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Scientific modeling and analysis for fault spreading process is a promising way for guaranteeing the safe, reliable, and efficient operation of complex system. However, the representing and reasoning of uncertain, time-varying, and sophisticated dependences are difficult, especially for the complex issues of dynamic negative feedback loops in multi...
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... effects from X nk (t q ), namely, extended causal effect. Thus, X nk (t q +1 ) does not need the traversing connections to the variables that already exist in the upstreaming causality chains of X nk (t q ), so as to avoid the repetition and redundancy. For example, X 1,1 (t 1 ) and X 1,1 (t 2 ) (likely for X 2,1 (t 1 ) and X 2,1 (t 2 )) in Fig. 6, X 1,1 (t 2 ) inherits the causalities of X 1,1 (t 1 ), so the logic expanding of X 1,1 (t 2 ) is X 1,1 (t 2 ) = X 1,1 (t 1 ) = F 1,1;4 (t 2; t 1 )B 4 ...
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... To address these problems, this study introduces the theory of Dynamic Uncertain Causality Graph (DUCG) [18][19][20][21] into the field of network attack analysis. The methodology of DUCG represents uncertain and complex causalities in a compact fashion of graph model and provides efficient probabilistic reasoning methods for inferences and predictions in complex systems. ...
... The causality simplification aims at eliminating the causalities and events that are unconcerned, unreasonable, or inconsistent with the observed evidence from the causality graph [21], thereby reducing the complexity of model and subsequent calculations. For the cases that the causality graph is large in scale and contains multiple independent root cause event hypotheses, it is necessary to decompose the attack causality graph into multiple sub-DUCAG based on each of the root event hypotheses. ...
... The calculation of the posterior probability of the Hypothesis event can be performed by using the weighted logical inference method proposed in [21]. This method allows for the determination of the posterior probability of the specific hypothesis event, Pr (H i,j |E), where H i,j represents the root event hypothesis. ...
In the context of cloud computing, network attackers usually exhibit complex, dynamic, and diverse behavior characteristics. Existing research methods, such as Bayesian attack graphs, lack evidence correlation and real-time reflection of the network attack events, and high computational complexity for attack analysis. To solve these problems, this study proposes a Dynamic Uncertain Causal Attack Graph (DUCAG) model and a Causal Chain-based Risk Probability Calculation (CCRP) algorithm. The DUCAG model is constructed to represent the uncertain underlying causalities among network attack events, and the CCRP algorithm aims at dynamically updating the causality weights among different network attack events and attacker hypotheses based on alarm information and causal chain reasoning process. By causality simplification and causality reasoning methods, the CCRP efficiently predicts the attacker behaviors and potential attack likelihood under uncertain time-varying attack situations, and is robust to the incompleteness and redundancy in alarm information. Four experiments under different attack scenarios demonstrate that, the DUCAG model can effectively characterize and predict the complex and uncertain attack causalities, in a manner of high time efficiency. The proposed method has application significance on cloud computing platforms by dynamically evaluating network security status, predicting the future behaviors of attackers, and assisting in adjusting network defense strategies.
... The original DUCG is decomposed into several sub-DUCGs; each DUCG contains one root event Bi, remarked as DUCG(Bi); (2) Obtain Slice_DG (Bi, tm Slice_DG (Bi, tm) is the intraslice causality graph at time tm. According to the evidence at tm, the DUCG (Bi)s are decomposed into several Slice_DG (Bi,tm)s based on the DUCG simplification rules [26,34]. Each Slice_DG (Bi,tm) contains one root fault, and scribes the causality between the root fault Bi and the evidence E (tm) at tm; (3) Cubi (Bi,tm) generation. ...
... is the intraslice causality graph at time t m . According to the evidence E (t m ) at t m , the DUCG (B i )s are decomposed into several Slice_DG (B i ,t m )s based on the cubic DUCG simplification rules [26,34]. Each Slice_DG (B i ,t m ) contains one root fault, and it describes the causality between the root fault B i and the evidence ...
This study presents an industrial fault diagnosis system based on the cubic dynamic uncertain causality graph (cubic DUCG) used to model and diagnose industrial systems without sufficient data for model training. The system is developed based on cloud native technology. It contains two main parts, the diagnostic knowledge base and the inference method. The knowledge base was built by domain experts modularly based on professional knowledge. It represented the causality between events in the target industrial system in a visual and graphical form. During the inference, the cubic DUCG algorithm could dynamically generate the cubic causal graph according to the real-time data and perform the logic and probability calculations based on the generated cubic DUCG models, visually displaying the dynamic causal evolution of faults. To verify the system’s feasibility, we rebuild a fault-diagnosis model of the secondary circuit system of No. 1 at the Ningde nuclear power plant based on the new system. Twenty-four fault cases were used to test the diagnostic accuracy of the system, and all faults were correctly diagnosed. The results showed that it was feasible to use the cubic DUCG platform for fault diagnosis.
... In [22], an enhanced knowledge reasoning algorithm based on the picture fuzzy operators was developed to resolve causal inference problems. Dong et al. [34] provided a methodology for modeling and reasoning about complex faults with negative feedback with cubic DUCG. Hao et al. [35] proposed a diagnostic modeling and reasoning system using DUCG for the intelligent diagnosis of jaundice, considering the causal interactions among diseases and symptoms. ...
A dynamic uncertain causality graph (DUCG) is a probabilistic graphical model for knowledge representation and reasoning, which has been widely used in many areas, such as probabilistic safety assessment, medical diagnosis, and fault diagnosis. However, the convention DUCG model fails to model experts’ knowledge precisely because knowledge parameters were crisp numbers or fuzzy numbers. In reality, domain experts tend to use linguistic terms to express their judgements due to professional limitations and information deficiency. To overcome the shortcomings of DUCGs, this article proposes a new type of DUCG model by integrating Pythagorean uncertain linguistic sets (PULSs) and the evaluation based on the distance from average solution (EDAS) method. In particular, experts express knowledge parameters in the form of the PULSs, which can depict the uncertainty and vagueness of expert knowledge. Furthermore, this model gathers the evaluations of experts on knowledge parameters and handles conflicting opinions among them. Moreover, a reasoning algorithm based on the EDAS method is proposed to improve the reliability and intelligence of expert systems. Lastly, an industrial example concerning the root cause analysis of abnormal aluminum electrolysis cell condition is provided to demonstrate the proposed DUCG model.
... Cubic_DB(B2,t3) at t3.Step 3.3. expend E(t3), H2,1E(t3), H2,2E(t3) based on Cubic_DB(B2,t3) and calculate the joint probability of Pr{E(t3)}, Pr{H2,1E(t3)}, Pr{H2,2E(t3)} shown in Equation(29)(30)(31)(32)(33)(34). ...
The working conditions of large-scale industrial systems are very complex. Once a failure occurs, it will affect industrial production, cause property damage, and even endanger the workers' lives. Therefore, it is important to control the operation of the system to accurately grasp the operation status of the system and find out the failure in time. The occurrence of system failure is a gradual process, and the occurrence of the current system failure may depend on the previous state of the system, which is sequential. The fault diagnosis technology based on time series can monitor the operating status of the system in real-time, detect the abnormal operation of the system within the allowable time interval, diagnose the root cause of the fault and predict the status trend. In order to guide the technical personnel to troubleshoot and solve related faults, in this paper, an industrial fault diagnosis system is implemented based on the cubic DUCG theory. The diagnostic model of the system is constructed based on expert knowledge and experience. At the same time, it can perform real-time fault diagnosis based on time sequence, which solves the problem of fault diagnosis of industrial systems without sample data.
... Artificial Intelligence (AI) technologies have been tried for fault alarms (e.g., Yang & Chang 1991), fault diagnosis (e.g., Martin & Nassersharif, 1990;Reifman, 1997;Dong, Zhou, & Zhang, 2018), and operation and maintenance support (e.g., Himeno et al. 1992;Jenkinson, Shaw, & Andow, 1991), in safety-critical systems. They received much attention in the 1990s and have regained attention recently. ...
Human error has contributed considerably to accidents in various working environments. In the field of human error and reliability, environmental factors are termed performance-shaping factors, error-forcing conditions, common performance conditions. This chapter focuses on the cognitive mechanisms related to the occurrence of human errors. This theory foundation can serve as the technical basis for further research, such as error detection, error prediction, error analysis, error correction, etc. Human error classification is fundamental to human error research, investigation, prediction, detection, analysis, and control. For human reliability analysis (HRA) practitioners, its most important function is to quantify human error probabilities in a task or scenario of interest in risk assessments. In other domains in which human errors could be a great source of vulnerability, HRA also has many considerations and adoptions, such as oil and gas, aviation, spaceflight, health care and surgery, railways, cybersecurity, and human-autonomy interaction.
... In [3], the algorithm for breaking the directed cyclic graph (DCG) in DUCG was presented. In [8], a new algorithm called cubic-DUCG was introduced for temporal inference of DUCG. In [9], DUCG is extended as intuitionistic fuzzy (IFDUCG) to handle the problem of describing vagueness and uncertainty of an event. ...
... The recursive algorithm is proposed in [5] and [20] for the inference of cubic-DUCG [8]. A typical cubic-DUCG model is shown in Fig.4, in which the variables in different time layers are treated as different nodes even for a same variable. ...
... (1) (8) In which, AEB(l) indicates the directly linked ancestor evidence/B-type event of the X-type evidence node in layer l. Equation (8) is actually used to calculate the cubic-DUCG presented in [6] and [25]. ...
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%.
... In [3], algorithm for breaking the directed cyclic graph (DCG) in DUCG was presented. In [8], a new algorithm called cubic-DUCG was introduced for temporal inference of DUCG. In [9], some new type variables were introduced to meet the requirement of practice. ...
... Due to the two problems, it is hard to get the value of {} Pr E and {} kj Pr B E directly. The recursive algorithm is proposed for the inference of cubic-DUCG [8] in [17]. A typical cubic-DUCG model is shown in Figure 5, in which the variables in different time layers are treated as different nodes even for a same variable. ...
... Eqn. (8) is actually used to calculate the cubic-DUCG presented in [6] and [25]. Figure 5 is an example. ...
Dynamic Uncertain Causality Graph(DUCG) is a recently proposed model for diagnoses of complex systems. It performs well for industry system such as nuclear power plants, chemical system and spacecrafts. However, the variable state combination explosion in some cases is still a problem that may result in inefficiency or even disability in DUCG inference. In the situation of clinical diagnoses, when a lot of intermediate causes are unknown while the downstream results are known in a DUCG graph, the combination explosion may appear during the inference computation. Monte Carlo sampling is a typical algorithm to solve this problem. However, we are facing the case that the occurrence rate of the case is very small, e.g. $10^{-20}$, which means a huge number of samplings are needed. This paper proposes a new scheme based on conditional stochastic simulation which obtains the final result from the expectation of the conditional probability in sampling loops instead of counting the sampling frequency, and thus overcomes the problem. As a result, the proposed algorithm requires much less time than the DUCG recursive inference algorithm presented earlier. Moreover, a simple analysis of convergence rate based on a designed example is given to show the advantage of the proposed method. % In addition, supports for logic gate, logic cycles, and parallelization, which exist in DUCG, are also addressed in this paper. The new algorithm reduces the time consumption a lot and performs 3 times faster than old one with 2.7% error ratio in a practical graph for Viral Hepatitis B.
... It uses weighted functional event and relationship intensity to express uncertain causal causalities instead of CPTs, and this makes DUCG able to support incomplete expression and construction in the form of a set of subgraphs, and combine them together as a whole, so as to ease the construction. What's more, it can handle logic cycle and real-time faults diagnosis [5]. As a result, DUCG has better performance and user-friendly construction scheme than BNs. ...
... S m , S l is the state assigned to X m and X l , and mark the condition in Eqn. (5) as W X i S n : ...
... According to Eqn. (5), for the state S of X k , we have Eqn. (9) ...
Dynamic Uncertain Causality Graph (DUCG) is an innovative model developed recently on the basis of dynamic causality diagram (DCD) model, which has been proved to be reliable for fault diagnosis of nuclear power plants. DUCG can represent complex uncertain causal relationship graphically, with both high efficient inference and support of incomplete expression. Therefore, DUCG is often built much larger than Bayesian Network (BN). However, as the scale of real problem is so large, DUCG still has the problem of combination explosion. Stochastic Simulation is a common solution for it. However, it is almost impossible to use traditional sampling algorithms for DUCG because the joint probability of evidences could be less than 10−20. In this paper, the algorithm based on conditional stochastic simulation for the inference of DUCG was proposed. It obtains the probability of evidences by calculating the expectation of the conditional probability in sampling process instead of using the sampling frequency, which overcomes the difficulty. What’s more, this algorithm uses recursive reasoning method of DUCG to calculate conditional probability distributions of node for sampling, which means this process only depends on its parent nodes’ states. As a result, the algorithm features in lower time complexity. In addition, it has the potential of parallelization like other sampling algorithms. In conclusion, this algorithm is promising to provide a new solution to the inference of the DUCG in large-scale and complex state situations.
... As a graphical causal probability model [35][36][37][38][39][40], the DUCG can intuitively express causal knowledge in various graphic symbols, present the result in the form of conditional probability and illustrate it graphically. With initial application in the fault diagnosis of complex large scale systems [41][42][43][44][45], the DUCG was later applied to clinical diagnoses, such as jaundice, vertigo, and nasal obstruction, and perfect results were achieved [46,47]. Due to the good performance of DUCG in clinical diagnosis, this paper uses the DUCG to perform outpatient triage. ...
... BXi ( ) represents an integrated cause variable. In a fault diagnosis, BXi is used as a collection of multiple root cause events [48], while in clinical diagnosis, BXi represents the root cause event affected by risk factors [45,46]. During the inference calculation, if BXi exists, it replaces Bi as the hypothesis. ...
In order to reduce the misdiagnosis caused by outpatient triage error and help triage nurses to improve their triage accuracy, a general outpatient triage system based on the hybrid Dynamic Uncertain Causality Graph (hybrid DUCG) is presented. The hybrid DUCG is a combination of the single-valued DUCG (S-DUCG) and the multivalued DUCG (M-DUCG). In this paper, the M-DUCG is utilized to construct the basic outpatient triage knowledge base. In the knowledge base, each department is modeled as one sub-M-DUCG. It describes the causal relations between the department and its associated clinical features. All the sub-M-DUCGs are compiled into one M-DUCG as the basic outpatient triage knowledge base. The S-DUCG is employed to adjust the basic knowledge base to match the variant triage requirements for various hospitals. To validate the triage system, we constructed a basic triage knowledge base and adjusted it to match the triage requirement of a county-level hospital. Verification experiments were performed based on the adjusted triage knowledge base. The result showed that the triage system has high triage accuracy, and it can effectively assist triage nurses in outpatient triage.
... e main focus of the proposed method is solving the three aforementioned problems of the knowledge-based modeling, incompleteness of the medical data, and interpretability of the method. e knowledge representation and inference methods of DUCG are introduced in [28][29][30][31]. In recent years, DUCG has been applied in several medical diagnostic systems involving multiple diseases such as jaundice, syncope, and sellar region disease, among others. ...
... e events in C 4 describe the respective different types of atypical BPPV, in an overlapping manner. Namely, all the basic events in {B 31 ...
... Furthermore, the proposed logical inference rules and logical operations can decrease the complexity and scale of logical event expressions. e actual efficiency tests for the DUCG inference algorithm have been performed using online fault diagnosis data from several large-scale industrial systems (e.g., nuclear power plants), in which thousands of variables and causality arcs are involved [28,31]. e diagnostic reasoning can typically be finished within 0.5-1 s (on a personal computer). ...
The accurate differentiation of the subtypes of benign paroxysmal positional vertigo (BPPV) can significantly improve the efficacy of repositioning maneuver in its treatment and thus reduce unnecessary clinical tests and inappropriate medications. In this study, attempts have been made towards developing approaches of causality modeling and diagnostic reasoning about the uncertainties that can arise from medical information. A dynamic uncertain causality graph-based differential diagnosis model for BPPV including 354 variables and 885 causality arcs is constructed. New algorithms are also proposed for differential diagnosis through logical and probabilistic inference, with an emphasis on solving the problems of intricate and confounding disease factors, incomplete clinical observations, and insufficient sample data. This study further uses vertigo cases to test the performance of the proposed method in clinical practice. The results point to high accuracy, a satisfactory discriminatory ability for BPPV, and favorable robustness regarding incomplete medical information. The underlying pathological mechanisms and causality semantics are verified using compact graphical representation and reasoning process, which enhance the interpretability of the diagnosis conclusions.
