Unknown Failure Rates Research Articles

Robust event-triggered distributed fusion for multi-sensor systems with unknown failure rates

A robust event-triggered distributed fusion algorithm is investigated in this paper for multi-sensor systems with unknown failure rates. A detection technique based on standard Gaussian distributed filtering innovation is designed and applied to judge whether the measurement is failed. This filtering innovation can also be used to construct the event-triggered condition. Specifically, the event condition is not triggered if the innovation is below the lower event-triggered threshold and the measurement is regarded as the failure measurement if the innovation exceeds the higher threshold. In the above two cases, the sensor measurement data is not transferred to the local estimator; otherwise, it will be transferred. Then, the sequential fast covariance intersection (SFCI) fusion algorithm is used for local estimation fusion. Besides, to analyze the estimation performance, sufficient conditions are given to demonstrate the boundness of the local estimation and fusion estimation covariance. Finally, a simulation example is given to show the usefulness of the presented algorithm.

Modelling and condition-based control of a flexible and hybrid disassembly system with manual and autonomous workstations using reinforcement learning

Remanufacturing includes disassembly and reassembly of used products to save natural resources and reduce emissions. While assembly is widely understood in the field of operations management, disassembly is a rather new problem in production planning and control. The latter faces the challenge of high uncertainty of type, quantity and quality conditions of returned products, leading to high volatility in remanufacturing production systems. Traditionally, disassembly is a manual labor-intensive production step that, thanks to advances in robotics and artificial intelligence, starts to be automated with autonomous workstations. Due to the diverging material flow, the application of production systems with loosely linked stations is particularly suitable and, owing to the risk of condition induced operational failures, the rise of hybrid disassembly systems that combine manual and autonomous workstations can be expected. In contrast to traditional workstations, autonomous workstations can expand their capabilities but suffer from unknown failure rates. For such adverse conditions a condition-based control for hybrid disassembly systems, based on reinforcement learning, alongside a comprehensive modeling approach is presented in this work. The method is applied to a real-world production system. By comparison with a heuristic control approach, the potential of the RL approach can be proven simulatively using two different test cases.

Reliability Analysis of Cloud Service-Based Applications Through SRGM and NMSPN

Cloud service-based applications are subject to reliability critical problem, as the reliability of the application relies on both the failed states and the probabilities of the failures. Classically, reliability analysis approaches are lack of estimating unknown failure rate and non-exponentially distributed failure times. We propose a new framework for analyzing the reliability. The method is mainly decomposed in four successive steps: a non-Makovian stochastic Petri net (NMSPN) model which describes the failure behavior of underlying applications, a software reliability growth model (SRGM) which estimates the failure data of each basic service, a reachability graph which discoveries all the failure sequences, and a computation procedure which computes the occurrences of non-exponential failures. We assess and validate our method by conducting experiment on an actual application. The results demonstrate that the method is competitive compared to the existing approaches for reliability analysis, while providing a better reliability. This result is helpful to the managers in optimizing the overall quality of the cloud service-based application.

Exploiting information of fusion component tests for failure rate estimation: Divertor Inner Vertical Target component study case

Reliability and availability performance assessment are useful tools in the design and delivery of fusion components to check whether expected system behaviour is likely to be observed. These analyses rely on failure rates input data, which must be estimated for components lacking operational experience. The proposed analysis is meant to gain insight on new components Failure Rates exploiting the information conveyed by qualification tests. In particular, a model for the failure rate (Exponential and Weibull model considered) and an Arrhenius-exponential model for failure rate dependency on the stress level are assumed. A parametric expression is thus derived for considered component failure rate. A set of high flux tests data part of ITER Divertor Inner Vertical Target components qualification programme is then statistically analysed in terms of censored data set and a survival likelihood function accordingly derived. Posterior distributions for failure model unknown parameters are finally inferred to provide a first approximation failure rate estimation. Despite the problematic assumptions that the usage of such preliminary and not-tailored-on-purpose data requires, the proposed method quantitatively supports RAMI analysts in the estimation of unknown failure rates taking advantage of currently available information.

Reliability evaluation of the Chinese Train Control System Level 3 using a fuzzy approach

The Chinese Train Control System Level 3 (CTCS-3) has been developed to ensure the safe and efficient operation of high-speed railway networks in China. Failure of the CTCS-3 may result in serious accidents, causing damage to assets and risking the safety of passengers. In this paper, the reliability evaluation of the CTCS-3, comprising an onboard and trackside subsystem, is conducted using the fault tree analysis where the basic events are divided into known failure rate basic events and unknown failure rate basic events. The probabilities of the occurrence of the known basic events are obtained from field data, while those of the unknown failure rate basic events are estimated by expert judgment. The fuzzy set theory is then adopted to deal with the imprecise expert judgment. Considering a specific operational scenario, to accomplish one mission, the failure probability of the CTCS-3 is calculated. Importance analysis is carried out not only to identify the critical basic events but also to provide suggestions on the improvement of preventive maintenance. Sensitivity analysis shows that hardware factors have the most significant effect on the reliability of the CTCS-3.

Smooth Nonparametric Estimation of the Failure Rate Function and its First Two Derivatives

The class of nonparametric estimators of kernel type is considered for the unknown failure rate function and its derivatives. The convergence of the suggested estimations in distribution and in the mean square sense to the unknown failure rate function and its derivatives is proved. The interval estimator of the failure rate function is constructed. Advantages of the nonparametric estimators in comparison with the parametric algorithms are discussed. The suggested estimators of the failure rate function can be used to solve problems of exploitation reliability of complex physical, technical, and software systems under uncertainty conditions.

Bayesian inspection model for the production process subject to a random failure

Consider a sequence of items produced on a high-speed mass production line which is subject to a random failure. When an item in the sequence is inspected it is possible to obtain directional information about the exact timing of a process failure—before or after producing the inspected item. Using this directional information this paper proposes Bayesian inspection procedures that deal with three related problems: (i) how often to inspect items on the production line; (ii) how to conduct the search for more defective items; and (iii) when to stop the search process and salvage the remaining items. Based on various cost factors, the problem of optimal inspection interval, optimal search process and an optimal stopping rule is formulated as a profit-maximization model via a dynamic programming approach. For the production process with an unknown failure rate, Bayesian methods of estimating the process failure rate are proposed. The proposed Bayesian inspection procedures can be applied to a wide variety of high-speed mass production processes such as printing labels, filling containers or mixing ingredients.

Estimation procedures with delayed observations

The statistical model is considered in which the collection of data from several independent populations is available only at random times determined by order statistics of lifetimes of a given number of objects. Each of the populations is distributed according to a general multiparameter exponential family. The problem is to estimate the mean value vector parameter of the multiparameter exponential family of distributions of the forthcoming observations. Under the loss function involving a weighted squared error loss, the cost proportional to the events appeared and a cost of observing the process, a class of optimal sequential procedures is established. The procedures are derived in two situations: when the lifetime distribution is completely known and in the case when it is unknown but assumed to belong to an exponential subfamily with an unknown failure rate parameter.

Reliability prediction models to support conceptual design

During the early stages of conceptual design, the ability to predict reliability is very limited. Without a prototype to test in a lab environment or without field data, component failure rates and system reliability performance are usually unknown. A popular method for early reliability prediction is to develop a computer model for the system. However, most of these models are extremely specific to an individual system or industry. This paper presents three general procedures (using both simulation and analytic solution techniques) for predicting system reliability and average mission cost. The procedures consider both known and unknown failure rates and component-level and subsystem-level analyzes. The estimates are based on the number of series subsystems and redundant (active or stand-by) components for each subsystem. The result is a set of approaches that engineers can use to predict system reliability early in the system-design process. Software was developed (and is discussed in this paper) that facilitates the application of the simulation-based techniques. For the specific type of system and mission addressed in this paper, the analytic approach is superior to the simulation-based prediction models. However, all three approaches are presented for two reasons: (1) to convey the development process involved with building these prediction tools; and (2) the simulation-based approaches are of greater value as the research is extended to consider more complex systems and scenarios.

Approximate confidence intervals after a sequential clinical trial comparing two exponential survival curves with censoring

A sequential test is considered for comparing two exponential survival curves with unknown failure rates θ 1, θ 2 > 0. The survival times are censored by both real time and an independent censoring variable. It is shown how very weak expansions for the bivariate version of the signed root transformation may be used to construct an approximate confidence interval for δ = log( θ 1 θ 2 ) following the test. The accuracy of the method is illustrated by simulation results for several sequential tests and data-dependent allocation rules.

A sequential test for the failure rate of an exponential distribution with censored data

This paper provides an optimal sequential decision procedure for deciding between two composite hypotheses about the unknown failure rate of an exponential distribution, using censored data. The procedure has two components, a stopping time and a decision function. The optimal stopping time minimizes the expected total loss due to a wrong decision plus cost of observing the process. The optimal decision function is easily characterized once a stopping time has been specified. The main result determines the continuation region for the optimal decision procedure

A Comparison of Several Component-Testing Plans For A Parallel System

We consider a parallel (1-out-of-n:G) system of n components with constant failure rates and treat three different classes of component testing procedures all of which guarantee that the given consumer and producer risks are not exceeded. It is necessary to impose certain restrictions on the magnitude of the unknown failure rates for guaranteeing the producer risk. The three classes of component test procedures use Type-I censoring and use decision rules based on: A) the total number of component failures during the testing periods, B) the number of failures for each individual component, and C) the maximum likelihood estimate of system reliability. Based on the requirement that both the consumer and producer risks lie within specified levels, class A plans exhibit lower testing costs in the selected numerical examples.

Bayes Analysis of Reliability for Complex Systems

We present a method for computing Bayes confidence limits for the reliability of an arbitrary series-parallel system consisting of failure independent modules. Each module has an internal structure of one or more failure independent devices in series, and each device consists of one operating component with identical nonoperating standby components. Each component is assumed to have exponential distribution of life with unknown failure rates that must be estimated from test data. We outline a systematic procedure to compute the posterior distribution of system reliability, from which exact confidence limits can be obtained. The posterior distribution function is developed in terms of an expansion in shifted Chebyshev polynomials of the second kind whose convergence properties and numerical evaluation are well suited for practical applications.

Approximate Confidence Limits for Complex Systems with Exponential Component Lives

The asymptotic distribution of the log-likelihood ratio is shown to provide a method of determining approximate confidence bounds for the reliability function of any coherent system when each component has an exponential life with unknown failure rate and component performance data are provided in the form: number of failures (minimum of one) and total operating time. Thus the method applies under all general types of censoring. This extends the results of the authors, Ann. Math. Statist. (1968), on confidence limits for coherent structures with binomial data on the component's reliability. Methods similar to those previously utilized are combined with some special properties of the exponential distribution to obtain the results.