Residual Lifetime Research Articles

Hhsmm: an R package for hidden hybrid Markov/semi-Markov models

This paper introduces the hhsmm R package, which involves functions for initializing, fitting, and predication of hidden hybrid Markov/semi-Markov models. These models are flexible models with both Markovian and semi-Markovian states, which are applied to situations where the model involves absorbing or macro-states. The left-to-right models and the models with series/parallel networks of states are two models with Markovian and semi-Markovian states. The hhsmm also includes Markov/semi-Markov switching regression model as well as the auto-regressive HHSMM, the nonparametric estimation of the emission distribution using penalized B-splines, prediction of future states and the residual useful lifetime estimation in the predict function. The commercial modular aero-propulsion system simulation (C-MAPSS) data-set is also included in the package, which is used for illustration of the application of the package features. The application of the hhsmm package to the analysis and prediction of the Spain’s energy demand is also presented.

The methodology for validation of cross sections in quasi monoenergetic neutron field

Dosimetry cross sections are fundamental quantities for proper determination of the neutron fluences in points of interest under heavy radiation load. One of the critical applications is the Reactor Pressure Vessel aging management, related to the correct estimation of its residual lifetime or for many non-reactor applications including neutron dosimetry of accelerator-based fields or space applications. The neutron flux of neutrons above 12 MeV in reference to fission spectra is below 1%. Therefore, new reference neutron fields with average energy above 5 MeV should be developed for the validation of neutron dosimetry cross sections up to 60 MeV. This paper presents the testing of a new methodology for the use of quasi monoenergetic neutron fields, where different sensitivity allows validations of the dosimetry cross section at energies much higher than the average energy of around 2 MeV typical of fission spectra. The exact shape of the neutron spectrum in the tested fields is measured by stilbene spectrometry. The total flux is determined from Ni and Al flux monitors. The developed methodology was applied to the validation of selected reactions from the IRDFF-II library showing satisfactory agreement.

Left‐truncated and right‐censored field failure data: Review of parametric analysis for reliability

AbstractIn field reliability analyses, a data collection period is given to monitor the failure events from the field. Left‐truncation arises due to early failures occurring before the data collection period, and right‐censoring arises for late failures occurring beyond the monitoring period. Naïve analyses of left‐truncated and right‐censored data lead to biased estimation of the population lifetime of interest. A variety of models and methods have been developed to analyze the left‐truncated and right‐censored data for field reliability analyses. The goal of the paper is to review the existing models and methods for fitting left‐truncated and right‐censored data. Our review includes the existing statistical models, such as the exponential, Weibull, lognormal, gamma, Gompertz, Lomax, and spline models. We comprehensively review the statistical issues of maximum likelihood estimation, model selection, residual lifetime prediction, and Bayesian methods. Some of these methods are illustrated through the field reliability analysis of the electric power transformer dataset.

Effect of Graphite on Residual Lifetime of Superheater Tubes Made of Steel 16Mo3 after Creep Exposure

Carbon and low-alloys steel components are used in various stages of steam power plants which are operating at elevated temperatures (up to 530°C). Long-term exposure of low-alloy steel components at elevated temperatures inevitably result in some kind of structural degradation; for example, creep cavitation, carbide coarsening and/or spheroidization, and, less commonly, also graphitization. Graphitization can be detected mainly in carbon steels, but also in 16Mo3 steel where the ferritic matrix is strengthened by Mo in the solid solution. The absence of strong carbide forming elements (typically Cr, V, Nb) is the principal reason why these steels are susceptible to cementite decomposition and graphitization much more than the other creep resistant steels. The paper describes microstructure and material properties of superheater 16Mo3 tube, in which graphite was found after 70,000 hours of exposure at 445 °C. The actual material properties were compared to another tube of the same material delivered in the as-received state. The comparison was based on evaluation and testing of mechanical strength, fracture properties and microstructure. Creep resistance of the steel was studied using small punch creep tests (SPCT).

Distributed energy-efficient clustering routing protocol for wireless sensor networks using affinity propagation and fuzzy logic

Organizing nodes into clusters and forwarding data to the base station (BS) in clustering routing protocols have been widely utilized to improve the energy efficiency, scalability and stability of wireless sensor networks. Making decisions on how many clusters are formed, which nodes are selected as cluster heads (CHs) and who become the relay nodes significantly impact the network performance. Therefore, a distributed clustering routing protocol combined affinity propagation (AP) with fuzzy logic called DAPFL is proposed in this paper, which considers not only energy efficiency but also energy balance to extend the network lifetime. In DAPFL, AP is firstly used to determine the number of clusters and select the best CHs simultaneously based on residual energy, distance between nodes. Then, the optimal next-hop CHs are chosen by using fuzzy logic system with residual energy, data length and distance to BS as descriptors. Simulations in different scenarios are carried out to verify the effectiveness of DAPFL, and the results show that DAPFL exhibits the promising performance in terms of network energy consumption, standard deviation of residual energy, network throughput and lifetime, compared with the up-to-date distributed clustering routing protocols EEFUC, EEFRP, LEACH-AP and APSA.

On conditional residual lifetimes of coherent systems consisting of components with discrete lifetimes

On conditional residual lifetimes of coherent systems consisting of components with discrete lifetimes

Varentropy of Past Lifetimes

In a variety of applicative fields the level of information in random quantities is commonly measured by means of the Shannon Entropy. In particular, in reliability theory and survival analysis, time-dependent generalizations of this measure of uncertainty have been considered to dynamically describe changes in the degree of information over time. The Residual Entropy and the Residual Varentropy, for example, have been considered in the specialized literature to measure the information and its variability in residual lifetimes. In a similar way, one can consider dynamic measures of information for past lifetimes, i.e., for random lifetimes of items when one assumes that their failures occur before a fixed inspection time. This paper provides a study of the Past Varentropy, defined as the dynamic measure of variability of information for past lifetimes. From this study emerges the interest on a particular family of lifetimes distributions, whose members satisfy the property to be the only ones having constant Past Varentropy.

Green Communication in Internet of Things: A Hybrid Bio-Inspired Intelligent Approach.

Clustering is a promising technique for optimizing energy consumption in sensor-enabled Internet of Things (IoT) networks. Uneven distribution of cluster heads (CHs) across the network, repeatedly choosing the same IoT nodes as CHs and identifying cluster heads in the communication range of other CHs are the major problems leading to higher energy consumption in IoT networks. In this paper, using fuzzy logic, bio-inspired chicken swarm optimization (CSO) and a genetic algorithm, an optimal cluster formation is presented as a Hybrid Intelligent Optimization Algorithm (HIOA) to minimize overall energy consumption in an IoT network. In HIOA, the key idea for formation of IoT nodes as clusters depends on finding chromosomes having a minimum value fitness function with relevant network parameters. The fitness function includes minimization of inter- and intra-cluster distance to reduce the interface and minimum energy consumption over communication per round. The hierarchical order classification of CSO utilizes the crossover and mutation operation of the genetic approach to increase the population diversity that ultimately solves the uneven distribution of CHs and turnout to be balanced network load. The proposed HIOA algorithm is simulated over MATLAB2019A and its performance over CSO parameters is analyzed, and it is found that the best fitness value of the proposed algorithm HIOA is obtained though setting up the parameters, number of rooster, number of hen’s and swarm updating frequency . Further, comparative results proved that HIOA is more effective than traditional bio-inspired algorithms in terms of node death percentage, average residual energy and network lifetime by 12%, 19% and 23%.

An Advanced System for the Visualisation and Prediction of Equipment Ageing

The control of major hazards involving dangerous substances in the chemical and process industry requires verifying equipment ageing according to the current legislation. This means to monitor its real conditions regarding degradation mechanisms and forecast their evolution over time. A system, named Virtual Sensor, supports this activity that is usually conducted during on-field inspections (safety walks). It is designed to collect ageing-related information, process data through some models, and produce prognostic estimates regarding the corrosion rate, the probability of the critical pit, the corrosion evolution on the equipment surface, and the residual lifetime, visualising the results in Augmented Reality (AR). An atmospheric storage tank of diesel oil was chosen as the case study; its 3D model was realised, and a miniature model was reproduced in the laboratory. Through the Virtual Sensor, data of past inspections were acquired. The application successfully managed and elaborated these data, showing the outputs in AR during a safety walk.

Comparing residual lifetimes and inactivity times of dependent random variables

In this paper, sufficient conditions are presented for comparing the residual lifetimes and inactivity times of dependent random variables at different fixed times, with respect to hazard rate, mean residual life and likelihood ratio orders. The stochastic comparisons of residual lifetimes at different random times are discussed next. Several examples are presented to illustrate the established results.

On Odd Perks-G Class of Distributions: Properties, Regression Model, Discretization, Bayesian and Non-Bayesian Estimation, and Applications

In this paper, we present a new univariate flexible generator of distributions, namely, the odd Perks-G class. Some special models in this class are introduced. The quantile function (QFUN), ordinary and incomplete moments (MOMs), generating function (GFUN), moments of residual and reversed residual lifetimes (RLT), and four different types of entropy are all structural aspects of the proposed family that hold for any baseline model. Maximum likelihood (ML) and maximum product spacing (MPS) estimates of the model parameters are given. Bayesian estimates of the model parameters are obtained. We also present a novel log-location-scale regression model based on the odd Perks–Weibull distribution. Due to the significance of the odd Perks-G family and the survival discretization method, both are used to introduce the discrete odd Perks-G family, a novel discrete distribution class. Real-world data sets are used to emphasize the importance and applicability of the proposed models.

Comment on “Modeling Purchasing Behavior with Sudden ‘Death’: A Flexible Customer Lifetime Model”

In their 2012 paper, Bemmaor and Glady introduced the gamma/Gompertz/negative binomial distribution model for customer base analysis. Their model uses exponentially distributed interpurchase times and a Gompertz distributed customer lifetime, where the latter distribution is nonmemoryless. This comment corrects an error in their expression for the conditional expected number of individual future purchases [Formula: see text] in a forecasting interval of length [Formula: see text]. Contrary to their approach, the correct derivation of the conditional expectation must be based on the conditional survival and density functions of the lifetime distribution. Using the wrong formula leads managers to overestimate the expected future customer purchases. Further, the comment corrects the erroneous expressions for the conditional variance [Formula: see text] and the conditional mean residual lifetime [Formula: see text]. This paper was accepted by Raphael Thomadsen, marketing. Supplemental Material: The e-companion is available at https://doi.org/10.1287/mnsc.2022.4422 .

Fast-converging chain-cluster-based routing protocols using the Red-Deer Algorithm in Wireless Sensor Networks

PurposeThe performance of Wireless Sensor Networks (WSNs) applications is bounded by the limited resources of battery-enabled Sensor Nodes (SNs), which include energy and computational power; the combination of which existing research seldom focuses on. Although bio-inspired algorithms provide a way to control energy usage by finding optimal routing paths, those which converge slower require even more computational power, which altogether degrades the overall lifetime of SNs.Design/methodology/approachHence, two novel routing protocols are proposed using the Red-Deer Algorithm (RDA) in a WSN scenario, namely Horizontal PEG-RDA Equal Clustering and Horizontal PEG-RDA Unequal Clustering, to address the limited computational power of SNs. Clustering, data aggregation and multi-hop transmission are also integrated to improve energy usage. Unequal clustering is applied in the second protocol to mitigate the hotspot problem in Horizontal PEG-RDA Equal Clustering.FindingsComparisons with the well-founded Ant Colony Optimisation (ACO) algorithm reveal that RDA converges faster by 85 and 80% on average when the network size and node density are varied, respectively. Furthermore, 33% fewer packets are lost using the unequal clustering approach which also makes the network resilient to node failures. Improvements in terms of residual energy and overall network lifetime are also observed.Originality/valueProposal of a bio-inspired algorithm, namely the RDA to find optimal routing paths in WSN and to enhance convergence rate and execution time against the well-established ACO algorithm. Creation of a novel chain cluster-based routing protocol using RDA, named Horizontal PEG-RDA Equal Clustering. Design of an unequal clustering equivalent of the proposed Horizontal PEG-RDA Equal Clustering protocol to tackle the hotspot problem, which enhances residual energy and overall network lifetime, as well as minimises packet loss.

Late relapses in Hodgkin lymphoma - should we search for the needle in the haystack?

Hodgkin lymphoma is among the most curable cancers. For patients in remission for 24 months, residual lifetime becomes close to that of the background population. However, late relapses can occur after several years and, as shown by Andersen et al., the outcomes are not always good.Commentary on: Andersen MD, Hamilton‐Dutoit S, Modvig L, Vase M, Christiansen I, Christensen JH, et al. Late recurrence of lymphoid malignancies after initial treatment for Hodgkin lymphoma – A study from the Danish Lymphoma Registry. Br J Haematol 2022;198:50‐61.

A probabilistic approach for the estimation of the residual useful lifetime of atmospheric storage tanks in oil industry

Based on the prediction of the equipment residual useful life, important decisions are made in oil industry to ensure a safe and profitable management. Atmospheric storage tanks are particularly critical from the safety point of view as their bottom is affected by localised corrosion (pitting), which is not easy to be monitored. The prediction of the useful lifetime defines the time up to which the equipment can continue to be in-service before the formation of holes where the greatest thinning is observed. In this study, the thickness data collected in subsequent inspections of the bottom of twenty-three large storage tanks of petroleum products has been processed by adopting an improved probabilistic approach. The method is unconventional and combines the consolidated extreme value theory and Bayes’ formula to quantify the probability of thinning below a fixed limit and, thus, predict the remaining useful lifetime, as well as the optimal time for the next full inspection. Data collected allowed the validation of the forecast model.

A Cross-Layer Design and Fuzzy Logic based Stability Oriented Routing Protocol

Ad-hoc networks in which nodes are mobile as well as communicate via wireless links fall under the category of mobile ad-hoc networks (MANETs). Evasive mobility and the limited battery life of MANET nodes make routing a difficult problem. Most of the conventional routing protocols recommend the shortest path without considering route stability into account. A Cross-Layer Design and Fuzzy Logic based Stability Oriented (CLDFL-SO) routing protocol is proposed in this research, which offers a solution for stable route formation by eliminating unstable links and low-quality nodes. Cross-layer interaction parameter based link residual lifetime calculation is used to assess the link's stability. The fuzzy logic is being used to evaluate the node quality by providing node metrics like node speed, residual energy and node degree. The simulations illustrate the efficacy of the suggested protocol in comparison to the popular Ad-hoc On-Demand Distance Vector (AODV) protocol.

An approach to estimate crack opening displacement in two-dimensional plain-woven silicon carbide fiber-reinforced silicon carbide composite considering different matrix cracking modes

Understanding of cracking opening behavior is important for the prediction of residual strength and lifetime of silicon carbide fiber-reinforced silicon carbide ceramic–matrix composites at elevated temperatures. Under tensile loading below the proportional limit stress, multiple matrix cracking modes in the longitudinal and transverse yarns of a two-dimensional plain-woven silicon carbide fiber-reinforced silicon carbide composite appear in the matrix with debonding at the fiber/matrix interface. Crack opening displacement is a key parameter to characterize the crack opening behavior in silicon carbide fiber-reinforced silicon carbide composites and is affected by the multiple matrix cracking modes. In this article, a damage-based micromechanical crack opening displacement model for two-dimensional plain-woven silicon carbide fiber-reinforced silicon carbide composites is developed considering different matrix cracking modes. Two typical matrix cracking modes in a two-dimensional plain-woven silicon carbide fiber-reinforced silicon carbide composite, that is, cracking mode III with cracking in the matrix of the longitudinal and transverse yarns and cracking mode V with cracking only in the matrix of the longitudinal yarns, are considered. Micro stress field in the fiber, matrix of the longitudinal yarns, and transverse yarns are obtained. The crack opening displacement and related damage parameters are determined. Relationships between composite's crack opening displacement, constitutive properties, woven parameters, and damage state are established. Experimental crack opening displacements of cracking modes III and V in a two-dimensional plain-woven melt-infiltration silicon carbide fiber-reinforced silicon carbide composite from the published literature are predicted. For cracking mode III with partial interface debonding, the crack opening displacement increased nonlinearly with applied stress; and for cracking mode V with complete interface debonding, the crack opening displacement increased linearly with applied stress.

Some new bounds for the mean value function of the residual lifetime process

In this study, mean residual lifetime (MRL) function of the residual lifetime process is investigated. Various two-sided bounds are obtained for this function by using the monotone convergence of the sequence of the functions. Since many important features of any stochastic process depend on classifications of distributions, improved bounds are found for MRL function by considering the taxonomy of the distributions. Moreover, some new bounds are achieved by using the asymptotic expression of MRL function and its boundary relation with the MRL of a single component at age t. Three examples are given to illustrate the results proposed.

A Combining Strategy of Energy Replenishment and Data Collection in Wireless Sensor Networks

In wireless rechargeable sensor networks, the mobile vehicle (MV) combining energy replenishment and data collection often collects all sensed data, which may lead to data overflow and larger delay. To avoid these defects, we propose a novel data collection policy, where the MV acts as an auxiliary mobile data collector to gather buffered data as much as possible and the base station receives and processes remaining sensed data. As the mobile vehicle acts the data collector and the mobile charger simultaneously, how to scheduling it to minimize energy consumption and improve data transmission performance without network lifetime degradation is a challenge. To address this, some algorithms are proposed under the new policy. First, according to the residual lifetime of nodes, a classic charging algorithm is utilized to dispatch the MV to charge nodes to maximize the network lifetime. After determining the trajectory of the MV, considering data type and buffer state, we divide data collection into two sub-problems: how to collect sensed data from neighbor clusters and roadside clusters respectively. To assign optimal data collection time and select best roadside clusters, some algorithms are proposed which can maximize the sum of normalized saved energy of neighbor clusters and roadside clusters. Finally, we evaluate the performance by simulations. The results show that our proposed algorithms can significantly improve the network performance, such as the delay, data overflow, energy consumption, network lifetime, and throughput.

Test for Decreasing Mean Residual Lifetimes Based on the Cumulative Residual Renyi’s Entropy

Abstract We introduce a new test for exponentiality against decreasing (increasing) mean residual life alternatives based on a cumulative residual Renyi’s entropy of order α. The exact and asymptotic distributions of the test statistic are given. The performance of the proposed test statistic is compared with other constructed tests in the literature using a simulation study. Finally, some numerical examples illustrating the theory are given.