithms.

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EAAI Journal 2020 Journal Article

Pattern diagnosis for stochastic discrete event systems

Xuena Geng
Dantong Ouyang
Zhengang Jiang

In this paper, pattern diagnosis problem in stochastic discrete event system (SDES) is investigated. In a system, an abnormal state may be caused by the occurrence of several normal events. This kind of fault is called fault pattern. The diagnosis problem of fault pattern is defined as pattern diagnosis. Based on the notions of A-diagnosability and AA-diagnosability in SDES, the definitions of PA-diagnosability and PAA-diagnosability for pattern diagnosability in SDES are presented in this paper. In addition, a necessary and sufficient condition for an SDES to be PA-diagnosable is proposed. The goal of this paper is diagnosing fault pattern in the real systems. Three-Tank Water Level Control System and Heating, Ventilation, and Air-conditioning System are described to illustrate our algorithm. Experimental results demonstrate that pattern diagnosability in SDES is more accurate than that in DES.

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IJCAI Conference 2019 Conference Paper

Finding Justifications by Approximating Core for Large-scale Ontologies

Mengyu Gao
Yuxin Ye
Dantong Ouyang
Bin Wang

Finding justifications for an entailment is one of the major missions in the field of ontology research. Recent advances on finding justifications w. r. t. the light-weight description logics focused on encoding this problem into a propositional formula, and using SAT-based techniques to enumerate all MUSes (minimally unsatisfiable subformulas). It's necessary to import more optimized techniques into finding justifications as emergence of large-scale real-world ontologies. In this paper, we propose a new strategy which introduce local search(in short, LS) technique to compute the approximating core before extracting an exact MUS. Although it is based on a heuristic and LS, such technique is complete in the sense that it always delivers a MUS for any unsatisfiable SAT instance. Our method will find the justifications for large-scale ontologies more effectively.

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EAAI Journal 2016 Journal Article

Probabilistic logical approach for testing diagnosability of stochastic discrete event systems

Xuena Geng
Dantong Ouyang
Xiangfu Zhao
Shuang Hao

Fault diagnosis plays an important role in the prevention of harmful events in discrete event systems (DESs). Stochastic DES (SDES) is a more precise formulation of DES. In this paper, a novel approach that uses probabilistic logic to diagnose SDES is investigated. SDES is formalized as a set of probabilistic logical formulas. Moreover, a logical diagnoser is presented. Fault diagnosis of SDES has two properties: A-diagnosability and AA-diagnosability. On the basis of resolution principle, an algorithm is proposed to test A-diagnosability and AA-diagnosability of the SDES. Experimental results demonstrate that our algorithm improves the accuracy and efficiency of verifying diagnosability of SDES.

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ECAI Conference 2008 Conference Paper

Model-Based Diagnosis of Discrete Event Systems with an Incomplete System Model

Xiangfu Zhao
Dantong Ouyang

Model-based diagnosis of discrete event systems (DESs) is more and more active in artificial intelligence. However, there has been always a very restrictive assumption in the previous works that the model of a given DES is complete, including all nominal behaviors and all possible failure behaviors of the system. In order to relax this so restrictive assumption, in this paper, model-based diagnosis of a DES with an incomplete system model is investigated. A new concept of "P-synchronization product" of finite state automata is proposed, by which the P-diagnosis of the DES with an incomplete system model is easily put forward. It is also shown that the traditional synchronization product of finite state automata can be seen as a special situation of P-synchronization product. In addition, an ideal heuristic way from theoretical view to improve the P-synchronization product is discussed as well.

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TCS Journal 2005 Journal Article

An improved model-based method to test circuit faults

Xiaochun Cheng
Dantong Ouyang
Jiang Yunfei
Chengqi Zhang

This paper presents an improved model-based reasoning method to test circuit faults. The testing procedure is applicable even when the target system contains multiple faulty modes. Using our method, the observation could be planned appropriately to guarantee correct solutions to be in the restricted candidate space. The existent consistency-checking method and abductive reasoning method are special cases of our method. The relationship between the testing procedure and the corresponding prime implication is analyzed for algorithmic implementation.

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