Integrated Importance Measure of Component States Based on Loss of System Performance

2012 ◽  
Vol 61 (1) ◽  
pp. 192-202 ◽  
Author(s):  
Shubin Si ◽  
Hongyan Dui ◽  
Xibin Zhao ◽  
Shenggui Zhang ◽  
Shudong Sun
Keyword(s):  
System Performance ◽  
Importance Measure

Dynamic Importance Analysis of Components of Complex Mechanical System by Small Sample Data

2018 ◽  
Author(s):  
Li Yangfan ◽  
Zhang Yingjie ◽  
Dai Bochao ◽  
Zhang Lin
Keyword(s):  
Mechanical System ◽  
System Performance ◽  
Small Sample ◽  
System Component ◽  
Importance Measure ◽  
Complex Mechanical System ◽  
Failure Data ◽  
Component Importance ◽  
Sample Data ◽  
Importance Analysis

Importance analysis deals with the influence of individual system component on system operation. Thus, a lot of failure data should be collected to make the analysis more accurate. This paper mainly focuses on the numerical estimation of component importance in complex mechanical system which is considered as a multi-state system with few failure data. In order to evaluate components’ failure probability distribution by small sample data, a time integral importance measure (TIIM) approach is proposed. In this measure, we aim to measure component importance using the change of system performance caused by wiping off component failure data. On this basis, the dynamic importance fluctuation of a component can be measured by calculating criticality of each state of the component. The approach has been verified by probability analysis of CNC machine tools. The main contribution of this work is the proposed dynamic importance measure which can be used to identify the key state of a component that influences system performance most by small-sample data.

scholarly journals Importance measure-based maintenance strategy considering maintenance costs

2021 ◽  
Vol 24 (1) ◽  
pp. 15-24
Author(s):  
Chao Zhang ◽  
Yadong Zhang ◽  
Hongyan Dui ◽  
Shaoping Wang ◽  
Mileta M. Tomovic
Keyword(s):  
Preventive Maintenance ◽  
System Performance ◽  
Maintenance Cost ◽  
Importance Measure ◽  
Repairable Systems ◽  
Consistent System ◽  
System Components ◽  
System Maintenance ◽  
The Difference ◽  
Selection Of

Maintenance is an important way to ensure the best performance of repairable systems. This paper considers how to reduce system maintenance cost while ensuring consistent system performance. Due to budget constraints, preventive maintenance (PM) can be done on only some of the system components. Also, different selections of components to be maintained can have markedly different effects on system performance. On the basis of the above issues, this paper proposes an importance-based maintenance priority (IBMP) model to guide the selection of PM components. Then the model is extended to find the degree of correlation between two components to be maintained and a joint importance-based maintenance priority (JIBMP) model to guide the selection of opportunistic maintenance (OM) components is proposed. Also, optimization strategies under various conditions are proposed. Finally, a case of 2H2E architecture is used to demonstrate the proposed method. The results show that generators in the 2E layout have the highest maintenance priority, which further explains the difference in the importance of each component in PM.

Reliability analysis for electrical power systems based on importance measures

2019 ◽  
pp. 1748006X1989487
Author(s):  
Fangyu Liu ◽  
Hongyan Dui ◽  
Ziyue Li
Keyword(s):  
Power Systems ◽  
Power System ◽  
Reliability Analysis ◽  
Preventive Maintenance ◽  
System Performance ◽  
Electrical Power ◽  
Importance Measure ◽  
Reliability Engineering ◽  
Electrical Power System ◽  
Electrical Power Systems

With the introduction of reliability engineering, electrical power system reliability has become an important basis for decision-making in the power industry. Two operation cases of electrical power systems are considered in this article. When the system is in an ordinary way, the influence between two system components will affect the importance measure of one component. When some component is in maintenance, preventive maintenance for working components and corrective maintenance for failed components can be executed simultaneously to enhance electrical power system performance. In view of the above two cases, two importance measures are proposed to effectively guide the preventive maintenance, aiming to improve the system performance within a limited budget. Reliability analysis procedure and methods applied toward the two importance measures are then developed and illustrated with the analysis on a Dual Element Spot Network system with double power supplies and double loads. Finally, a strategy for preventive maintenance is proposed by ranking the importance of these components.

The relationship of a retinal-gain index to system performance.

1960 ◽  
Author(s):  
S. Seidenstein ◽  
R. Chernikoff ◽  
F. V. Taylor
Keyword(s):  
System Performance ◽  
Relationship Of ◽  
The Relationship

The use of event-related potentials in the enhancement of system performance

1976 ◽  
Author(s):  
Christopher Wickens ◽  
Jack Isreal ◽  
Gregory McCarthy ◽  
Daniel Gopher ◽  
Emanuel Donchin
Keyword(s):  
System Performance ◽  
Event Related Potentials ◽  
Related Potentials

Study of 1024-QAM system performance in the presence of filtering imperfections

1989 ◽  
Vol 136 (2) ◽  
pp. 175-179 ◽  
Author(s):  
P. Mathiopoulos ◽  
H. Ohnishi ◽  
K. Feher
Keyword(s):  
System Performance

Effects of Observer Variation on Performance in Probabilistic Diagnosis of Jaundice

1981 ◽  
Vol 20 (03) ◽  
pp. 163-168 ◽  
Author(s):  
G. Llndberg
Keyword(s):  
Physical Examination ◽  
System Performance ◽  
Transition Matrix ◽  
Bayes Theorem ◽  
Observer Variation ◽  
Kappa Statistics ◽  
Diagnostic Categories ◽  
Number Of Patients ◽  
Probability Transition Matrix ◽  
Probabilistic Diagnosis

A system for probabilistic diagnosis of jaundice has been used for studying the effects of taking into account the unreliability of diagnostic data caused by observer variation. Fourteen features from history and physical examination were studied. Bayes’ theorem was used for calculating the probabilities of a patient’s belonging to each of four diagnostic categories.The construction sample consisted of 61 patients. An equal number of patients were tested in the evaluation sample. Observer variation on the fourteen features had been assessed in two previous studies. The use of kappa-statistics for measuring observer variation allowed the construction of a probability transition matrix for each feature. Diagnostic probabilities could then be calculated with and without the inclusion of weights for observer variation. Tests of system performance revealed that discriminatory power remained unchanged. However, the predictions rendered by the variation-weighted system were diffident. It is concluded that taking observer variation into account may weaken the sharpness of probabilistic diagnosis but it may also help to explain the value of probabilistic diagnosis in future applications.

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