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

The gas path system is an important part of an aero-engine, whose health states can affect the security of the airplane. During the process of aircraft operation, the gas path system will have different working conditions over time, owing to the change of control parameters. However, the different working conditions which change the symmetry of the system will affect parameters of the health state prediction model for the gas path system. The symmetry of the system will also change. Therefore, it is important to consider the influence of variable working conditions when predicting the health states of gas path system. The accuracy of the health state prediction results of the gas path system will be low if the same evaluation standard is used for different working conditions. In addition, the monitoring data of the gas path system’s health state feature quantity is huge while the fault data which can reflect the health states of the gas path system are poor. Thus, it is difficult to establish a health state prediction model only by using the monitoring data of the gas path system. In order to avoid problems, this paper proposes a health state prediction model considering multiple working conditions based on time domain analysis and a belief rule base. First, working condition is divided by using time domain characteristics. Then, a belief rule base (BRB) theory-based health state prediction model is built, which can fuse expert knowledge and fault monitoring data to improve modeling accuracy. The reference value of the feature is given by the fuzzy C-means algorithm in a model. To decrease the uncertainty of expert knowledge, the covariance matrix adaptive evolution strategy (CMA-ES) is used as the optimization algorithm. Finally, a NASA public dataset without labels is used to verify the proposed health state model. The results show that the proposed health prediction model of a gas path system can accurately realize health state prediction under multiple working conditions.

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Reliability Assessment Model for Industrial Control System Based on Belief Rule Base

International Journal of Computers Communications & Control ◽

10.15837/ijccc.2019.3.3548 ◽

2019 ◽

Vol 14 (3) ◽

pp. 419-436 ◽

Cited By ~ 3

Author(s):

Yuhe Wang ◽

Peili Qiao ◽

Zhiyong Luo ◽

Guanglu Sun ◽

Guangze Wang

Keyword(s):

Control System ◽

Reliability Assessment ◽

Quantitative Information ◽

Assessment Method ◽

Assessment Model ◽

Rule Base ◽

Industrial Control System ◽

Industrial Control ◽

Proposed Model ◽

Belief Rule Base

This paper establishes a novel reliability assessment method for industrial control system (ICS). Firstly, the qualitative and quantitative information were integrated by evidential reasoning(ER) rule. Then, an ICS reliability assessment model was constructed based on belief rule base (BRB). In this way, both expert experience and historical data were fully utilized in the assessment. The model consists of two parts, a fault assessment model and a security assessment model. In addition, the initial parameters were optimized by covariance matrix adaptation evolution strategy (CMA-ES) algorithm, making the proposed model in line with the actual situation. Finally, the proposed model was compared with two other popular prediction methods through case study. The results show that the proposed method is reliable, efficient and accurate, laying a solid basis for reliability assessment of complex ICSs.

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A New Prediction Model Based on Belief Rule Base for System's Behavior Prediction

IEEE Transactions on Fuzzy Systems ◽

10.1109/tfuzz.2011.2130527 ◽

2011 ◽

Vol 19 (4) ◽

pp. 636-651 ◽

Cited By ~ 36

Author(s):

Xiao-Sheng Si ◽

Chang-Hua Hu ◽

Jian-Bo Yang ◽

Zhi-Jie Zhou

Keyword(s):

Prediction Model ◽

Rule Base ◽

Behavior Prediction ◽

Model Based ◽

Belief Rule Base

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

Science China Information Sciences ◽

10.1007/s11432-018-9620-7 ◽

2019 ◽

Vol 62 (10) ◽

Cited By ~ 2

Author(s):

Zhijie Zhou ◽

Zhichao Feng ◽

Changhua Hu ◽

Xiaoxia Han ◽

Zhiguo Zhou ◽

...

Keyword(s):

Prediction Model ◽

Fault Prediction ◽

Rule Base ◽

Model Based ◽

Belief Rule Base ◽

Power Set ◽

Hidden Fault

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A novel structural safety assessment method of large liquid tank based on the belief rule base and finite element method

Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability ◽

10.1177/1748006x211021690 ◽

2021 ◽

pp. 1748006X2110216

Author(s):

Yuan Chen ◽

Zhijie Zhou ◽

Lihao Yang ◽

Guanyu Hu ◽

Xiaoxia Han ◽

...

Keyword(s):

Finite Element Method ◽

Finite Element ◽

Safety Assessment ◽

Expert Knowledge ◽

Assessment Method ◽

Assessment Model ◽

Structural Safety ◽

Rule Base ◽

Belief Rule Base ◽

Element Method

The structural safety assessment of large liquid tanks (LLT) has attracted an extensive attention. As a typical gray box model, the belief rule base (BRB) model can handle qualitative information and quantitative data simultaneously, which is a suitable modeling tool for structural safety assessment. However, it is difficult to establish and train the BRB model when there is a lack of expert experience and fault samples of LLT. Therefore, a novel safety assessment model for LLT based on BRB and finite element method (FEM-BRB) is proposed in this paper. The FEM is introduced to construct the BRB model by combining expert knowledge and industry standards for the first time, which can effectively compensate for the lack of expert experience. The fault samples are generated in the mechanism simulation model under different working conditions. Based on the fault samples generated by the FEM and historical samples, the projection covariance matrix adaption evolution strategy (P-CMA-ES) optimization algorithm is then used to train the model, which further improves the structural safety assessment accuracy when lacking fault samples. A case study of three actual oil tanks in a coastal port is conducted to illustrate the effectiveness and advantage of the developed structural safety assessment method.

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A new risk assessment method based on belief rule base and fault tree analysis

Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability ◽

10.1177/1748006x211011457 ◽

2021 ◽

pp. 1748006X2110114

Author(s):

Hai-Long Zhu ◽

Shan-Shan Liu ◽

Yuan-Yuan Qu ◽

Xiao-Xia Han ◽

Wei He ◽

...

Keyword(s):

Risk Assessment ◽

Expert Knowledge ◽

Fault Tree ◽

Fault Tree Analysis ◽

Evaluation Process ◽

Assessment Method ◽

Assessment Model ◽

Rule Base ◽

Tree Analysis ◽

Belief Rule Base

Risk assessment methods are often used in complex industrial systems to avoid risks and reduce losses. The existing methods have not effectively solved the problems of lack of evaluation data and the interpretability of the entire evaluation process. This paper proposes a new risk assessment model based on the belief rule base (BRB) and Fault Tree Analysis (FTA). The FTA algorithm overcomes the difficulties of traditional BRB model in obtaining expert knowledge, clear indicators, and establishing logical relationships. This method establishes FTA rules based on the BRB model and expands the knowledge base through the FTA algorithm. A Bayesian network is applied as a conversion bridge between the FTA and BRB model. In addition, the model is optimized to reduce the uncertainty in the model. The method proposed is described by a case and its effectiveness is verified.

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