scholarly journals Determining Interval Time of Maintenance in Bosowa Cement Indonesia using Reliability Method

2016 ◽  
Vol 3 (2) ◽  
pp. 62
Author(s):  
Muhammad Arsyad Suyuti ◽  
Rusdi Nur
Keyword(s):  
Reliability Analysis ◽  
System Reliability ◽  
Operating Conditions ◽  
Target System ◽  
Maintenance Strategies ◽  
Time Period ◽  
Reliability Method ◽  
Distribution Parameters ◽  
Mill Unit ◽  
Finish Mill

Maintenance for machining and production facility is an important aspect to ensure a smooth production process. During this time, it was performed regular maintenance based on technical advice from supplier’s engines which just shows things in general without considering the actual operating conditions. This paper aims to plan the maintenance strategies for the Finish Mill unit based on reliability analysis by considering the target system reliability and cost of improving reliability. The data distribution obtained the most appropriate distribution. Based on the data obtained distribution parameters, then the function of the reliability of each part an be determined so that the value of the reliability of each part and the overall system for a specific time period can be calculated. The results showed that the failure or breakdown Mill Finish Unit was majority caused by the part of 561.BM1, 531.WF1, 531.BC6, 531.BC2, 531.BC1, 561.SR1 and 531.BC3. it means that need to focus o the reliability analysis to allocate their parts.

System Reliability Analysis With Second Order Saddlepoint Approximation

2019 ◽  
Author(s):  
Hao Wu ◽  
Xiaoping Du
Keyword(s):  
Normal Distribution ◽  
Reliability Analysis ◽  
System Reliability ◽  
Multivariate Normal Distribution ◽  
High Efficiency ◽  
Saddlepoint Approximation ◽  
Second Order ◽  
Multivariate Normal ◽  
System Reliability Analysis ◽  
Reliability Method

Abstract The second order saddlepoint approximation (SPA) has been used for component reliability analysis for higher accuracy than the traditional second order reliability method. This work extends the second order SPA to system reliability analysis. The joint distribution of all the component responses is approximated by a multivariate normal distribution. To maintain high accuracy of the approximation, the proposed method employs the second order SPA to accurately generate the marginal distributions of component responses; to simplify computations and achieve high efficiency, the proposed method estimates the covariance matrix of the multivariate normal distribution with the first order approximation to component responses. Examples demonstrate the high effectiveness of the second order SPA method for system reliability analysis.

On the Use of Surrogate Models in Reliability-Based Analysis of Dented Pipes

2016 ◽  
Author(s):  
Sherif Hassanien ◽  
Muntaseer Kainat ◽  
Samer Adeeb ◽  
Doug Langer
Keyword(s):  
Finite Element ◽  
Reliability Analysis ◽  
Random Systems ◽  
Probability Of Failure ◽  
Surrogate Models ◽  
Operating Conditions ◽  
Additional Stress ◽  
Internal Stresses ◽  
Stress Risers ◽  
Reliability Method

Pipeline dents lead to changes in the stress/strain state of the pipe body, making it more susceptible to integrity concerns. This susceptibility is especially prevalent in cases where additional stress risers such as crack and/or corrosion features interact with the dented region. While some guidance is available in codes, regulations, and industry best practices, there is substantial room for innovation and improvement to ensure pipeline safety. Existing explicit models are primarily based on experimental correlations and historical findings using simple parameters such as dent depth and location on the pipeline. Moreover, these models are subjected to a substantial uncertainty in both accuracy and precision. This paper presents a state-of-the-art methodology for analyzing dents and dents associated with stress risers through the use of finite element method (FEM) as a mechanical model and reliability analysis to address uncertainties associated with input variables. FEM is used to model the full geometry of dents and any interacting stress risers reported by inline inspection (ILI) to be incorporated into calculations of the internal stresses/strains within the feature. Theoretically, FEM and reliability analysis can be integrated through reliability-based stochastic finite element methodologies due to the absence of closed form mechanical models of dented pipes. However, these methodologies are computationally prohibitive and not suited/designed for frequent integrity analysis. This study aims at further advancing such integration by combining FEM with reliability science to account for pipe properties and measurement uncertainties in order to determine the probability of failure under different operating conditions using surrogate models. This provides the opportunity to more accurately assess the risk posed by ILI reported dent features. Herein, surrogate models refer to the response surface method (RSM) which is considered as a valuable tool for obtaining insight into the behavior of structural random systems at low computational costs. The proposed approach was applied focusing on a plain dent, a dent interacting with a corrosion feature, and a dent interacting with a crack feature. First Order Reliability Method (FORM) is used to evaluate the probability of failure/reliability using the resulting RSM non-linear limit states for each dent feature.

Time-Dependent System Reliability Analysis Using Random Field Discretization

2015 ◽  
Vol 137 (10) ◽  
Author(s):  
Zhen Hu ◽  
Sankaran Mahadevan
Keyword(s):  
Random Field ◽  
Reliability Analysis ◽  
System Reliability ◽  
Probability Of Failure ◽  
Two Dimensions ◽  
Time Dependent ◽  
Time Interval ◽  
System Reliability Analysis ◽  
Reliability Method ◽  
Time Dependent System

This paper proposes a novel and efficient methodology for time-dependent system reliability analysis of systems with multiple limit-state functions of random variables, stochastic processes, and time. Since there are correlations and variations between components and over time, the overall system is formulated as a random field with two dimensions: component index and time. To overcome the difficulties in modeling the two-dimensional random field, an equivalent Gaussian random field is constructed based on the probability equivalency between the two random fields. The first-order reliability method (FORM) is employed to obtain important features of the equivalent random field. By generating samples from the equivalent random field, the time-dependent system reliability is estimated from Boolean functions defined according to the system topology. Using one system reliability analysis, the proposed method can get not only the entire time-dependent system probability of failure curve up to a time interval of interest but also two other important outputs, namely, the time-dependent probability of failure of individual components and dominant failure sequences. Three examples featuring series, parallel, and combined systems are used to demonstrate the effectiveness of the proposed method.

Complementary Intersection Method for System Reliability Analysis

2009 ◽  
Vol 131 (4) ◽  
Author(s):  
Byeng D. Youn ◽  
Pingfeng Wang
Keyword(s):  
Reliability Analysis ◽  
System Reliability ◽  
High Efficiency ◽  
Second Order ◽  
Approximation Formula ◽  
Joint Failure ◽  
System Reliability Analysis ◽  
Reliability Method ◽  
Reliability Methods ◽  
Intersection Method

Although researchers desire to evaluate system reliability accurately and efficiently over the years, little progress has been made on system reliability analysis. Up to now, bound methods for system reliability prediction have been dominant. However, two primary challenges are as follows: (1) Most numerical methods cannot effectively evaluate the probabilities of the second (or higher)–order joint failure events with high efficiency and accuracy, which are needed for system reliability evaluation and (2) there is no unique system reliability approximation formula, which can be evaluated efficiently with commonly used reliability methods. Thus, this paper proposes the complementary intersection (CI) event, which enables us to develop the complementary intersection method (CIM) for system reliability analysis. The CIM expresses the system reliability in terms of the probabilities of the CI events and allows the use of commonly used reliability methods for evaluating the probabilities of the second–order (or higher) joint failure events efficiently. To facilitate system reliability analysis for large-scale systems, the CI-matrix can be built to store the probabilities of the first- and second-order CI events. In this paper, three different numerical solvers for reliability analysis will be used to construct the CI-matrix numerically: first-order reliability method, second-order reliability method, and eigenvector dimension reduction (EDR) method. Three examples will be employed to demonstrate that the CIM with the EDR method outperforms other methods for system reliability analysis in terms of efficiency and accuracy.

A Time-Variant Reliability Analysis Method Based on Stochastic Process Discretization

2014 ◽  
Vol 136 (9) ◽  
Author(s):  
C. Jiang ◽  
X. P. Huang ◽  
X. Han ◽  
D. Q. Zhang
Keyword(s):  
Stochastic Process ◽  
Reliability Analysis ◽  
System Reliability ◽  
Limit State ◽  
Complex Structure ◽  
Random Variable ◽  
State Function ◽  
Analysis Method ◽  
Reliability Problem ◽  
Reliability Method

Time-variant reliability problems caused by deterioration in material properties, dynamic load uncertainty, and other causes are widespread among practical engineering applications. This study proposes a novel time-variant reliability analysis method based on stochastic process discretization (TRPD), which provides an effective analytical tool for assessing design reliability over the whole lifecycle of a complex structure. Using time discretization, a stochastic process can be converted into random variables, thereby transforming a time-variant reliability problem into a conventional time-invariant system reliability problem. By linearizing the limit-state function with the first-order reliability method (FORM) and furthermore, introducing a new random variable, the converted system reliability problem can be efficiently solved. The TRPD avoids the calculation of outcrossing rates, which simplifies the process of solving time-variant reliability problems and produces high computational efficiency. Finally, three numerical examples are used to verify the effectiveness of this approach.

Time-Dependent Reliability Analysis for Bivariate Responses

2015 ◽  
Author(s):  
Zhen Hu ◽  
Zhifu Zhu ◽  
Xiaoping Du
Keyword(s):  
Reliability Analysis ◽  
System Reliability ◽  
General System ◽  
Time Dependent ◽  
Series System ◽  
Analysis Method ◽  
First Order ◽  
Reliability Method ◽  
General Systems ◽  
Bivariate Responses

Time-dependent system reliability is measured by the probability that the responses of a system do not exceed prescribed failure thresholds over a period of time. In this work, an efficient time-dependent reliability analysis method is developed for bivariate responses that are general functions of random variables and stochastic processes. The proposed method is based on single and joint upcrossing rates, which are calculated by the First Order Reliability Method (FORM). The method can efficiently produce accurate upcrossing rates for the systems with two responses. The upcrossing rates can then be used for system reliability predictions with two responses. As the general system reliability may be approximated with the results from reliability analyses for individual responses and bivariate responses, the proposed method can be extended to reliability analysis for general systems with more than two responses. Two examples, including a parallel system and a series system, are presented.

Time-Dependent System Reliability Analysis With Second-Order Reliability Method

2020 ◽  
Vol 143 (3) ◽  
Author(s):  
Hao Wu ◽  
Zhangli Hu ◽  
Xiaoping Du
Keyword(s):  
Reliability Analysis ◽  
System Reliability ◽  
Limit State ◽  
Second Order ◽  
Time Dependent ◽  
Component Reliability ◽  
Reliability Method ◽  
Series Systems ◽  
State Functions ◽  
Time Dependent System

Abstract System reliability is quantified by the probability that a system performs its intended function in a period of time without failures. System reliability can be predicted if all the limit-state functions of the components of the system are available, and such a prediction is usually time consuming. This work develops a time-dependent system reliability method that is extended from the component time-dependent reliability method using the envelope method and second-order reliability method. The proposed method is efficient and is intended for series systems with limit-state functions whose input variables include random variables and time. The component reliability is estimated by the second-order component reliability method with an improve envelope approach, which produces a component reliability index. The covariance between component responses is estimated with the first-order approximations, which are available from the second-order approximations of the component reliability analysis. Then, the joint distribution of all the component responses is approximated by a multivariate normal distribution with its mean vector being component reliability indexes and covariance being those between component responses. The proposed method is demonstrated and evaluated by three examples.

Time-Dependent System Reliability Analysis With Second Order Reliability Method

2020 ◽  
Author(s):  
Hao Wu ◽  
Xiaoping Du
Keyword(s):  
Reliability Analysis ◽  
System Reliability ◽  
Limit State ◽  
Second Order ◽  
Time Dependent ◽  
Component Reliability ◽  
Reliability Method ◽  
Series Systems ◽  
State Functions ◽  
Time Dependent System

Abstract System reliability is quantified by the probability that a system performs its intended function in a period of time without failure. System reliability can be predicted if all the limit-state functions of the components of the system are available, and such a prediction is usually time consuming. This work develops a time-dependent system reliability method that is extended from the component time-dependent reliability method that uses the envelop method and second order reliability method. The proposed method is efficient and is intended for series systems with limit-state functions whose input variables include random variables and time. The component reliability is estimated by the existing second order component reliability method, which produces component reliability indexes. The covariance between components responses are estimated with the first order approximations, which are available from the second order approximations of the component reliability analysis. Then the joint probability of all the component responses is approximated by a multivariate normal distribution with its mean vector being component reliability indexes and covariance being those between component responses. The proposed method is demonstrated and evaluated by three examples.

Reliability Analysis by Multilevel Flow Models: A Qualitative Analysis Method and Its Application for FTA Validation

2009 ◽  
Author(s):  
Ming Yang ◽  
Zhijian Zhang ◽  
Jie Liu ◽  
Shengyuan Yan
Keyword(s):  
Reliability Analysis ◽  
System Reliability ◽  
Failure Modes ◽  
System Modeling ◽  
Fault Tree ◽  
System Level ◽  
Injection System ◽  
Target System ◽  
Flow Models ◽  
Analytical Technique

Fault Tree Analysis (FTA) is a powerful analytical technique for analyzing system reliability and safety by enumerating any possible safety-critical failure modes, which is very useful for identifying the risks and weaknesses in the system. Therefore, FTA is widely applied to the safety evaluation of large-scale and mission-critical systems. However, the following problems are usually pointed out when building a fault tree for a complex system: 1) System modeling is a hard and time consuming work, and 2) FTA models are difficult to be validated. In this paper, we propose a new method for system reliability analysis based on Multilevel Flow Models (MFM) and Goal Tree-Success Tree (GTST) methods. We use Goal Tree (GT) methodology to model the target system at a higher and system level, and use the Success Tree (ST) together with MFM at a lower and functional level. In this way, modeling effort could be significantly reduced. In this paper, an algorithm is also presented to translate the GTST-MFM model into ST model based on which qualitative reliability analysis can be performed by the Fusell-vesely algorithm. In this paper, a Low Head Safety Injection System (LHSIS) is taken as a case study to exemplify how to apply our proposed GTST-MFM method to model the system and to validate fault trees directly built by deductive method.

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