Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (3): 601-628.doi: 10.13229/j.cnki.jdxbgxb20221370

   

Speculative views on health assessment of complex systems

Quan QUAN1(),Gen CUI1,Zhi-yao ZHAO2,Xun-hua DAI3,Chang WEN4,Kai-yuan CAI1   

  1. 1.School of Automation Science and Electrical Engineering，Beihang University，Beijing 100191，China
    2.School of Artificial Intelligence，Beijing Technology and Business University，Beijing 100048，China
    3.School of Computer Science and Engineering，Central South University，Changsha 410083，China
    4.Beihang University Hospital，Beijing 100191，China
  • Received:2022-10-26 Online:2023-03-01 Published:2023-03-29

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Abstract:

With the increasing system complexity in various engineering applications， greater demands are being placed on the reliability and safety of these systems. A kind of more comprehensive requirement called “health” has been naturally put up beyond reliability and safety. Consequently， Prognostics and Health Management （PHM） for complex systems has naturally become a hotspot in systems engineering. Moreover， it has been applied in many areas， such as aerospace， machinery， and power electronics. This paper summarizes and proposes a framework for the health assessment of complex systems， which consists of four aspects： data acquisition， data processing， health assessment， and health prediction. At last， the prospect view of health assessment is presented， and a system-leveled open-source project， namely OpenHA （Open Health Assessment） is suggested.

Key words: system engineering, complex system, health assessment, health prediction, OpenHA

CLC Number: 

  • TP277

Fig.1

Line plot of the number of PHM-related iteratures in each year"

Fig.2

Clustering analysis results from CiteSpace"

Fig.3

Relationship between faults and symptoms"

Fig.4

Condition/performance degradation or deterioration curves over time"

Fig.5

Flow chart of health assessment"

Fig.6

General architecture for health assessment"

Fig.7

General procedure of data processing"

Fig.8

Denoising methods by signal decomposition"

Table 1

Common statistic characteristics ofdigital signal"

序号特征名称计算公式
1均值（Mean）μ=1n∑i=1nxi
2整理平均值（Average rectified value）XARV=1n∑n=1nxi
3方差 （Variance）σ2=1n-1∑i=1nxi-μ2
4方根幅值Xr=1n∑i=1nxi2
5峰峰值（Peak to peak）XPP=maxxi-minxi
6峰值（Peak）XP=maxxi
7均方根（Root mean square，RMS）XRMS=1n∑i=1nxi2
8峰度（Kurtosis）XK=1σ4∑i=1nxi-μ4n

Fig.9

Schematic diagram of principal component analysis"

Fig.10

Schematic diagram of convolutionalneural network"

Fig.11

Schematic diagram of membership function"

Fig.12

Healthy and unhealthy sets under fuzzy logic"

Fig.13

Schematic diagram of healthy or unhealthy sets"

Fig.14

Step-by-step attribute assessment flow chart"

Table 2

Table for determining the weight condition"

体重情况BMI
体重过低<18.5
体重正常18.5~23.9
超重24~27.9
肥胖≥28

Fig.15

Schematic diagram of assessment method based on output bias"

Fig.16

Schematic diagram of assessment methodbased on state deviations"

Fig.17

Schematic diagram of clustering"

Fig.18

Total health of complex systems depends on all subsystems， sensors and components， etc."

Fig.19

Schematic diagram of different structures and configurations of different multicopters"

Fig.20

Common reliability block diagrams"

Fig.21

Schematic diagram of fault tree"

Fig.22

Flow chart and a simple example of phase-cyclic system"

Fig.23

Schematic diagram of state prediction"

Fig.24

Schematic diagram of model fitting and prediction"

Fig.25

Monte Carlo methodology"

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