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A new interpretable fault diagnosis method based on belief rule base and probability table

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Chinese Journal of Aeronautics 2023年第3期36卷 184-201页

作者： Zhichao MING Zhijie ZHOU You CAO Shuaiwen TANG Yuan CHEN Xiaoxia HAN Wei HE High-Tech Institute of Xi’an Xi’an 710025China School of Computer Science and Information Engineering Harbin Normal UniversityHarbin 150025China

It is vital to establish an interpretable fault diagnosis model for critical equipment.belief rule base(BRB)is an interpretable expert system gradually applied in fault diagnosis.However,the expert knowledge cannot be utilized to establish the initial BRB accurately if there are multiple referential grades in different fault features.In addition,the interpretability of BRB-based fault diagnosis is destroyed in the optimization process,which reflects in two aspects:deviation from the initial expert judgment and over-optimization of parameters.To solve these problems,a new interpretable fault diagnosis model based on BRB and probability table,called the BRB-P,is proposed in this paper.Compared with the traditional BRB,the BRB-P constructed by the probability table is more accurate.Then,the interpretability constraints,i.e.,the credibility of expert knowledge,the penalty factor and the rule-activation factor,are inserted into the projection covariance matrix adaption evolution strategy to maintain the interpretability of BRB-P.A case study of the aerospace relay is conducted to verify the effectiveness of the proposed method.

关键词： Aerospace relay belief rule base Expert knowledge Fault diagnosis Interpretability constraints

A novel combination belief rule base model for mechanical equipment fault diagnosis

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Chinese Journal of Aeronautics 2022年第5期35卷 158-178页

作者： Manlin CHEN Zhijie ZHOU Bangcheng ZHANG Guanyu HU You CAO High-Tech Institute of Xi’an Xi’an 710025China School of Applied Technology Changchun University of TechnologyChangchun 130000China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin 541004China

Due to the excellent performance in complex systems modeling under small samples and uncertainty,belief rule base(BRB)expert system has been widely applied in fault diagnosis.However,the fault diagnosis process for complex mechanical equipment normally needs multiple attributes,which can lead to the rule number explosion problem in BRB,and limit the efficiency and accuracy.To solve this problem,a novel Combination belief rule base(C-BRB)model based on Directed Acyclic Graph(DAG)structure is proposed in this paper.By dispersing numerous attributes into the parallel structure composed of different sub-BRBs,C-BRB can effectively reduce the amount of calculation with acceptable result.At the same time,a path selection strategy considering the accuracy of child nodes is designed in C-BRB to obtain the most suitable submodels.Finally,a fusion method based on Evidential Reasoning(ER)rule is used to combine the belief rules of C-BRB and generate the final results.To illustrate the effectiveness and reliability of the proposed method,a case study of fault diagnosis of rolling bearing is conducted,and the result is compared with other methods.

关键词： Fault diagnosis belief rule base Directed acyclic graph Evidential reasoning Mechanical equipment

Sensor Fault Diagnosis and Tolerant Control based on belief rule base for Complex System

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Journal of Systems Science & Complexity 2023年第3期36卷 1002-1023页

作者： FENG Zhichao ZHOU Zhijie BAN Xiaojun HU Changhua ZHANG Xiaobo Rocket Force University of Engineering Xi'an 710025China Harbin Institute of Technology Harbin 150001China

This paper develops a new fault diagnosis and tolerant control framework of sensor failure(SFDTC)for complex system such as rockets and missiles.The new framework aims to solve two problems:The lack of data and the multiple uncertainty of knowledge.In the SFDTC framework,two parts exist:The fault diagnosis model and the output reconstruction model.These two parts of the new framework are constructed based on the new developed belief rule base with power set(BRB-PS).The multiple uncertainty of knowledge can be addressed by the local ignorance and global ignorance in the new developed BRB-PS model.Then,the stability of the developed framework is proved by the output error of the BRB-PS model.For complex system,the sensor state is determined by many factors and experts cannot provide accurate knowledge.The multiple uncertain knowledge will reduce the performance of the initial SDFTC framework.Therefore,in the SFDTC framework,to handle the influence of the uncertainty of expert knowledge and improve the framework performance,a new optimization model with two optimization goals is developed to ensure the smallest output uncertainty and the highest accuracy simultaneously.A case study is conducted to illustrate the effectiveness of the developed framework.

关键词： belief rule base fault diagnosis and tolerant control optimization model uncertainty

A New Prediction System based on Self-Growth belief rule base with Interpretability Constraints

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Computers, Materials & Continua 2023年第5期75卷 3761-3780页

作者： Yingmei Li Peng Han Wei He Guangling Zhang Hongwei Wei Boying Zhao School of Computer Science and Information Engineering Harbin Normal UniversityHarbin150025China

Prediction systems are an important aspect of intelligent decisions.In engineering practice,the complex system structure and the external environment cause many uncertain factors in the model,which influence the modeling accuracy of the model.The belief rule base(BRB)can implement nonlinear modeling and express a variety of uncertain information,including fuzziness,ignorance,randomness,etc.However,the BRB system also has two main problems:Firstly,modeling methods based on expert knowledge make it difficult to guarantee the model’s accuracy.Secondly,interpretability is not considered in the optimization process of current research,resulting in the destruction of the interpretability of BRB.To balance the accuracy and interpretability of the model,a self-growth belief rule basewith interpretability constraints(SBRB-I)is proposed.The reasoning process of the SBRB-I model is based on the evidence reasoning(ER)approach.Moreover,the self-growth learning strategy ensures effective cooperation between the datadriven model and the expert system.A case study showed that the accuracy and interpretability of the model could be guaranteed.The SBRB-I model has good application prospects in prediction systems.

关键词： belief rule base evidence reasoning interpretability optimization prediction system

Milling Fault Detection Method based on Fault Tree Analysis and Hierarchical belief rule base

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Computer Systems Science & Engineering 2023年第9期46卷 2821-2844页

作者： Xiaoyu Cheng Mingxian Long Wei He Hailong Zhu School of Computer Science and Information Engineering Harbin Normal UniversityHarbin 150025China High-Tech Institute of Xi’an Xi’anShanxi710025China

Expert knowledge is the key to modeling milling fault detection systems based on the belief rule base.The construction of an initial expert knowledge base seriously affects the accuracy and interpretability of the milling fault detection model.However,due to the complexity of the milling system structure and the uncertainty of the milling failure index,it is often impossible to construct model expert knowledge effectively.Therefore,a milling system fault detection method based on fault tree analysis and hierarchical BRB(FTBRB)is proposed.Firstly,the proposed method uses a fault tree and hierarchical BRB modeling.Through fault tree analysis(FTA),the logical correspondence between FTA and BRB is sorted out.This can effectively embed the FTA mechanism into the BRB expert knowledge base.The hierarchical BRB model is used to solve the problem of excessive indexes and avoid combinatorial explosion.Secondly,evidence reasoning(ER)is used to ensure the transparency of the model reasoning process.Thirdly,the projection covariance matrix adaptation evolutionary strategies(P-CMA-ES)is used to optimize the model.Finally,this paper verifies the validity model and the method’s feasibility techniques for milling data sets.

关键词： Fault detection milling system belief rule base fault tree analysis evidence reasoning

A hidden fault prediction model based on the belief rule base with power set and considering attribute reliability

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Science China(Information Sciences) 2019年第10期62卷 86-101页

作者： Zhijie ZHOU Zhichao FENG Changhua HU Xiaoxia HAN Zhiguo ZHOU Gailing LI High-Tech Institute of Xi'an Department of Radiation Oncology The University of Texas Southwestern Medical Center

Hidden faults are important characteristics of complex systems that cannot be observed directly.The hidden behavior of a system, such as the health state and safety level, has a direct correlation with these hidden faults. After the predicted hidden behavior reaches a fault boundary, certain measures must be taken to avoid fault occurrences. Thus, hidden faults can be predicted by the hidden behavior of a system.The belief rule base(BRB) has been used to predict hidden behaviors. However, two problems remain to be solved in engineering practice. First, when the observed information is absent, ignorance may exist in the output. If only global ignorance is considered, it may be unreasonable in certain cases, which can influence the prediction model. Second, the effects of disturbance factors such as noise and sensor quality may cause the reliability of the gathered information to decline, which indirectly leads to unreliability of the hidden behavior. Thus, to address the global ignorance and unreliable hidden behavior, a new hidden BRB model with a power set and considering attribute reliability(PHBRB-r) is proposed for hidden fault prediction. In the PHBRB-r model, the effects of disturbance factors on hidden behavior are considered using attribute reliability, and the discernment frame is a power set. A case study of hidden fault prediction is conducted to demonstrate the effectiveness of the PHBRB-r model.

关键词： fault diagnosis fault prediction belief rule base hidden behavior attribute reliability power set

Real-time fault detection method based on belief rule base for aircraft navigation system

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Chinese Journal of Aeronautics 2013年第3期26卷 717-729页

作者： Zhao Xin Wang Shicheng Zhang Jinsheng Fan Zhiliang Min Haibo High-Tech Institute of Xi'an

Real-time and accurate fault detection is essential to enhance the aircraft navigation system’s reliability and safety. The existent detection methods based on analytical model draws back at simultaneously detecting gradual and sudden faults. On account of this reason, we propose an online detection solution based on non-analytical model. In this article, the navigation system fault detection model is established based on belief rule base （BRB）, where the system measuring residual and its changing rate are used as the inputs of BRB model and the fault detection function as the output. To overcome the drawbacks of current parameter optimization algorithms for BRB and achieve online update, a parameter recursive estimation algorithm is presented for online BRB detection model based on expectation maximization （EM） algorithm. Furthermore, the proposed method is verified by navigation experiment. Experimental results show that the proposed method is able to effectively realize online parameter evaluation in navigation system fault detection model. The output of the detection model can track the fault state very well, and the faults can be diagnosed in real time and accurately. In addition, the detection ability, especially in the probability of false detection, is superior to offline optimization method, and thus the system reliability has great improvement.

关键词： belief rule base Fault detection Fault tolerant control Integrated navigation Parameter recursive estimation algorithm

New health-state assessment model based on belief rule base with interpretability

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Science China(Information Sciences) 2021年第7期64卷 209-223页

作者： Zhijie ZHOU You CAO Guanyu HU Youmin ZHANG Shuaiwen TANG Yuan CHEN High-Tech Institute of Xi'an Department of Information and Control Engineering Xi'an University of Technology School of Computer Science and Information Security Guilin University of Electronic Technology Department of Mechanical Industrial and Aerospace Engineering Concordia University

Health-state assessment is the foundation of optimal-maintenance decision-making for complex systems to maintain reliability and safety. Generating the assessment results in a convincing and interpretable way to avoid potential risks is of great importance. belief rule base(BRB) as an interpretable model performs well in health-state assessment. However, the interpretability of a BRB-based model may be lost during the optimization process, which is expressed mainly as three problems: expert knowledge is not effectively used in the optimization process; the optimized rules of BRB may be in conflict with real systems; and some parameters get over-optimized, which may affect experts’ initial judgment. Three concepts — "searching intensity", "interpretability constraint of belief distribution", and "rule-activation factor" — are defined to address these problems. Using these concepts, we propose a new health-state assessment model based on the interpretable BRB and a new optimization method to improve the accuracy and preserve the interpretability of the new model. To demonstrate the effectiveness of the proposed model, we conducted an aero-engine case study.

关键词： health-state assessment belief rule base interpretability complex systems

Safety Assessment of Liquid Launch Vehicle Structures based on Interpretable belief rule base

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Computer Systems Science & Engineering 2023年第10期47卷 273-298页

作者： Gang Xiang Xiaoyu Cheng Wei He Peng Han Beihang University School of Automation Science and Electrical Engineering Beijing100083China Beijing Aerospace Automatic Control Institute Beijing100070China School of Computer Science and Information Engineering Harbin Normal UniversityHarbin150025China Rocket Force University of Engineering Xi’an710025China

A liquid launch vehicle is an important carrier in aviation,and its regular operation is essential to maintain space security.In the safety assessment of fluid launch vehicle body structure,it is necessary to ensure that the assessmentmodel can learn self-response rules from various uncertain data and not differently to provide a traceable and interpretable assessment process.Therefore,a belief rule base with interpretability(BRB-i)assessment method of liquid launch vehicle structure safety status combines data and knowledge.Moreover,an innovative whale optimization algorithm with interpretable constraints is proposed.The experiments are carried out based on the liquid launch vehicle safety experiment platform,and the information on the safety status of the liquid launch vehicle is obtained by monitoring the detection indicators under the simulation platform.The MSEs of the proposed model are 3.8000e-03,1.3000e-03,2.1000e-03,and 1.8936e-04 for 25%,45%,65%,and 84%of the training samples,respectively.It can be seen that the proposed model also shows a better ability to handle small sample data.Meanwhile,the belief distribution of the BRB-i model output has a high fitting trend with the belief distribution of the expert knowledge settings,which indicates the interpretability of the BRB-i model.Experimental results show that,compared with other methods,the BRB-i model guarantees the model’s interpretability and the high precision of experimental results.

关键词： Liquid launch vehicle belief rule base with interpretability belief rule base whale optimization algorithm vibration frequency swaying angle