The lifetime analysis of the Weibull model based on Generalized Type-I progressive hybrid censoring schemes

<abstract><p>In this study, we estimate the unknown parameters, reliability, and hazard functions using a generalized Type-I progressive hybrid censoring sample from a Weibull distribution. Maximum likelihood (ML) and Bayesian estimates are calculated using a choice of prior distributions and loss functions, including squared error, general entropy, and LINEX. Unobserved failure point and interval Bayesian predictions, as well as a future progressive censored sample, are also developed. Finally, we run some simulation tests for the Bayesian approach and numerical example on real data sets using the MCMC algorithm.</p></abstract>

Fatigue mitigation of wind turbine system using multiple point model predictive control

<span lang="EN-US">A multi-point model predictive control (MPMPC) is widely used for many applications, including wind energy system (WES), notably enhanced power characteristics and oscillation regulation. In this work, MPMPC is adapted to condense the fatigue load of the WES and improve the lifetime of the turbine assembly. The lifetime examination is carried out by considering the three chief parameters: basic lifetime until failure, short-time damage equivalent loads (DELs), and lifetime DELs. The simulation study is performed for two cases: blade root bending moments and tower top bending. Further, fatigue load examination is demonstrated to analyze the effectiveness of the proposed controller. The observed results show that the lifetime analysis of the wind turbine system displayed more excellent characteristics, i.e., 49.50% greater than MPC. Also, the fatigue load mitigation showed greater magnitude due to the control action of the proposed controller, about 37.38% grander than MPC. Therefore, the attained outcomes exhibit outstanding performance compared with conventional controllers.</span>

Microstructure-Based Lifetime Assessment of Austenitic Steel AISI 347 in View of Fatigue, Environmental Conditions and NDT

The assessment of metallic materials used in power plants’ piping represents a big challenge due to the thermal transients and the environmental conditions to which they are exposed. At present, a lack of information related to degradation mechanisms in structures and materials is covered by safety factors in its design, and in some cases, the replacement of components is prescribed after a determined period of time without knowledge of the true degree of degradation. In the collaborative project “Microstructure-based assessment of maximum service life of nuclear materials and components exposed to corrosion and fatigue (MibaLeb)”, a methodology for the assessment of materials’ degradation is being developed, which combines the use of NDT techniques for materials characterization, an optimized fatigue lifetime analysis using short time evaluation procedures (STEPs) and numerical simulations. In this investigation, the AISI 347 (X6CrNiNb18-10) is being analyzed at different conditions in order to validate the methodology. Besides microstructural analysis, tensile and fatigue tests, all to characterize the material, a pressurized hot water pipe exposed to a series of flow conditions will be evaluated in terms of full-scale testing as well as prognostic evaluation, where the latter will be based on the materials’ data generated, which should prognose changes in the material’s condition, specifically in a pre-cracked stage. This paper provides an overview of the program, while the more material’s related aspects are presented in the subsequent paper.

Analysis of Fluorescence Decay Kinetics of Indocyanine Green Monomers and Aggregates in Brain Tumor Model In Vivo

Spectroscopic approach with fluorescence time resolution allows one to determine the state of a brain tumor and its microenvironment via changes in the fluorescent dye’s fluorescence lifetime. Indocyanine green (ICG) is an acknowledged infra-red fluorescent dye that self-assembles into stable aggregate forms (ICG NPs). ICG NPs aggregates have a tendency to accumulate in the tumor with a maximum accumulation at 24 h after systemic administration, enabling extended intraoperative diagnostic. Fluorescence lifetime analysis of ICG and ICG NPs demonstrates different values for ICG monomers and H-aggregates, indicating promising suitability for fluorescent diagnostics of brain tumors due to their affinity to tumor cells and stability in biological tissue.

Network Lifetime Analysis in IOT Environment in Healthcare Sectors Using Deep Learning Routing Approach

The Internet of Things (IoT) provides an improved flexibility in data collection, network deployment and data transmission to the sink nodes. However, depending on the application, the IoT network tends to consume lot of power from the individual devices. Various conventional solutions are provided to reduce the consumption of energy but most methods focus on increasing the data acquisition speed, data transmission and routing capabilities. However, these methods tend to fall under the trade-off between these three factors. Hence, in order to maintain the trade-off between these constraints, a viable solution is developed in this paper. A deep learning-based routing is built considering the faster acquisition of data, faster data transmission and routing path estimation with increasing path estimation. The paper models a Deep belief Network (DBN) to route the data considering all these constraints. The experimental validation is conducted to check the network lifetime, energy consumption of IoT nodes. The results show that the DBN offers greater source of flexibility with increased data routing capabilities than other methods.

Defect concentration and Δn change in light- and elevated temperature- induced degradation

The wide variety of silicon materials used by various groups to investigate LeTID make it difficult to directly compare the defect concentrations (Nt) using the typical normalised defect density (NDD) metric. Here, we propose a new formulation for a relative defect concentration (β) as a correction for NDD that allows flexibility to perform lifetime analysis at arbitrary injection levels (Δn), away from the required ratio between Δn and the background doping density (Ndop) for NDD of Δn/N dop = 0.1. As such, β allows for a meaningful comparison of the maximum degradation extent between different samples in different studies and also gives a more accurate representative value to estimate the defect concentration. It also allows an extraction at the cross-over point in the undesirable presence of iron, or flexibility to reduce the impact of modulation in surface passivation. Although the accurate determination of β at a given Δn requires knowledge of the capture cross-section ratio (k), the injection-independent property of the β formulation allows a self-consistent determination of k. Experimental verification is also demonstrated for boron-oxygen related defects and LeTID defects, yielding k-values of 10.6 ± 3.2 and 30.7 ± 4.0, respectively, which are within the ranges reported in the literature. With this, when extracting the defect density at different Δn ranging between 1014 /cm3 to 1015 /cm3 with Ndop = 9.1 ×1015 /cm3, the error is less than 12% using β, allowing for a greatly improved understanding of the defect concentration in a material.

Thermal Cycling in Converter IGBT Modules with Different Cooling Systems in Pitch- and Active Stall-Controlled Tidal Turbines

This paper compares active and passive cooling systems in tidal turbine power electronic converters. The comparison is based on the lifetime of the IGBT (insulated gate bipolar transistor) power modules, calculated from the accumulated fatigue due to thermal cycling. The lifetime analysis accounts for the influence of site conditions, namely turbulence and surface waves. Results indicate that active cooling results in a significant improvement in IGBT lifetime over passive cooling. However, since passive cooling systems are inherently more reliable than active systems, passive systems can present a better solution overall, provided adequate lifetime values are achieved. On another note, the influence of pitch control and active speed stall control on the IGBT lifetime was also investigated. It is shown that the IGBT modules in pitch-controlled turbines are likely to have longer lifetimes than their counterparts in active stall-controlled turbines for the same power rating. Overall, it is demonstrated that passive cooling systems can provide adequate cooling in tidal turbine converters to last longer than the typical lifetime of tidal turbines (>25 years), both for pitch-controlled and active speed stall-controlled turbines.

Characterizing System-Level Masking Effects against Soft Errors

From early design phases to final release, the reliability of modern embedded systems against soft errors should be carefully considered. Several schemes have been proposed to protect embedded systems against soft errors, but they are neither always functional nor robust, even with expensive overhead in terms of hardware area, performance, and power consumption. Thus, system designers need to estimate reliability quantitatively to apply appropriate protection techniques for resource-constrained embedded systems. Vulnerability modeling based on lifetime analysis is one of the most efficient ways to quantify system reliability against soft errors. However, lifetime analysis can be inaccurate, mainly because it fails to comprehensively capture several system-level masking effects. This study analyzes and characterizes microarchitecture-level and software-level masking effects by developing an automated framework with exhaustive fault injections (i.e., soft errors) based on a cycle-accurate gem5 simulator. We injected faults into a register file because errors in the register file can easily be propagated to other components in a processor. We found that only 5% of injected faults can cause system failures on an average over benchmarks, mainly from the MiBench suite. Further analyses showed that 71% of soft errors are overwritten by write operations before being used, and the CPU does not use 20% of soft errors at all after fault injections. The remainder are also masked by several software-level masking effects, such as dynamically dead instructions, compare and logical instructions that do not change the result, and incorrect control flows that do not affect program outputs.