Dynamic Uncertain Causality Graph for Knowledge Representation and Probabilistic Reasoning: Statistics Base, Matrix, and Application

Zhang, Qin; Dong, Chunling; Cui, Yan; Yang, Zhihui

doi:10.1109/tnnls.2013.2279320

Cited by 65 publications

(34 citation statements)

References 17 publications

(39 reference statements)

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“…We may get better results in finding out the fault node than [14]. Compared to [21], [20], we have an automatic algorithm to discover the root cause.…”

Section: B Anomaly In Node(s)mentioning

confidence: 99%

Data-driven root-cause analysis for distributed system anomalies

Liu

Lore

Sarkar

2017

2017 IEEE 56th Annual Conference on Decision and Control (CDC)

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Modern distributed cyber-physical systems encounter a large variety of anomalies and in many cases, they are vulnerable to catastrophic fault propagation scenarios due to strong connectivity among the sub-systems. In this regard, root-cause analysis becomes highly intractable due to complex fault propagation mechanisms in combination with diverse operating modes. This paper presents a new data-driven framework for root-cause analysis for addressing such issues. The framework is based on a spatiotemporal feature extraction scheme for distributed cyber-physical systems built on the concept of symbolic dynamics for discovering and representing causal interactions among subsystems of a complex system. We present two approaches for root-cause analysis, namely the sequential state switching (S 3 , based on free energy concept of a Restricted Boltzmann Machine, RBM) and artificial anomaly association (A 3 , a multi-class classification framework using deep neural networks, DNN). Synthetic data from cases with failed pattern(s) and anomalous node are simulated to validate the proposed approaches, then compared with the performance of vector autoregressive (VAR) model-based root-cause analysis. Real dataset based on Tennessee Eastman process (TEP) is also used for validation. The results show that: (1) S 3 and A 3 approaches can obtain high accuracy in root-cause analysis and successfully handle multiple nominal operation modes, and (2) the proposed tool-chain is shown to be scalable while maintaining high accuracy.

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“…We may get better results in finding out the fault node than [14]. Compared to [21], [20], we have an automatic algorithm to discover the root cause.…”

Section: B Anomaly In Node(s)mentioning

confidence: 99%

Data-driven root-cause analysis for distributed system anomalies

Liu

Lore

Sarkar

2017

2017 IEEE 56th Annual Conference on Decision and Control (CDC)

View full text Add to dashboard Cite

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“…As a technical development, the dynamic uncertain causality graph (DUCG) method which deals with the causal link between uncertain information with graphical expression and probability measurement is proposed (Zhang et al, 2014;Zhang, 2015a). DUCG is a probabilistic graphical model which intuitively expresses a causal relationship among variables in an explicit pattern, and uses a "chaining" inference algorithm to achieve efficient reasoning.…”

Section: Introductionmentioning

confidence: 99%

Intelligent diagnosis of jaundice with dynamic uncertain causality graph model

Hao

Geng

Fan

et al. 2017

J. Zhejiang Univ. Sci. B

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Jaundice is a common and complex clinical symptom potentially occurring in hepatology, general surgery, pediatrics, infectious diseases, gynecology, and obstetrics, and it is fairly difficult to distinguish the cause of jaundice in clinical practice, especially for general practitioners in less developed regions. With collaboration between physicians and artificial intelligence engineers, a comprehensive knowledge base relevant to jaundice was created based on demographic information, symptoms, physical signs, laboratory tests, imaging diagnosis, medical histories, and risk factors. Then a diagnostic modeling and reasoning system using the dynamic uncertain causality graph was proposed. A modularized modeling scheme was presented to reduce the complexity of model construction, providing multiple perspectives and arbitrary granularity for disease causality representations. A "chaining" inference algorithm and weighted logic operation mechanism were employed to guarantee the exactness and efficiency of diagnostic reasoning under situations of incomplete and uncertain information. Moreover, the causal interactions among diseases and symptoms intuitively demonstrated the reasoning process in a graphical manner. Verification was performed using 203 randomly pooled clinical cases, and the accuracy was 99.01% and 84.73%, respectively, with or without laboratory tests in the model. The solutions were more explicable and convincing than common methods such as Bayesian Networks, further increasing the objectivity of clinical decision-making. The promising results indicated that our model could be potentially used in intelligent diagnosis and help decrease public health expenditure.

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“…To overcome the above problems, DUCG [3,4], a new intelligent diagnosis model, is presented. Conditional linkage events in DUCG can effective deal with the multi-condition of spacecraft; DUCG [5] can effectively solve the causal logic cycle in spacecraft fault diagnosis; furthermore, DUCG [6] can also predict faults with uncertain evidence with continuous variable.…”

Section: Introductionmentioning

confidence: 99%

Application of dynamic uncertain causality graph in spacecraft fault diagnosis: Multi-conditions

Yao¹,

Zhang²,

Liu³

et al. 2017

AIP Conference Proceedings

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Abstract. Intelligent diagnosis system is applied to fault diagnosis in spacecraft. Dynamic Uncertain Causality Graph (DUCG) is a new probability graphic model with many advantages. In this paper, DUGG is applied to fault diagnosis in spacecraft: introducing conditional functional events into ordinary DUCG to deal with spacecraft multi-conditions. Now, DUCG has been tested in 16 typical faults with 100% diagnosis accuracy.

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Dynamic Uncertain Causality Graph for Knowledge Representation and Probabilistic Reasoning: Statistics Base, Matrix, and Application

Cited by 65 publications

References 17 publications

Data-driven root-cause analysis for distributed system anomalies

Data-driven root-cause analysis for distributed system anomalies

Intelligent diagnosis of jaundice with dynamic uncertain causality graph model

Application of dynamic uncertain causality graph in spacecraft fault diagnosis: Multi-conditions

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