Reliability Importance Measures Research Articles

Link interaction for K-terminal network cascading failures subject to saturating branching process

When improving a multi-component system reliability, we often replaced or maintained a component while ignoring the reliability of another component due to the budget cost constraint. Thus, the interaction effects regarding system reliability between two components should be analyzed. However, conventional research efforts concerning component interactions have two gaps: (i) they are unsuitable for systems with complex structures, such as K-terminal network (for short network), only pertaining to multi-component systems with simple structures; and (ii) they fail to consider component interactions in the context of cascading failure. To fill these gaps, this paper analyzes the effects of link interactions regarding network reliability in the context of link cascading failures. Under this context, we characterize the total number of link failures by a saturating branching process, based on which a network reliability model is established. According to the reliability model, we introduce the joint reliability importance (JRI) measure of two links to analyze link interactions. To evaluate the JRI, we derive several equations and construct a numerical algorithm from the theoretical and numerical standpoints, respectively. Theoretically, we find the conditions under which the scale and sign of the JRI is decided, so that the extent and type of interactions for two links can be analyzed. Numerically, an experiment is conducted to demonstrate how to apply JRI to evaluate link interactions in the circumstance of link cascading failures, which further confirms the theoretical analysis.

Systems Reliability and Data Driven Analysis for Marine Machinery Maintenance Planning and Decision Making

Understanding component criticality in machinery performance degradation is important in ensuring the reliability and availability of ship systems, particularly considering the nature of ship operations requiring extended voyage periods, usually traversing regions with multiple climate and environmental conditions. Exposing the machinery system to varying degrees of load and operational conditions could lead to rapid degradation and reduced reliability. This research proposes a tailored solution by identifying critical components, the root causes of maintenance delays, understanding the factors influencing system reliability, and recognising failure-prone components. This paper proposes a hybrid approach using reliability analysis tools and machine learning. It uses dynamic fault tree analysis (DFTA) to determine how reliable and important a system is, as well as Bayesian belief network (BBN) availability analysis to assist with maintenance decisions. Furthermore, we developed an artificial neural network (ANN) fault detection model to identify the faults responsible for system unreliability. We conducted a case study on a ship power generation system, identifying the components critical to maintenance and defects contributing to such failures. Using reliability importance measures and minimal cut sets, we isolated all faults contributing over 40% of subsystem failures and related events. Among the 4 MDGs, the lubricating system had the highest average availability of 67%, while the cooling system had the lowest at 38% using the BBN availability outcome . Therefore, the BBN DSS recommended corrective action and ConMon as maintenance strategies due to the frequent failures of certain critical parts. ANN found overheating when MDG output was above 180 kVA, linking component failure to generator performance. The findings improve ship system reliability and availability by reducing failures and improving maintenance strategies.

Copula-Based Reliability Analysis of Vehicles Based on Censored Failures Data Using Reliability Importance Measures

Copula-Based Reliability Analysis of Vehicles Based on Censored Failures Data Using Reliability Importance Measures

A new integrated strategy for optimising the maintenance cost of complex systems using reliability importance measures

ABSTRACT With the aging of production systems, failure modes become more common resulting in additional maintenance costs. To reduce these costs excess, flexible and smart maintenance strategies should be considered. This article proposes an integrated condition-based maintenance method associating opportunistic and importance measure concepts (IMC). The objective of an IMC-based model is to determine the contribution of the system’s components according to their criticality degree for reliability improvement and maintenance planning. The identification of the best maintenance planning consists of determining the expected minimal cost that guarantees the repair actions of a group of critical components in one shot. Therefore, determining IMC values is not so easy for complex systems, it requires knowing their operational structure, the determination of the reliability value of each configuration and finally calculating each component's IMC degree and ranking them for the prioritisation selection. For testing the proposed method, an industrial case study has been used, regarding a complex system whose components fail at random times. The system undergoes minimal repairs if one or more components fail accidentally or by decision after inspection actions. The numerical results show that the developed approach incurs minimal maintenance costs, and can be integrated as a decision-aid solution for manufacturers.

Component Criticality Analysis for Improved Ship Machinery Reliability

Redundancy in ship systems is provided to ensure operational resilience through equipment backups, which ensure system availability and offline repairs of machinery. The electric power generation system of ships provides the most utility of all systems; hence, it is provided with a good level of standby units to ensure reliable operations. Nonetheless, the occurrence of undesired blackouts is common onboard ships and portends a serious danger to ship security and safety. Therefore, understanding the contributing factors affecting system reliability through component criticality analysis is essential to ensuring a more robust maintenance and support platform for efficient ship operations. In this regard, a hybrid reliability and fault detection analysis using DFTA and ANN was conducted to establish component criticality and related fault conditions. A case study was conducted on a ship power generation system consisting of four marine diesel power generation plants onboard an Offshore Patrol Vessel (OPV). Results from the reliability analysis indicate an overall low system reliability of less than 70 percent within the first 24 of the 78 operational months. Component criticality-using reliability importance measures obtained through DFTA was used to identify all components with more than a 40 percent contribution to subsystem failure. Additionally, machine learning was used to aid the reliability analysis through feature engineering and fault identification using Artificial Neural Network classification. The ANN has identified a failure pattern threshold at about 200 kva, which can be attributed to overheating, hence establishing a link between component failure and generator performance.

Stochastic analysis of a non-identical three units repairable system with operational priority to simultaneously working of identical units

PurposeThe purpose of present study is to carry out stochastic analysis of some important reliability measures of a three-unit system where there are two types of units called type-I and type-II. In type-II, two identical units work simultaneously to meet the system requirements while in type-I, a single unit is kept in spare and can be used to work whenever required at the failure of either of the units of type-II. The system starts its operation with type-II units and thus priority is given to the operation of type-II units. The type II units are different to type-I unit but can perform the same task. There is a single server who handles repair activities of both type of units and the units are assumed to be in pristine condition after every repair. Failures of both types of units are independent and constant. The system can fail at the failure of either of the type-II units and type-I unit.Design/methodology/approachSemi-Markov process and regenerative point technique are used to study the behaviour of different reliability measures of the system.FindingsIn this paper, a three-unit non-identical repairable system is analysed stochastically and its reliability characteristics such as transition probabilities (TP), mean sojourn time (MST), MTSF, steady state availability, expected number of repairs of units, expected number of visits of the repairman and expected busy period of repairman are derived to carry out profit analysis.Research limitations/implicationsThe direct application of the present study can be seen is case of a power distribution system where solar panels may be considered as type-II units and transformer as the type-I unit. Here, the case study is carried out by using approximate data from some old studies; however, a study with accurate data would be more preferable.Originality/valueThis paper is one of the few reliability studies conducted on a system with simultaneous working units. Also, the comparative study of the profit of the present system model has been done with that of the model Kadyan et al. (2020a).

Cumulative Residual Tsallis Entropy-Based Test of Uniformity and Some New Findings

The Tsallis entropy is an extension of the Shannon entropy and is used extensively in physics. The cumulative residual Tsallis entropy, which is a generalization of the Tsallis entropy, plays an important role in the measurement uncertainty of random variables and has simple relationships with other important information and reliability measures. In this paper, some novel properties of the cumulative residual Tsallis entropy are disclosed. Moreover, this entropy measure is applied to testing the uniformity, where the limit distribution and an approximation of the distribution of the test statistic are derived. In addition, the property of stability is discussed. Furthermore, the percentage points and power against seven alternative distributions of this test statistic are presented. Finally, to compare the power of the suggested test with that of other tests of uniformity, a simulation study is conducted.

Reliability Importance Measures considering Performance and Costs of Mechanical Hydraulic System for Hydraulic Excavators

Although some reliability importance measures and maintenance policies for mechanical products exist in literature, they are rarely investigated with reference to weakest component identification in the design stage and preventive maintenance interval during the life cycle. This paper is mainly study reliability importance measures considering performance and costs (RIMPC) of maintenance and downtime of the mechanical hydraulic system (MHS) for hydraulic excavators (HE) with energy regeneration and recovery system (ERRS) and suggests the scheduled maintenance interval for key components and the system itself based on the reliability R i t . In the research, the required failure data for reliability analysis is collected from maintenance crews and users over three years of a certain type of hydraulic excavators. Minitab is used for probable distribution estimation of the mechanical hydraulic system failure times, and the model is verified to obey Weibull distribution. RIMPC is calculated by multiplying the reliability R i t and weighting factor W i and then compared with other classical importance measures. The purpose of this paper is to identify the weakest component for MHS in the design stage and to make appropriate maintenance strategies which help to maintain a high reliability level for MHS. The proposed method also provides the scientific maintenance suggestion for improving the MHS reliability of the HE reasonably, which is efficient, profitable, and organized.

A new integrated strategy for optimizing the maintenance cost of Production systems using reliability importance measures

This paper presents an opportunistic condition-based maintenance strategy for multi-component systems. This strategy allows the selection of systems’ components to be replaced or to undergo maintenance repairs based on the effect of importance measure concepts (IMC). IMC models proceeds by determining the contribution of each component in terms of success or failure degrees respecting two factors: (1) the probability of components to remain in operations under various conditions, and (2) the location of such components in the system structure. However, evaluating IMC factors is not so easy for complex systems; it requires knowing their operational structure, their reliability, and the calculation of the IMC degree and ranking them for the selection. Then, the IMC results are integrated into an opportunistic maintenance strategy to constitute a maintenance system's framework. In addition, a multicomponent system composed of 14 nodes and 23 links is provided to illustrate the proposed maintenance strategy that incurs a maximum of cost saving and to reduce spare parts unnecessarily usage.

Resilience Assessment: A Performance-Based Importance Measure

The resilience of a system can be considered as a function of its reliability and recoverability. Hence, for effective resilience management, the reliability and recoverability of all components which build up the system need to be identified. After that, their importance should be identified using an appropriate model for future resource allocation. The critical infrastructures are under dynamic stress due to operational conditions. Such stress can significantly affect the recoverability and reliability of a system’s components, the system configuration, and consequently, the importance of components. Hence, their effect on the developed importance measure needs to be identified and then quantified appropriately. The dynamic operational condition can be modeled using the risk factors. However, in most of the available importance measures, the effect of risk factors has not been addressed properly. In this paper, a reliability importance measure has been used to determine the critical components considering the effect of risk factors. The application of the model has been shown through a case study.

STOCHASTIC MODELING AND SENSITIVITY ANALYSIS OF AN INTEGRATED HARDWARE-SOFTWARE SYSTEM SUBJECT TO DIFFERENT WEATHER CONDITIONS

The current study covenants with stochastic investigation of an integrated hardware-software system considering hardware failure, software up-gradation upon failure, precautionary maintenance (PM) after a pre-determined process time, maximum repair time of hardware and different weather conditions. All time dependent indiscriminate variables are arbitrarily dispersed. Some important reliability measures like MTSF, availability and revenue of the system are obtained by using well established techniques semi-Markov process. Regenerative point technique has also been taken into consideration during model development. Sensitivity analysis of these measures is also performed. Finally, empirical analysis is done to demonstrate the results for a specific case. To climax the significance of the study, graphs of MTSF, availability, profit and sensitivity are also depicted.

An importance measure of a CNC lathe considering failure correlations

AbstractFailure correlation is a physical phenomenon observed, for example, in CNC (computer numerical control) lathe applications. Reliability importance measure considering failure correlation is an effective measure to identify the importance of CNC lathe. This paper fully considers the failure correlation among the subsystems of the CNC lathe. Based on the Copula function, the joint reliability model of each subsystem of the CNC lathe is established. Three methods (D‐test, the average cumulative error, and the root mean square error) are used to compare the joint reliability models under different Copula functions. Then ascertained the Clayton Copula model can describe the failure correlation reliability of CNC lathe most accurately. The reliability dynamic importance measure can judge the difference in CNC lathe subsystem importance measure more accurately. In this paper, the reliability dynamic core importance measure is proposed to analyze the potential of the CNC lathe subsystems for improvement. It can determine that the servo subsystem is the crucial part of the CNC lathe for priority improvement.

Cost Benefit Analysis of Three Non-Identical Machine Model with Priority in Operation and Repair

The paper proposes a new real life model and the main aim is to examine the cost benefit analysis of Textile Industry model subject to different failure and repair strategies. The reliability model comprises of three units i,e Spinning machine (S), Weaving machine (W), Colouring and Finishing machine(Cf). The working principal of the model starts with spinning machine (S) where in unit S is in operative state while as weaving machine, Colouring and Finishing machine are in ideal state. Complete failure of system is observed when all three units of system i.e. S,W and Cf are in down state. Repairperson is always available to carry out the repair activities in the system in which first priority in repair is given to Colouring and Finishing machine followed by Spinning and weaving machine. The proposed model attempts to maximize the reliability of a real life system. Reliability measures such as Mean Sojourn time, Mean time to system failure, Profit analysis of system are examined to define the performance of the reliability characteristics. For concluding the study of such model, different stochastic measures are analyzed in steady state using regenerative point technique. The tables are prepared for arbitrary values of the parameters to show the performance of some important reliability measures and to check the efficiency of the model under such situations.

An Expository Note on Unit - Gompertz Distribution with Applications

In a recent paper, Mazucheli et al. (2019) introduced the unit-Gompertz (UG) distribution and studied some of its properties. It is a continuous distribution with bounded support, and hence may be useful for modelling life-time phenomena. We present counter-examples to point out some subtle errors in their work, and subsequently correct them. We also look at some other interesting properties of this new distribution. Further, we also study some important reliability measures and consider some stochastic orderings associated with this new distribution.

Stochastic Analysis of a Three-Unit Non-Identical Repairable System with Simultaneous Working of Cold Standby Units

Here, stochastic analysis of a repairable system of three units has been carried out by taking one unit in operation (called main unit) and two identical units (called duplicate units) in cold standby. The switch device is used to convert the standby units into operative mode. A single server is hired to handle repair activities of the units who visits the system instantly whenever needed. The repair done by the server is perfect and thus the repaired unit follows the same lifetime distribution as the original. The constant failure rates are considered for both main and the duplicate units while their repair time distributions are taken as arbitrary. Some important reliability measures including mean sojourn times (MST), transition probabilities (TP), mean time to system failure (MTSF), availability, expected number of repairs for both kinds of units separately, expected number of visits by the server and busy period analysis of the server due to repair are determined using semi-Markov process (SMP) and regenerative point technique (RPT). The arbitrary values of the parameters are considered to examine the behaviour of some significant reliability measures through graphs. The possible application of the system model can be visualized in a power supply system of a house where a set of solar panels are kept in spare for their simultaneously working when main power supply is discontinued.

Reliability and importance measures for a general consecutive-type system

ABSTRACT A general consecutive-type system, called -consecutive-,-out-of-:F system, is considered. Such a system consists of linearly or circularly ordered components and fails if and only if there are at least -overlapping runs of consecutive failed components. For systems with statistically independent components, the system reliability is obtained via recursive schemes and simulation. Reliability importance measures for system components are calculated and analyzed as well. Illustrative analytic and numerical examples demonstrate further the theoretical results.

Reliability Analysis and Imprecise Component Importance Measure of Redundant Systems of OWTs Based on Component Swapping

Due to the high cost of failures of wind turbines, redundancy designs are commonly applied in wind turbines for improving the reliability and availability of systems. For this reason, replacing failed components with other working components of the same type in redundant systems is becoming an attractive option of maintenance strategies towards more resilient systems. To quantitatively evaluate system’s reliability, this paper focuses on the reliability analysis of redundant systems of offshore wind turbines based on swapping existing components. The survival signature-based component swapping method is introduced to describe the new structure-function of the system upon swapping. Furthermore, the reliability model of redundant systems is established using the fault tree and survival signature. Following this, the influences of component swapping on component reliability importance measure (marginal reliability importance and joint reliability importance) without and with considerations of the imprecision of failure rates are explored. Finally, a 5MW offshore wind turbine is presented to show the applicability of the proposed approach for redundant systems, and the results show that the proposed approach can obtain realistic reliability assessment of redundant systems and considering component swapping can significantly improve system reliability.

Reliability Importance Measures for Network Based on Failure Counting Process

Traditional importance measures seldom consider how the number of failed components influences the network reliability. This paper proposes two importance measures under the circumstance that the failure sequence of the components follows a counting process. The first importance measure aims to assess the contribution of the individual component (edge) to the network failure. The second evaluates the contribution of the individual component to the network functionality. Both importance measures are time-dependent functions, and their values are jointly determined by the network structure and the distribution of the number of failed components at a particular time. We prove that the proposed importance measures are able to generate consistent rankings based on edge's impact on the network reliability behavior. When networks possess special structure or the number of failed edges follows the special distribution, the rankings are coincident with the results generated from some traditional importance measures. When component's failure sequence follows a saturated nonhomogeneous Poisson process, the proposed importance measures are equivalent to the structural importance measure as time approaches zero or infinite. Finally, numerical examples are provided to demonstrate the application and performance of the proposed measures.

An Efficient Reliability Guaranteed Deployment Scheme for Service Function Chains

This paper encloses an in-depth investigation of a novel cost-efficient reliability-guaranteed deployment scheme for service function chains (SFCs) to tackle the drawbacks of the existing studies for the virtual network function (VNF) redundancy allocation. We model the VNF redundancy allocation as a cost minimization problem using the integer linear programming (ILP). For the VNF redundancy allocation, we first examine various reliability importance measures in the system reliability theory for the VNF environments. We then propose a novel cost-aware criticality-based priority index (CCI) to evaluate the priority level of VNFs in the SFC level and the network level for the VNF redundancy allocation. Based on CCI, we design a cost-efficient VNF redundancy allocation algorithm, namely, CCI-RA. Through our comprehensive simulation and analysis results, we show that the proposed scheme achieves a significant improvement in terms of the cost efficiency, scalability, and the percentage of the admitted services compared to the existing approaches.

System reliability and component importance when components can be swapped upon failure

AbstractResilience of systems to failures during functioning is of great practical importance. One of the strategies that might be considered to enhance reliability and resilience of a system is swapping components when a component fails, thus replacing it by another component from the system that is still functioning. This paper studies this scenario, particularly with the use of the survival signature to quantify system reliability, where it is assumed that such a swap of components requires these components to be of the same type. We examine the effect of swapping components on a reliability importance measure for the specific components, and we also consider the joint reliability importance of two components. Such swapping of components may be an attractive means toward more resilient systems and could be an alternative to adding more components to achieve redundancy of repair and replacement activities.