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 Dynamic Importance Analysis of Components with Known Failure Contribution of Complex Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Yangfan Li ◽  
Yingjie Zhang ◽  
Bochao Dai ◽  
Lin Zhang
Keyword(s):  
Small Sample ◽  
System Component ◽  
Importance Measure ◽  
Probability Curve ◽  
Performance Loss ◽  
Failure Data ◽  
Time Integral ◽  
Multistate System ◽  
Individual System ◽  
Importance Analysis

Importance analysis deals with the investigation of influence of individual system component on system operation. This paper mainly focuses on dynamic important analysis of components in a multistate system. Assuming that failure probabilities of system components are independent, a new time integral-based importance measure approach (TIIM) is proposed to measure the loss of system performance that is caused by each individual component. Reversely the importance of a component can be evaluated according to the magnitude of performance loss of the system caused by it. Moreover, the dynamic varying curve of importance of a component with time can be described by calculating criticality of the component at each state. On the other hand, in the proposed approach, the importance probability curve of a component is fitted by using the failure data from all components of system excluding that of the component itself so as to solve the problem of inaccurate fitting caused by small sample data. The approach has been verified by probability analysis of failure data of CNC machines.

Component Reliability Importance Measure in Presence of Transient Faults

2011 ◽  
Vol 58-60 ◽  
pp. 529-534 ◽  
Author(s):  
Xin Qi ◽  
De C. Zuo ◽  
Zhan Zhang ◽  
Xiao Zong Yang
Keyword(s):  
Field Data ◽  
Importance Measure ◽  
Transient Faults ◽  
Composite Measure ◽  
Component Reliability ◽  
Component Importance ◽  
Importance Analysis ◽  
System Improvement ◽  
State Changes ◽  
Consistent Performance

Importance measures are widely used to characterize the contribution of components to the system performance such as reliability, availability, risk, etc, and thus give great help in identifying system weaknesses and prioritizing system improvement activities. Although much work has been carried out on component importance analysis, most studies only concern the consistent states of components within which components exhibit consistent performance until state changes happen. Unfortunately, field data shows that many transient faults in components may result in severe consequences without causing any state changes, and, this can lead to a misunderstanding of component importance. This paper focuses on the reliability importance analysis in presence of transient faults, and proposes a composite measure for evaluation. A sample series parallel system is analyzed to illustrate the use of this measure.

Components Importance Analysis Fault Tree of Power Transformer of Gi Simangkuk Switchyard in Indonesia

2013 ◽  
Vol 694-697 ◽  
pp. 907-910 ◽  
Author(s):  
Josep Franklin Sihite ◽  
Takehisa Kohda
Keyword(s):  
System Reliability ◽  
Power Transformer ◽  
Fault Tree ◽  
Importance Measure ◽  
Component Importance ◽  
Safety Improvement ◽  
System Components ◽  
Importance Analysis

The purpose of this paper is to study the importance measures of power transformer system components. Importance measures analysis is a key part of the system reliability quantification process which are most effective towards safety improvement. This paper presented an application and results of the importance measures analysis of a power transformer system of GI Simangkuk switchyard in Indonesia by using Birnbaum importace measures, critically importance measure, and Fussel-Vessely importance measures. These method present the rank of the component importance measures quantitavily according to their contribution to system reliability and safety.

scholarly journals Importance Measures for NFV Data Center: An Availability Evaluation

2018 ◽  
Author(s):  
Rafael Souza ◽  
Marcelo Santos ◽  
Stênio Fernandes
Keyword(s):  
Data Center ◽  
Data Centers ◽  
Network Function Virtualization ◽  
Importance Measure ◽  
Substantial Impact ◽  
Network Function ◽  
Component Importance ◽  
Importance Analysis ◽  
Reliability Block Diagrams ◽  
The Impact

Data center infrastructures need to provide high availability of their services. Unexpected spikes of downtime in data centers lead to financial losses. Besides, there are intangible costs such as damaged reputation, low employee satisfaction, and reduced customer retention. In this context, Network Function Virtualization (NFV) emerged as a paradigm that assists data centers in becoming more dynamic and flexible. This paper presents an availability evaluation and importance analysis under the redundancy of NFV in data centers. The NFV data center component importance is represented by using Reliability Block Diagrams (RBD). The proposed models have identified the availability importance and critical devices in an NFV data center. This research also suggests alternatives for device redundancy to reach higher availability and proposes a new importance measure for analyzing the impact of NFV factors on data center availability. The measure can evaluate the degree of the impact of a failure on the data center, therefore, help to identify the factors with a substantial impact on data center availability.

The Method of Failure Data Processing Analysis of Filling System in Launching Site Based on Bayesian Method

2014 ◽  
Vol 644-650 ◽  
pp. 2537-2541
Author(s):  
Ji Guang Zhao ◽  
Yang Zhang ◽  
Bao Cui ◽  
Jia Jie Hao
Keyword(s):  
Data Processing ◽  
Bayesian Method ◽  
High Reliability ◽  
Small Sample ◽  
Quantitative Risk Assessment ◽  
Data Types ◽  
Failure Data ◽  
Filling System ◽  
Sample Data ◽  
Launching Site

The high reliability and the short times carried of quantitative risk assessment of Filling system in launching site, the failure data is small sample data. In this paper, we will focus on these small sample data and take processing analysis. A method based on Bayesian method of failure data processing analysis of Filling system has been proposed, which firstly analyzes the characteristic of failure data of Filling system. Secondly, we classify the failure data into running failure data and demanding data according to the data types, and further analyze various uncertainty distributions under this two kinds of data type. What’s more, we use Bayesian method to solve the problems of various failure data. Lastly, we combine the real circumstance of Filling system and take the Filling system as example, and choose the filling pump, shut-off valve and pneumatic ball valve these three different failure types to use Bayesian method to analyze, and obtain various prior distributions and posterior distributions of different failure equipment, and give the simulation results.

scholarly journals A Performance Prediction Method Based on Sliding Window Grey Neural Network for Inertial Platform

2021 ◽  
Vol 13 (23) ◽  
pp. 4864
Author(s):  
Langfu Cui ◽  
Qingzhen Zhang ◽  
Liman Yang ◽  
Chenggang Bai
Keyword(s):  
Neural Network ◽  
Prediction Models ◽  
Prediction Method ◽  
Sliding Window ◽  
Small Sample ◽  
Grey Theory ◽  
Sensing System ◽  
Performance Change ◽  
Sample Data ◽  
Platform System

An inertial platform is the key component of a remote sensing system. During service, the performance of the inertial platform appears in degradation and accuracy reduction. For better maintenance, the inertial platform system is checked and maintained regularly. The performance change of an inertial platform can be evaluated by detection data. Due to limitations of detection conditions, inertial platform detection data belongs to small sample data. In this paper, in order to predict the performance of an inertial platform, a prediction model for an inertial platform is designed combining a sliding window, grey theory and neural network (SGMNN). The experiments results show that the SGMNN model performs best in predicting the inertial platform drift rate compared with other prediction models.

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