Reliability Analysis of Repairable System With Multiple-Input and Multi-Function Component Based on Goal-Oriented Methodology

This paper presents an approach for reliability analysis of repairable systems with two-unit parallel structure considering Common Cause Failure (CCF) and maintenance correlation based on GO methodology. First, the GO algorithm for dealing with CCF is introduced. Then, the common cause failure probability formulas of two-unit parallel structure considering maintenance correlation are deduced based on Markov theory. Furthermore, the analysis process of the new GO method is formulated. Finally, the dynamic availability analysis of HTOSS is conducted by the GO method. And the result is compared with the result of system considering CCF, and the result of system without considering CCF and maintenance correlation. The results show that the CCF and maintenance correlation are not ignored for reliability analysis of such system. All in all, this study not only widens the application of GO method. But it also provides guidance and an approach for reliability analysis of repairable systems with two-unit parallel structure considering CCF and maintenance correlation.

Reliability analysis of repairable systems has been widely conducted based on collected information such as the systems’ failure counting processes. In many real-life situations, the failure counting processes may not be completely observed when some failure times are not recorded or missing due to various practical reasons. In the literature, only a limited number of reliability studies focus on missing failure time data for repairable systems with an assumption that the system repair is either perfect or minimal. When the repair effect is general as in many real-world situations, challenges arise for reliability analysis in that the observed and unobserved failure times are statistically dependent in a complex manner, and the time censoring problem becomes unconventional. In this paper, to overcome these challenges, we propose a reliability model for repairable systems with incomplete failure time data under general repair. The proposed model is suitable for modeling both a single system and multiple systems subject to failure heterogeneity. A maximum likelihood estimation method is developed to estimate the model parameters. Based on the proposed model, statistical inference of the missing times is developed. Simulation and real-life case studies are conducted to verify the developed methods.

Reliability analysis of repairable systems using Petri nets and vague Lambda-Tau methodology

This paper combines time varying failure rates and Markov chain analysis to obtain a hybrid reliability and availability analysis. However, combining these techniques can, depending on the size of the system, result in solutions of the Markov chain differential matrix equations that are intractable. This paper identifies solutions that are tractable, These form the analytical baseline for the reliability and availability analysis of systems with time varying failure rates. Tractable solutions were found for the 1-component 2-state and the 2-component 4-state configurations. Time varying failure rates were characterized by a general polynomial expression. Constant, linear, and Weibull failure rate functions are special cases of this polynomial. The general polynomial failure rate provides flexibility in modeling the time varying failure rates that occur in practice.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

A main challenge in reliability analysis of repairable systems is to model the heterogeneity in their failure behavior, which can be reflected by the corresponding recurrent failure-time data. To capture the system heterogeneity for data analysis, a system-specific random effect is typically introduced in most existing statistical models. In practice, the random effect of repairable systems tends to be time varying; for example, each repair action could change system's physical properties. Prior studies, however, generally do not take account of this time-varying nature and few of them circumvent the risk of model misspecification on the parametric distribution of frailty. In this article, we propose a semiparametric model that uses multivariate Gaussian convolution processes (MGCPs) to meet the above challenges. First, we use the trend RP to model the baseline intensity function of each repairable system. Based on the baseline intensity function, we then introduce MGCPs to simultaneously factor in heterogeneity and infer commonalities across multiple systems. A Bayesian framework is used for parameter estimation and time-to-failure prediction. Simulation studies show the advantages of our model in terms of robustness and estimation accuracy. A group of oil and gas well systems are used to illustrate the application of the proposed model.

This paper proposes a new reliability optimization allocation for multifunction systems with multistate units based on goal-oriented (GO) methodology. First, this optimization allocation method is expounded in terms of establishing GO model, establishing reliability optimization allocation model, and solving algorithm. Then its process is formulated. Finally, the new method is applied in reliability optimization allocation of power-shift steering transmission (PSST), whose goal is to minimize the system cost. The results analysis shows that the system costs for different operation times turn to a relatively stable value, and the allocated reliability indices of unit are satisfied with engineering requirements. All in all, this new optimization allocation method can not only obtain the reasonable allocation results quickly and effectively, but it also can overcome the disadvantages of existing reliability optimization allocation methods for complex multifunction systems efficiently. In addition, the analysis process shows that the reliability optimization allocation method based on GO method can provide a new approach for the reliability optimization allocation of multifunction systems with multistate units.

The aim of this paper is introducing a method based on Fuzzy Time-To Failure (FTTF) to improve reliability analysis of complex engineering systems based on fault tree analysis. This method focuses on the quantitative part of fault trees (either static or dynamic) analysis and will compute failure probabilities. FTTF model is developed to estimate the reliability of system and solve aforetime methods problems. The presented FTTF model is able to figure out any construction consist of static and dynamic gates with FTTF distributions integrated on Fuzzy Monte Carlo Simulation (FMCS) techniques to analyzing Possibilistic functions associated with the fuzzy probability distributions for each basic event. Using fuzzy algorithm, gates FTTF are generated, and Top-event TTF evaluated. Some case studies are used to demonstrate the priority of this method in exact evaluation in compared with other solving methods (like: BN, Analytical solution, Markov chain and traditional fuzzy fault tree modeling), but has much less effort while having higher accuracy. Finally, this model is implemented in an Emergency Detection System (EDS) which is a useful system in aerospace and space applications.

The study and analysis of repairable systems is an important topic in reliability. Analytical techniques become very complicated and unrealistic especially for modern complex systems. There have been attempts in the literature to evolve more realistic techniques using simulation approach for reliability analysis of systems. The purpose of this paper is to develop a novel Markov system dynamics (MSD) simulation framework for the reliability modeling and analysis of a repairable system. This paper proposes a hybrid approach called as MSD approach which combines the Markov approach with system dynamics simulation approach for reliability modeling. This approach will have the advantages of both Markov as well as system dynamics methodologies. The proposed framework is illustrated for a repairable two component system. The results of the simulation obtained in this work when compared with that obtained by traditional Markov analysis clearly validate that this novel MSD approach is an alternative approach for reliability modeling and analysis.

The reliability analysis of critical systems is often performed using fault tree analysis. Fault trees are analyzed using analytic approaches or Monte Carlo simulation. The usage of the analytic approaches is limited in few models and certain kinds of parameter distributions. In contrast to the analytic approaches, Monte Carlo simulation can be broadly used. However, Monte Carlo simulation is time-consuming because of the intensive computation. In this paper, a new model is presented which is called time-to-failure tree. Static and dynamic fault trees can be easily transformed into time-to-failure trees. In fact, each time-to-failure tree is a digital circuit, which can be synthesized to a field programmable gate array (FPGA). Therefore, Monte Carlo simulation can be significantly accelerated using FPGAs.

In this paper, the authors demonstrate how fault tree analysis could be used to perform reliability analysis of hardware-software systems. The functional dependency of the hardware components on the interfacing software components is appropriately modeled using fault trees. The MIT (Massachusetts Institute of Technology)-CSR's (Center for Space Research) ACIS (Advanced X-ray Astrophysics Facility Imaging Charge Couple Device Imaging Spectrometer) system is used to illustrate the fault tree analysis method in reliability analysis of complex hardware-software systems. The ACIS science instrument system is a space borne system to acquire and process X-ray images over the sky, and sends them to earth. It has hardware and software components with interfaces between them making it a very good example of a complex hardware software system. This approach could be used in analyzing other complex systems being designed today and in identifying the critical components to make the system safe and more reliable.

A novel approach for software reliability analysis of digital instrumentation and control systems in nuclear power plants

The multi-flows pneumatic braking system is one of the modern braking systems equipped with truck vehicles which is a very complex system consisting of many elements connected in series or parallel. The reliability of the braking system has a significant influence on safety and the ability to manoeuvre. The study of identifying low-reliability elements in the brake system is of great significance to improve the efficiency of maintenance and repair. This article shows the research results that determine low-reliability elements of the multi-flows pneumatic brake system based on GO methodology, and allow to examine each element's reliability and the complex system. The reliability assessment model is built based on GO theory and statistics data collected at the used units about the frequency of damage, ability, and time to repair and restore the assemblies in the brake system. By using this model, the reliability of the assemblies and the whole system can be determined at any point in time relative to the actual operating time of the brake system. The article's research results can be applied for surveying other components’ reliability and complex systems such as steering or suspension systems.

PurposeDegraded failures and sudden critical failures are quite prevalent in industries. Degradation processes commonly belong to Weibull family and critical failures are found to follow exponential distribution. Therefore, it becomes important to carry out reliability and availability analysis of such systems. From the reported literature, it is learnt that models are available for the situations where the degraded failures as well as critical failures follow exponential distribution. The purpose of this paper is to present models suitable for reliability and availability analysis of systems where the degradation process follows Weibull distribution and critical failures follow exponential distribution.Design/methodology/approachThe research uses Semi-Markov modeling using the approach of method of stages which is suitable when the failure processes follow Weibull distribution. The paper considers various states of the system and uses state transition diagram to present the transition of the system among good state, degraded state and failed state. Method of stages is used to convert the semi-Markov model to Markov model. The number of stages calculated in Method of stages is usually not an integer value which needs to be round off. Method of stages thus suffers from the rounding off error. A unique approach is proposed to arrive at failure rates to reduce the error in method of stages. Periodic inspection and repairs of systems are commonly followed in industries to take care of system degradation. This paper presents models to carry out reliability and availability analysis of the systems including the case where degraded failures can be arrested by appropriate inspection and repair.FindingsThe proposed method for estimating the degraded failure rate can be used to reduce the error in method of stages. The models and the methodology are suitable for reliability and availability analysis of systems involving degradation which is very common in systems involving moving parts. These models are very suitable in accurately estimating the system reliability and availability which is very important in industry. The models conveniently cover the cases of degraded systems for which the model proposed by Hokstad and Frovig is not suitable.Research limitations/implicationsThe models developed consider the systems where the repair phenomenon follows exponential and the failure mechanism follows Weibull with shape parameter greater than 1.Practical implicationsThese models can be suitably used to deal with reliability and availability analysis of systems where the degradation process is non-exponential. Thus, the models can be practically used to meet the industrial requirement of accurately estimating the reliability and availability of degradable systems.Originality/valueA unique approach is presented in this paper for estimating degraded failure rate in the method of stages which reduces the rounding error. The models presented for reliability and availability analyses can deal with degradable systems where the degradation process follows Weibull distribution, which is not possible with the model presented by Hokstad and Frovig.

Foamed asphalt is the main means of achieving largescale road repairs. Small foam asphalt mixture mixers are necessary test equipment before the road construction process. To avoid serious injuries due to equipment failure, the structural reliability of the mixer is analyzed using failure mode and effects analysis (FMEA) and fault tree analysis (FTA). Foamed asphalt is a metastable state product, and the mixer operation requires immediate and efficient operation, which results in a relatively complicated structure of the device. Potential faults are easily overlooked in reliability analyses of mixer systems. For FMEA, the familiarity proportionality coefficient should be introduced to ensure accurate ranking of the risk priority numbers and to reduce potential bias due to subjective evaluations by personnel. For FTA, the bottom event with the lowest probability of failure is removed while the bottom event with the smallest risk priority numbers and correlations are combined. The obtained FMEA table and structural fault tree model can improve the accuracy of the risk priority numbers, effectively reduce the workload of the reliability analysis, and obtain the structural reliability with less data.NomenclatureFoamed asphalt. Small foam asphalt mixture mixer. Risk priority number. Fault tree analysis. Failure mode and effects analysis. Reliability. The familiarity ratio coefficient of the scorer.

In view of the current study on the reliability analysis for complex new equipment systems, the fault tree analysis method is used in reliability engineering (Fault Tree Analysis, FTA). However, qualitative and quantitative analysis for complex equipment systems reliability are difficult problems. In this paper, firstly, fault tree analysis method is introduced, secondly, through an instance of a certain type of equipment, mathematical description and analysis methods for qualitative and quantitative analysis of system reliability are carried out. Finally, the results from fault tree analysis is presented and discussed, which shows that FTA is a method can quickly and accurately analyze equipment systems reliability, and is a designer tool for evaluation and improvement of system reliability.