Application of dynamic uncertain causality graph in spacecraft fault diagnosis: Logic cycle

Yao, Quanying; Zhang, Qin; Liu, Peng; Yang, Ping; Zhu, Mingshan; Wang, Xiaochen

doi:10.1063/1.4981577

Cited by 4 publications

(4 citation statements)

References 5 publications

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“…An initiative way is to sample on the joint conditional probability distribution of each layer instead of single variable n X in step 2. In other word, use (19) to replace (12) in Step 3, where W(l) means the set of variables in layer l and Sl(t) is the joint states of them in cycle t. However, the state space of W(l) is exponential to the scale of W(l). As a result, this method is low efficient and not practical.…”

Section: Figure 8 Example For Expression Absorptionmentioning

confidence: 99%

“…In [11], DUCG was applied in the online fault diagnoses of the generator system of a nuclear power plant. In [12], DUCG was applied for the power supply system of a space craft. In [13], DUCG was applied for fault diagnoses on gearbox.…”

Section: Introductionmentioning

confidence: 99%

“…Because the time complexity of multiplication between two kk  matrices is3 k , the time complexity for expanding each path is Calculate the conditional probability distribution of Xn as shown in (9), then assign Xn with a new state Sn(t) which is sampled based on states of other nodes during the cycle.St during sampling cycle t-1, the expanding process for each Xn will end at its parent variables according to the recursive algorithm. Denote pn W as the set of parents of Xn, then (9) can be reformed as(12) .…”

mentioning

confidence: 99%

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A New Inference Algorithm of Dynamic Uncertain Causality Graph Based on Conditional Sampling Method for Complex Cases

Nie

Zhang

2021

IEEE Access

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Dynamic Uncertain Causality Graph (DUCG) is a recently developed model for fault diagnoses of industrial systems and general clinical diagnoses. In some cases, however, when state-unknown intermediate variables are many, the variable state combination explosion may appear and result in the inefficiency or even disability in DUCG inference. Monte Carlo sampling is a typical algorithm to solve this type of problem. However, since the calculation values are very small, a huge number of samplings are needed. This paper proposes an algorithm based on conditional stochastic simulation, which obtains the final calculation result from the expectation of the conditional probability in sampling cycles instead of counting the sampling frequency. Compared with the early presented recursive algorithm, the proposed algorithm requires much less computation time in the case when state-unknown intermediate variables are many. An example for diagnosing Viral Hepatitis B shows that the new algorithm performs 3 times faster than the early presented recursive algorithm and the error ratio is within 2.7%.

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Section: Figure 8 Example For Expression Absorptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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A New Inference Algorithm of Dynamic Uncertain Causality Graph Based on Conditional Sampling Method for Complex Cases

Nie

Zhang

2021

IEEE Access

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“…Dynamic Uncertain Causality Graph (DUCG) is a probabilistic graphical model developed in recent years [10,11]. It is based on domain knowledge, independent of training data, and has strong interpretation ability and robustness, high diagnosis accuracy and high calculation efficiency.…”

Section: Introductionmentioning

confidence: 99%

The Application of Dynamic Uncertain Causality Graph Based Diagnosis and Treatment Unification Model in the Intelligent Diagnosis and Treatment of Hepatitis B

Deng

Zhang

2021

Symmetry

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Although hepatitis B is widespread, it is hard to cure. This paper presents a new and more accurate model for the diagnosis and treatment of hepatitis B. Based on previous research, the diagnosis and treatment modes were combined into one. By adding more influencing factors and risk factors, the overall diagnosis and treatment model will be further expanded, and a richer and more detailed overall diagnosis and treatment model will be constructed. Reverse logic gates are used in the model to improve the accuracy of the treatment planning. The new unified model is more accurate in subdividing diagnosis results, and it is more flexible and accurate in providing dynamic treatment plans. The prediction process and the static diagnosis process of the model are symmetric, and the related sub-graph is symmetric in structure. In addition, an algorithm for predicting the response probability of treatment scheme is developed, so as to predict the subsequent treatment effects of the current treatment scheme, such as the probability of drug resistance. The results show that this method is more accurate than other available systems, and it has encouraging diagnostic accuracy and effectiveness, which provides a promising help for doctors in diagnosing hepatitis B.

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Optimization for Logic Operation Problem in Stochastic Simulation of Dynamic Causality Uncertain Graph

Nie

Zhang

2021

2021 3rd International Conference on Applied Machine Learning (ICAML)

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Application of dynamic uncertain causality graph in spacecraft fault diagnosis: Logic cycle

Cited by 4 publications

References 5 publications

A New Inference Algorithm of Dynamic Uncertain Causality Graph Based on Conditional Sampling Method for Complex Cases

A New Inference Algorithm of Dynamic Uncertain Causality Graph Based on Conditional Sampling Method for Complex Cases

The Application of Dynamic Uncertain Causality Graph Based Diagnosis and Treatment Unification Model in the Intelligent Diagnosis and Treatment of Hepatitis B

Optimization for Logic Operation Problem in Stochastic Simulation of Dynamic Causality Uncertain Graph

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