Reliability analysis methods for healthcare system

PurposeAs a clean and renewable energy source, wind energy will become one of the main sources of new energy supply in the future. Relying on the stable and strong wind resources at sea, wind energy has great potential to become the primary energy. As a critical part of the wind turbine, the gearbox of a wind turbine often bears a large external load. Especially at sea, due to the effects of ocean corrosion, waves and wind, the burden of the wind turbine gearbox is greater, which brings great challenges to its reliability analysis. This study aims to systematically review the reliability research in wind turbine gearboxes and guide future research directions and challenges.Design/methodology/approachThis study systematically reviews some design requirements and reliability analysis methods for wind turbine gearboxes. Then, it summarizes previous studies on wind load uncertainty modeling methods, including the processing of wind measurement data and the summary of three different classifications of random wind speed prediction models. Finally, existing reliability analysis studies on two major parts of the gearbox are described and summarized.FindingsFirst, the basic knowledge of wind turbine gearboxes and their reliability analysis is introduced. The requirements and reliability analysis methods of wind turbine gearboxes are explained. Then, the processing methods of wind measurement data and three different random wind speed prediction models are described in detail. Furthermore, existing reliability analysis studies on two common parts of wind turbine gearboxes, gears and bearings, are summarized and classified, including a summary of bearing failure modes. Finally, three possible future research directions for wind turbine gearbox reliability analysis are discussed, namely, reliability research under the influence of multiple factors on gears, damage indicators of bearing failure modes and quantitative evaluation criteria for the overall dynamic characteristics of offshore wind turbine gearboxes and a summary is also given.Originality/valueThis paper aims to systematically introduce the relevant contents of wind turbine gearboxes and their reliability analysis. The contents of wind speed data processing, predictive modeling and reliability analysis of major components are also comprehensively reviewed, including the classification and principle introduction of these contents.

Fuzzy reliability analysis can be implemented using two discrete optimization maps in the processes of reliability and fuzzy analysis. Actually, the efficiency and robustness of the iterative reliability methods are two main factors in the fuzzy-based reliability analysis due to the huge computational burdens and unstable results. In the structural fuzzy reliability analysis, the first-order reliability method (FORM) using discrete nonlinear map can provide a C membership function. In this paper, a discrete nonlinear conjugate map is proposed using a relaxed-finite step size method for fuzzy structural reliability analysis, namely Fuzzy conjugate relaxed-finite step size method fuzzy CRS. A discrete conjugate map is stabilized using two adaptive factors to compute the relaxed factor and step size in FORM. The framework of the proposed fuzzy structural reliability method is established using two linked iterative discrete maps as an outer loop, which constructs the membership function of the response using alpha level set optimization based on genetic operator, and the inner loop, implemented for reliability analysis using proposed conjugate relaxed-finite step size method. The fuzzy CRS and fuzzy HL-RF methods are compared to evaluate the membership functions of five structural problems with highly nonlinear limit state functions. Results demonstrated that the fuzzy CRS method is computationally more efficient and is strongly more robust than the HL-RF for fuzzy-based reliability analysis of the nonlinear structural reliability problems.

This thesis presents Version 2.0 of Software Tool for Reliability Estimation (STORE 2.0). It expands on the work done by Parekh [1] by revising the algorithm for tie-set and cut-set calculation, by including fault tree reliability analysis, by analyzing state dependent system, and by integrating component and system reliability analysis.;This thesis also presents an approach to the simplification of complex systems by collapsing series and parallel components into a sub-system. The approach was illustrated on an example described by Nelson et al. [2]. The example had 16 components resulting in ten cut-sets and fifty five tie-sets. Upon simplification, the problem was reduced to one tie-set only.;STORE 2.0 integrates parameter estimation, component reliability analysis, system reliability analysis, estimation of reliability of state dependent systems, and fault tree analysis. It was verified and validated on several examples taken from the open literature. The software was developed in Visual Basic 2008 with SQL as the database.

Bayesian networks inference algorithm to implement Dempster Shafer theory in reliability analysis

This paper presents a general mechanical system reliability modeling and analysis method, which consider the correlations of failure modes. Firstly, the performance functions which mathematically represent the failure modes are linearized based on FORM (First Order Reliability Methods), and the correlation coefficient matrix of those functions is calculated. Then, the PFTA (Probabilistic Fault Tree Analysis) method is adopted to establish the system reliability model which can take the correlations into account. Finally, effective algorithms such as G-FOMN (General First Order Multinormal Method) are used to quantify the system reliability, and the sensitivities of the system reliability index with respect to each failure mode, as well as the mean value and standard deviation of each random variable are also calculated. An engineering example demonstrates the applicability of the proposed method.

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.

In this study, long term component reliability analysis of PV system is performed using Markov method. Four major components for evaluation of the system are considered with 16 different states to evaluate a PV system as a package with one reliability index. The probability analysis for each state shows that although at very first years the states of combination of multiple failures are negligible, after several years of operation, all states are effective in the reliability of the system. Based on Markov modeling given in the study and actual failure and repair rates of components, the reliability analysis shows that the reliability of the system decreases from 0.99 to 0.81 in 20 years. The initial condition for each year is gained from the final condition of the previous year. The annual value of the reliability of PV system makes the designers plan major maintenance and switching based on cost and load. 1

In recent years, time‐variant reliability issues have garnered widespread attention, particularly due to their consideration of the cumulative effects of material degradation and the time dependency of stochastic process loads. Methods based on the outcrossing rate are typically employed for time‐variant reliability analysis. However, they fail to effectively capture the correlations among multiple failure modes. To address this limitation, this paper proposes a time‐variant reliability analysis method that accounts for the correlation of multiple failure modes by integrating copula and PHI2. The method first utilizes Copula to analyze the correlations among various failure modes, followed by applying the PHI2 method for the time‐dependent reliability analysis of each individual failure mode. Ultimately, case studies confirm that the proposed method proficiently evaluates the time‐variant reliability of systems with multiple failure modes, yielding a maximum relative error below 5% compared to MCS and significant performance enhancements.

Efficient system reliability analysis illustrated for a retaining wall and a soil slope

The main objective of system reliability measures or indices is to quantify the behavior of the system related to its failures. This chapter describes some of the commonly used analytical methods in power system reliability evaluation. It provides a detailed description of the methods: State space method using Markov processes; Network reduction method; Conditional probability method; and Cut‐set or Tie‐set method. The state space approach is used to calculate reliability indices when all system states are described by a state‐transition diagram. Network reduction method can be applied for systems whose interactions among components can be easily captured by a block diagram called reliability block diagram. The main idea of cut‐set and tie‐set methods is to represent a reliability block diagram of a complicated structure by an equivalent one in a combination of series and parallel structures.

In this paper, a new method is developed that can be applied efficiently in reliability analysis and design verification of structures under random vibration. This method is based on singular value decomposition of modal coordinates cross covariance matrix. Computational algorithm and relative flow chart for this method is developed and compatible reliability criterion with this algorithm has been extracted. In order to results verification, we use Miles relation and Monte Carlo simulation -that are two well known methods in reliability analysis- and all of these methods are applied to a simple finite element model. Results show that new algorithm is more efficient for reliability analysis of structures under random vibration.

A new unequal-weighted sampling method for efficient reliability analysis

Engineering fuzzy heat conduction problem with subjective uncertainties in input parameters constitutes a significant challenge. Based on fuzzy and interval theory, this paper presents novel numerical methods to efficiently identify the effect of fuzzy uncertainty on the system reliability analysis and optimization design. Firstly using the interval ranking strategy, the interval safety possibility in the transition state can be precisely quantified, and the eventual fuzzy safety possibility is calculated by integral operation. Then a fuzzy reliability-based optimization model is established with considerable computational cost caused by the two-layer nested loop. In order to improve the computational efficiency, a subinterval perturbation method based on the first-order Taylor series is presented to replace the inner loop. Comparing numerical results with traditional reliability model, two numerical examples are provided to evidence the superiority of proposed model and method for fuzzy reliability analysis and optimization in practical engineering.

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.

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.