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This article aims to develop procedures for estimation and prediction in case of Type-I hybrid censored samples drawn from a two-parameter generalized half-logistic distribution (GHLD).

The GHLD is a versatile model which is useful in lifetime modelling. Also, hybrid censoring is a time and cost-effective censoring scheme which is widely used in the literature. The authors derive the maximum likelihood estimates, the maximum product of spacing estimates and Bayes estimates with squared error loss function for the unknown parameters, reliability function and stress-strength reliability. The Bayesian estimation is performed under an informative prior set-up using the “importance sampling technique”. Afterwards, we discuss the Bayesian prediction problem under one and two-sample frameworks and obtain the predictive estimates and intervals with corresponding average interval lengths. Applications of the developed theory are illustrated with the help of two real data sets.

The performances of these estimates and prediction methods are examined under Type-I hybrid censoring scheme with different combinations of sample sizes and time points using Monte Carlo simulation techniques. The simulation results show that the developed estimates are quite satisfactory. Bayes estimates and predictive intervals estimate the reliability characteristics efficiently.

The proposed methodology may be used to estimate future observations when the available data are Type-I hybrid censored. This study would help in estimating and predicting the mission time as well as stress-strength reliability when the data are censored.

This paper reviews the literature on opportunistic maintenance (OM) as new advance maintenance approach and policy. The purpose of this paper is to conceptually identify common principle and thereby provide absolute definition, concept and characteristics of this policy.

A conceptual analysis was conducted on various literatures to clarify a number of principle and concepts as a method for understanding information on OM. The analysis involves the process of separating the compound terms used in the literatures into a few parts, analyse them and then recombining them to have more clear understanding of the policy.

The paper discussed the maintenance approach, genealogy, principle, concept and applications of OM both in numerical analysis and real industry. OM policy is developed based on combination of age replacement policy and block replacement policy and in practical; OM is applied as the combination of corrective maintenance which is applied when any failure occurred, with preventive maintenance (PM) – a planned and scheduled maintenance approach to prevent failure to happen. Any machine shutdown or stoppages due to failure is the “opportunity” to conduct PM even though it is not as planned. The characterization of OM was provided in order to present its theoretical novelty for researchers and practical significance for industries.

To date, there is no publication that reviews the OM in-depth and provides clear understanding on the topic. Therefore, this paper aims to show lineage of OM and the current trend in researches. This discussion will pave the way of new research areas on this optimal maintenance policy. Clear definition and principle of OM provided in this paper will trigger interest in its practicality as well as aid industries to understand and conduct OM in operation plant.

This paper discussed the available literature about OM in various perspectives and scopes for further understanding of the topic by maintenance management professionals and researchers. Therefore, OM can be widely studied and applied in real industry as it is an effective and optimal maintenance policy.

Wireless sensor networks are expected to be an integral part of any pervasive computing environment. This implies an ever‐increasing need for efficient energy and resource management of both the sensor nodes, as well as the overall sensor network, in order to meet the expected quality of data and service requirements. There have been numerous studies that have looked at the routing of data in sensor networks with the sole intention of reducing communication power consumption. However, there has been comparatively little prior art in the area of multi‐criteria based routing that exploit both the semantics of queries and the state of sensor nodes to improve network service longevity. In this paper, we look at routing in sensor networks from this perspective and propose an adaptive multi‐criteria routing protocol. Our algorithm offers automated reconfiguration of the routing tree as demanded by variations in the network state to meet application service requirements. Our experimental results show that our approach consistently outperforms, in terms of Network Lifetime and Coverage, the leading semantic‐based routing algorithm which reconfigures the routing tree at fixed periods.

The purpose of this paper is to show how the fabrication parameters of screen‐printed thick‐film reference electrodes have been experimentally varied and their effect on device characteristics investigated.

The tested devices were fabricated as screen‐printed planar structures consisting of a silver back contact, a silver/silver chloride interfacial layer and a final salt reservoir layer containing potassium chloride. The fabrication parameters varied included deposition method and thickness, salt concentration and binder type used for the final salt reservoir layer. Characterisation was achieved by monitoring the electrode potentials as a function of time following initial immersion in test fluids in order to ascertain initial hydration times, subsequent electrode drift rates and useful lifetime of the electrodes. Additionally, the effect of fabrication parameter variation on electrode stability and their response time in various test media was also investigated.

Results indicate that, although a trade‐off exists between hydration times and drift rate that is dependent on device thickness, the initial salt concentration levels and binder type also have a significant bearing on the practical useful lifetime. Generally speaking, thicker devices take longer to hydrate but have longer useful lifetimes in a given range of chloride environments. However, the electrode stability and response time is also influenced by the type of binder material employed for the final salt reservoir layer.

The reported results help to explain better the behaviour of thick‐film reference electrodes and contribute towards the optimisation of their design and fabrication for use in solid‐state chemical sensors.

Capacitors are one of the most common passive components on a circuit board. From a tiny toy to substantial satellite, a capacitor plays an important role. Untimely failure of a capacitor can destruct the entire system. This research paper targets the reliability assessment of tantalum capacitor, to reduce e-waste and enhance its reusable capability.

The residual lifetime of a tantalum capacitor is estimated using the empirical method, i.e. military handbook MILHDBK2017F, and validated using an experimental approach, i.e. accelerated life testing (ALT). The various influencing acceleration factors are explored, and experiments are designed using Taguchi's approach. Empirical methods such as the military handbook is used for assessing the reliability of a tantalum capacitor, for ground and mobile applications.

After exploring the lifetime of a tantalum capacitor using empirical and experimental techniques, an error analysis is conducted, which shows the validity of empirical technique, with an accuracy of 95.21%.

The condition monitoring and health prognostics of tantalum capacitors, for ground and mobile applications, are explored using empirical and experimental techniques, which warns the user about its residual lifetime so that the faulty component can be replaced in time.

Although Multi-Layer Ceramic Capacitors (MLCC) are known for its better frequency performance and voltage handling capacity, but under various environmental conditions, its reliability becomes a challenging issue. In modern era of integration, the failure of one component can degrade or shutdown the whole electronic device. The lifetime estimation of MLCC can enhance the reuse capability and furthermore, reduces the e-waste, which is a global issue.

The residual lifetime of MLCC is estimated using empirical method i.e. Military handbook MILHDBK2017F, statistical method i.e. regression analysis using Minitab18.1 as well as intelligent technique i.e. artificial neural networks (ANN) using MATLAB2017b. ANN Feed-Forward Back-Propagation learning with sigmoid transfer function [3–10–1–1] is considered using 73% of available data for training and 27% for testing and validation. The design of experiments is framed using Taguchi’s approach L16 orthogonal array.

After exploring the lifetime of MLCC, using empirical, statistical and intelligent techniques, an error analysis is conducted, which shows that regression analysis has 97.05% accuracy and ANN has 94.07% accuracy.

An intelligent method is presented for condition monitoring and health prognostics of MLCC, which warns the user about its residual lifetime so that faulty component can be replaced in time.

This paper aims to establish a more accurate model for lifetime estimation.

Finite element model simulation and experimental tests are used to enhance the lifetime prediction model of the solder joint.

A more precise model was found.

It is confirmed that the paper is original.

The purpose of this study is to investigate the effect of aging temperature on the barrel-type solder joint lifetime of electronic devices and to include these effects in the modified prediction model.

Several accelerated shear stress tests under different stress amplitudes and aging temperatures were performed.

It was found that by aging temperature increasing, the lifetime decreases. Morrow energy model was also found as the best prediction model when the aging temperature is taken into consideration.

It is confirmed.

This paper aims to present the novel stacked machine learning approach (SMLA) to estimate low-cycle fatigue (LCF) life of SAC305 solder across structural parts. Using the finite element simulation (FEM) and continuous damage mechanics (CDM) model, a fatigue life database is built. The stacked machine learning (ML) model's iterative optimization during training enables precise fatigue predictions (2.41% root mean square error [RMSE], R² = 0.975) for diverse structural components. Outliers are found in regression analysis, indicating potential overestimation for thickness transition specimens with extended lifetimes and underestimation for open-hole specimens. Correlations between fatigue life, stress factors, nominal stress and temperature are unveiled, enriching comprehension of LCF, thus enhancing solder behavior predictions.

This paper introduces stacked ML as a novel approach for estimating LCF life of SAC305 solder in various structural parts. It builds a fatigue life database using FEM and CDM model. The stacked ML model iteratively optimizes its structure, yielding accurate fatigue predictions (2.41% RMSE, R² = 0.975). Outliers are observed: overestimation for thickness transition specimens and underestimation for open-hole ones. Correlations between fatigue life, stress factors, nominal stress and temperature enhance predictions, deepening understanding of solder behavior.

The findings of this paper highlight the successful application of the SMLA in accurately estimating the LCF life of SAC305 solder across diverse structural components. The stacked ML model, trained iteratively, demonstrates its effectiveness by producing precise fatigue lifetime predictions with a RMSE of 2.41% and an “R²” value of 0.975. The study also identifies distinct outlier behaviors associated with different structural parts: overestimations for thickness transition specimens with extended fatigue lifetimes and underestimations for open-hole specimens. The research further establishes correlations between fatigue life, stress concentration factors, nominal stress and temperature, enriching the understanding of solder behavior prediction.

The authors confirm the originality of this paper.

This study aims to present a more accurate lifetime prediction model considering solder chemical composition.

Thermal cycling and standard creep tests as well as finite element simulation were used.

The study found lower error in the solder joint lifetime evaluation. The higher the Ag content is, the higher the lifetime is achieved.

It is confirmed.