Reliability Optimization Allocation Method for Multifunction Systems With Multistate Units Based on Goal-Oriented Methodology

This paper proposes a new goal-oriented (GO) method for reliability analysis of repairable systems with multiple-input and multi-function component (MIMFC). First, a new GO operator combination, which is composed of a new function GO operator and a new auxiliary GO operator, is created to represent MIMFC. The new function GO operator named as Type 22 operator is created to represent MIMFC itself, and the auxiliary GO operator named as Type 15B operator is created to represent multi-condition control signals of MIMFC. Then, GO operation formulas of the new GO operator combination are deduced based on logical relationships among inputs, outputs, and the component itself. The reliability analysis process of the new GO method is formulated. Furthermore, this new GO method is applied for the first time in steady availability analysis and qualitative analysis of the fan drive system of a power-shift steering transmission. Finally, the results obtained by the new GO method are compared with the results of fault tree analysis (FTA) and Monte Carlo simulation (MCS), and the comparison results show that this new GO method is reasonable and advantageous in reliability analysis of repairable systems with MIMFC. Moreover, the analysis process shows that it is more advantageous in the aspect of building system models and conducting reliability analysis. Overall, this paper not only improves the basic theory of the GO method and expands the application of the GO method, but it also provides a new approach for reliability analysis of repairable systems with MIMFC.

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.

Lagrange multiplier method for optimal reliability allocation in a series system

With the deepening of multi-energy flow coupling and the diversification of energy utilization demand, integrated energy system (IES) puts forward higher requirements for the economy and reliability of capacity allocation scheme. However, there is a complex relationship between economy and reliability, which makes it difficult for them to achieve coordinated optimization. Therefore, an economic and reliability optimal capacity allocation method of IES based on improved co-evolution algorithm is proposed. Firstly, the timing scenario reduction method of “wind power-electrical load” based on ordered clustering algorithm and K-means clustering algorithm is proposed. Aiming at minimizing the expected annual cost of construction investment, operation and maintenance, carbon emission and energy purchase cost, and constrained by equipment operation and energy flow balance, a multi-scenario economic operation model is constructed. Considering the dissatisfaction degree of comprehensive energy consumption, the energy supply reliability evaluation model based on Markov chain Monte Carlo (MCMC) method is established. On this basis, the IES multi-objective optimal capacity allocation model is established. Further, based on the orthogonal test method, the influence factors of decision variables on IES economy and reliability are calculated, and the evolution subpopulation of economy and reliability is established, and then the optimization model’s solution method based on improved co-evolution algorithm is proposed. Finally, an example simulation analysis is carried out combined with the actual data of a certain region. The simulation results show that the proposed model can cooperatively improve the economy and reliability of IES capacity allocation scheme, and provide a reference for the optimal capacity allocation of equipment in IES.

Remaining useful life prediction is a critical issue to fault diagnosis and health management of power-shift steering transmission. Power-shift steering transmission wear, which leads to the increase of wear particles and severe wear afterwards, is a slow degradation process, which can be monitored by oil spectral analysis, but the actual degree of the power-shift steering transmission degradation is often difficult to evaluate. The main purpose of this article is to provide a more accurate remaining useful life prediction methodology for power-shift steering transmission compared to relying solely on an individual spectral oil data. Our methodology includes multiple degradation data fusion, degradation index construction, degradation modelling and remaining useful life estimation procedures. First, the robust kernel principal component analysis is used to reduce the data dimension, and the state space model is utilized to construct the wear degradation index. Then, the Wiener process–based degradation model is established based on the constructed degradation index, and the explicit formulas for several important quantities for remaining useful life estimation such as the probability density function and cumulative distribution function are derived. Finally, a case study is presented to demonstrate the applicability of the proposed methodology. The results show that the proposed remaining useful life prediction methodology can objectively describe the power-shift steering transmission degradation law, and the predicted remaining useful life has been extended as 65 Mh (38.2%) compared with specified maintenance interval. This will reduce the maintenance times of power-shift steering transmission life cycle and finally save the maintenance costs.

Purpose – Reliability evaluation of healthcare services has been a challenging task for both operations managers and system engineers working in the respective field. The purpose of this paper is to develop a data envelopment analysis-based reliability allocation model. Design/methodology/approach – A two-phase optimization scheme for the reliability evaluation and allocation of homogeneous system entities, namely, hospitals, operating in a healthcare network is proposed. First, reliability evaluation is performed nonparametrically through the frontier estimation technique data envelopment analysis by considering several failure modes and failure free discharged patients as the inputs and output of the service system. Subsequently, optimal reliability allocation that maximizes the overall network reliability subject to a budget constraint is carried out by utilizing weights of the inputs and output calculated on the Pareto optimal frontier, which is constructed from the most reliable hospitals operating in the network. Findings – The popular performance assessment methodology DEA is found to be an invaluable reliability assessment and allocation tool, where optimal weights of the associated envelopment model, under certain budget restrictions, are used to maximize overall network reliability. Originality/value – An empirical illustration of the proposed model is presented on a set of hospital network data from Turkey. Modeling implications can be carried out on similar service operations where identification of the critical performance indicator costs is possible.

통상적으로 표준적인 최적배분은 층별 조사비용을 고려하여 표본을 배분한다. 만일 조사단위당 비용이 서로 다를 경우 보다 현실적인 배분방법을 고려할 필요가 있다. 즉, 개별 조사단위의 특성에 따라 이익비용비를 최대로 하는 단위를 먼저 표본으로 고려하는 동적배분을 고려하였다. 이러한 관점에서 층별 표본수를 배분하고, 배분된 표본규모에 따라 임의로 표본을 선정하는 방식인 표준적인 최적배분과는 차이가 있다. 이 논문은 약물오용, 낙태, 알콜중독 등과 같은 민감한 특성을 조사하는 층화확률화 응답기법에 대해 각 층별로 표본을 배분할 경우 최적 동적배분을 고려하여 보다 현실적인 문제를 해결하고자 하였으며, 수치적 예제를 통해 동적배분 방법의 효과성을 증명하였다. Typically the standard optimal allocation method distributes the sample for each stratum considering survey cost. In case of varying survey cost for each survey unit, we need to consider more practical allocation method. In other words, according to characteristics of an individual unit, we consider the optimal dynamic allocation method which first selects the survey unit having maximum value of benefit cost ratio. In terms of this, the proposed allocation method is different from standard optimal allocation method which allocate samples for each stratum and selects the random sample according to each size of sample. This paper is considered the dynamic optimal allocation method for the stratified randomized response technique which surveys for sensitive characteristic of survey units such as drug abuse, abortion, alcoholic. We prove the practical usefulness of proposed method using the numerical example.

Problems of optimal reliability and redundancy allocation are not new. However, the literature describes two isolated situations, either when redundancy allocation is the target and the reliability metrics of the components are known, or focusing on the reliability allocation, that demands the metrics to be known in advance and the use of some optimization approach. In the present paper, we consider a combined situation, i.e., when both situations must be considered together. To guide the allocation process, we developed a cost function that considers the costs of acquisition, development and/or improvement as a function of monetary effort, along with the reliability target and expected failure costs. The results considering two classical test problems in the literature demonstrated the efficacy of the proposed approach to deal with similar situations.

The paper presents a method for obtaining an optimal reliability allocation of an n-stage series system. In each stage, redundant comnponents can be added (in parallel, stand-by, or k-out-of-n:G, etc.), or a more reliable component can be used in order to improve the system reliability. The solution is obtained by repeatedly using a more reliable candidate at each stage that has the greatest value of a `weighted sensitivity function'. The balance between the objective unction and the constraints is controlled by a `balancing coefficient'. The overall computational procedure is given and an example is presented. The computations are given for a set of randomly generated test problems in which the optimal parallel redundancy under linear onstraints is determined. The proposed method is then compared with other methods.

Optimal stochastic scenario-based allocation of smart grids’ renewable and non-renewable distributed generation units and protective devices

Recent Advances in Optimal Reliability Allocation

Optimal effort allocation in stratified, large area trawl surveys, with application to shrimp surveys in the Barents Sea

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To solve the question of guaranteeing the safety of a system with a complex structure and tight couplings, a new method of optimal allocation exploiting the dynamical planning theory is presented. Two key factors that can affect system safety, i.e., accident loss and accident rate are analyzed. Then, to measure comprehensively requirements of system safety, accident safety level is defined based on these two factors. Subsequently, optimal allocation for complex systems’ safety resource is implemented according to the network properties of the inherent structures of the systems and constraint conditions for safe investments. Finally, a case study shows that the method using dynamical planning for optimal allocation of system safety can ensure preferential allocations of the parts with high safety requirements. Besides, this optimal allocation method is feasible and valid.

The regional integrated energy system, as a comprehensive energy production form which integrates the production and conversion of various energy sources, is characterized by high energy utilization rate, multi-energy complementation, cleanness and low carbon, and its optimal allocation method is of great significance for realizing energy conservation and emission reduction and ensuring regional energy safety. In this paper, an optimal allocation model of regional IES with topology planning is established to construct a mixed integer linear programming model with the objective of economic optimization by introducing the selection of power lines and heat transmission pipelines and capacity allocation constraints and effectively taking into account factors such as network loss, upper capacity limit and voltage amplitude, with the distributed energy stations as basic node units. The test results show that the planning model established in this paper can effectively reduce the installation capacity of redundant equipment, improve the economy of the planning scheme and the comprehensive energy efficiency of the system, and has certain reference value for the planning and construction of IES.