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.

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.

Reliability Analysis of Water Mist Systems

2007 ◽  
Author(s):  
Shuzhen Xu ◽  
Enrique A. Susemihl
Keyword(s):  
Failure Modes ◽  
National Fire Protection Association ◽  
Water Mist ◽  
Confidence Bounds ◽  
Failure Data ◽  
Importance Analysis ◽  
History Of ◽  
Operational Failures ◽  
System Configurations ◽  
Failure Probabilities

This paper presents some preliminary results from a reliability study of water mist systems conducted at FM Global. The study includes a detailed Failure Modes and Effects Analysis (FMEA) to identify all the major potential failure modes, which include demand, quiescent and operational failures. Various fault trees are thus constructed for the typical water mist system configurations to evaluate the failure probabilities. However, due to the short history of industrial application of water mist systems, no specific reliability data are available. Therefore, in the calculation of system failure probability, the component failure data are obtained from other applications. The failure probabilities and the confidence bounds of the typical water mist systems listed in the Standard 750 of the National Fire Protection Association are compared in the paper. The major failure modes identified through an importance analysis are also presented.

Research on Mechanical Reliability Growth Model for Complete Failure Data

2010 ◽  
Vol 118-120 ◽  
pp. 536-540 ◽  
Author(s):  
Zhi Li Sun ◽  
Yu Guo ◽  
Shi Ji
Keyword(s):  
Growth Model ◽  
Small Sample ◽  
Reliability Growth ◽  
Mechanical Reliability ◽  
Failure Data ◽  
Mechanical Products ◽  
Duane Model ◽  
Complete Failure ◽  
Growth Technology ◽  
Mechanical Characters

As everyone knows, reliability growth technology is an essential part in the mechanical reliability theory as well as an insurance of the products capability in usage. It exists throughout the entire lifespan of development, manufacturing and application. Concerning the reliability characters of mechanical products, that product life obeys Weibull distribution, which is mostly resulted from the test on the small sample, three parameters of life distribution are estimated by the grey estimation in this paper. Then according to the data acquired in the test, Duane growth model is surely developed to assess the situation of reliability growth. Furthermore, the following example ascertains that the developed model is in accordance with mechanical characters. From the result, Duane model is reasonable to evaluate the reliability growth level of mechanical products. It is obvious that the improved measure is effective to enhance the reliability and the value of MTBF can be calculated with the model.

Small sample discrete reliability growth modeling using a grey systems model

2018 ◽  
Vol 8 (3) ◽  
pp. 246-271 ◽  
Author(s):  
Thomas Paul Talafuse ◽  
Edward A. Pohl
Keyword(s):  
Growth Model ◽  
Small Sample ◽  
System Level ◽  
Parameter Estimates ◽  
Reliability Growth ◽  
Sample Sizes ◽  
Content Type ◽  
Failure Data ◽  
Amsaa Model ◽  
Small Sample Sizes

PurposeWhen performing system-level developmental testing, time and expenses generally warrant a small sample size for failure data. Upon failure discovery, redesigns and/or corrective actions can be implemented to improve system reliability. Current methods for estimating discrete (one-shot) reliability growth, namely the Crow (AMSAA) growth model, stipulate that parameter estimates have a great level of uncertainty when dealing with small sample sizes. The purpose of this paper is to present an application of a modified GM(1,1) model for handling system-level testing constrained by small sample sizes.Design/methodology/approachThe paper presents a methodology for incorporating failure data into a modified GM(1,1) model for systems with failures following a poly-Weibull distribution. Notional failure data are generated for complex systems and characterization of reliability growth parameters is performed via both the traditional AMSAA model and the GM(1,1) model for purposes of comparing and assessing performance.FindingsThe modified GM(1,1) model requires less complex computational effort and provides a more accurate prediction of reliability growth model parameters for small sample sizes and multiple failure modes when compared to the AMSAA model. It is especially superior to the AMSAA model in later stages of testing.Originality/valueThis research identifies cost-effective methods for developing more accurate reliability growth parameter estimates than those currently used.

scholarly journals Weibull Failure Probability Estimation Based on Zero-Failure Data

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Ping Jiang ◽  
Yunyan Xing ◽  
Xiang Jia ◽  
Bo Guo
Keyword(s):  
Weibull Distribution ◽  
Failure Probability ◽  
Development Time ◽  
Shape Parameter ◽  
Distribution Curve ◽  
Small Sample ◽  
Reliability Testing ◽  
Failure Data ◽  
Curve Method ◽  
Small Sample Sizes

Reliability testing is often carried out with small sample sizes and short duration because of increasing costs and the restriction of development time. Therefore, for highly reliable products, zero-failure data are often collected in such tests, which could not be used to evaluate reliability by traditional methods. To cope with this problem, the match distribution curve method was proposed by some researchers. The key step needed to exercise this method is to estimate the failure probability, which has yet to be solved in the case of the Weibull distribution. This paper presents a method to estimate the intervals of failure probability for the Weibull distribution by using the concavity or convexity and property of the distribution function. Furthermore, to use the method in practice, this paper proposes using the approximate value of the shape parameter determined by either engineering experience or by hypothesis testing through apvalue. The estimation of the failure probability is thus calculated using a Bayesian approach. A numerical example is presented to validate the effectiveness and robustness of the method.

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.

scholarly journals Statistical Reliability Assessment for Small Sample of Failure Data of Dumper Diesel Engines Based on Power Law Process and Maximum Likelihood Estimation

2021 ◽  
Vol 11 (12) ◽  
pp. 5387
Author(s):  
Brajeshkumar Kishorilal Dinkar ◽  
Alok Kumar Mukhopadhyay ◽  
Somnath Chattopadhyaya ◽  
Shubham Sharma ◽  
Firoz Alam ◽  
...  
Keyword(s):  
Diesel Engines ◽  
Hypothesis Test ◽  
Meta Analysis ◽  
Likelihood Estimation ◽  
Small Sample ◽  
Trend Test ◽  
Failure Data ◽  
Subsystem Level ◽  
Dump Trucks ◽  
Engine Failure

Dumpers or dump trucks are used all over the world to move overburden from many opencast mines. Diesel engines are the main driving force behind the trucks. The frequency of damage due to the failure of diesel engines is enormous. Therefore, efforts are necessary to analyze failure to reduce the downtime periods. A detailed analysis of engine failure at the subsystem level needs to be done. Reliability analysis and maintenance planning remain the norm in this regard. The obstacle faced while analysing the reliability of dumpers was the availability of a large number of data failures. In this paper, this issue is addressed by using Common Beta Hypothesis test and Meta-analysis test. The engine is divided into five subsystems. The result shows that all five subsystems pass the CBH test and Meta-analysis test. Accordingly, the failure data is grouped. The trend test of grouped failure data shows that the Failure data of two subsystems follows the independent and identically distributed characteristics while the remaining three do not follow it. The reliability is estimated for all five subsystems. Finally, fuel supply subsystems show the highest reliability while the lowest value is seen for self-starting subsystems.

scholarly journals Implementation of Maintenance Scenario for Critical Subsystem In Aircraft Engine: Case Study NTP CT7 Engine

2017 ◽  
Vol 1 (02) ◽  
pp. 52
Author(s):  
Fransiskus Tatas Dwi Atmaji ◽  
Anna Annida Noviyanti ◽  
Widia Juliani
Keyword(s):  
Aircraft Engine ◽  
Maintenance Cost ◽  
Optimal Time ◽  
Time Interval ◽  
Performance Loss ◽  
Maintenance Schedule ◽  
Failure Data ◽  
Reliability Centered Maintenance ◽  
Critical Components ◽  
Critical Subsystem

An aircraft company needs to "secure" their aircraft engine for a good maintenance system to keep the optimum engine's performance during the flight. This paper proposed maintenance analysis and scenario for the CT7, the main engine for aircraft at NTP company. A failure data record from four critical components of the CT7 engine is analyzed using Reliability Centered Maintenance (RCM) and Risk Based Maintenance (RBM) methods to obtain the optimum maintenance interval task for the critical subsystem of the CT7 engine and also seeing the risk of maintenance cost of the engine's failure effect. The RCM analysis result obtained seven scheduled on condition task, six scheduled discard task, and three scheduled restoration task. The interval of the maintenance schedule of each critical component varies according to the function obtained. And based RBM analysis, the risk from system performance loss is got $ 7.014.841, 90. Meanwhile, the total cost of maintenance interval based on a calculation of optimal time interval got $1.885.612, 82.

Multivariate probability distribution for sewer system vulnerability assessment under data-limited conditions

2015 ◽  
Vol 73 (4) ◽  
pp. 751-760 ◽  
Author(s):  
G. Del Giudice ◽  
R. Padulano ◽  
D. Siciliano
Keyword(s):  
Goodness Of Fit ◽  
Small Sample ◽  
Multivariate Normal ◽  
Sewer System ◽  
Modeling Tools ◽  
Factors Affecting ◽  
Failure Data ◽  
Multivariate Probability Distribution ◽  
Funds Management ◽  
Box Cox Transformation

The lack of geometrical and hydraulic information about sewer networks often excludes the adoption of in-deep modeling tools to obtain prioritization strategies for funds management. The present paper describes a novel statistical procedure for defining the prioritization scheme for preventive maintenance strategies based on a small sample of failure data collected by the Sewer Office of the Municipality of Naples (IT). Novelty issues involve, among others, considering sewer parameters as continuous statistical variables and accounting for their interdependences. After a statistical analysis of maintenance interventions, the most important available factors affecting the process are selected and their mutual correlations identified. Then, after a Box-Cox transformation of the original variables, a methodology is provided for the evaluation of a vulnerability map of the sewer network by adopting a joint multivariate normal distribution with different parameter sets. The goodness-of-fit is eventually tested for each distribution by means of a multivariate plotting position. The developed methodology is expected to assist municipal engineers in identifying critical sewers, prioritizing sewer inspections in order to fulfill rehabilitation requirements.

Sign in / Sign up

Export Citation Format

Share Document