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We study a reliability system subject to occasional random shocks of random magnitudes W0,W1,W2,… occurring at times τ0,τ1,τ2,…. Any such shock is harmless or critical dependent on Wk≤H or Wk>H, given a fixed threshold H. It takes a total of N critical shocks to knock the system down. In addition, the system ages in accordance with a monotone increasing continuous function δ, so that when δT crosses some sustainability threshold D at time T, the system becomes essentially inoperational. However, it can still function for a while undetected. The most common way to do the checking is at one of the moments τ1,τ2,… when the shocks are registered. Thus, if crossing of D by δ occurs at time T∈τk,τk+1, only at time τk+1, can one identify the system’s failure. The age-related failure is detected with some random delay. The objective is to predict when the system fails, through the Nth critical shock or by the observed aging moment, whichever of the two events comes first. We use and embellish tools of discrete and continuous operational calculus (D-operator and Laplace–Carson transform), combined with first-passage time analysis of random walk processes, to arrive at fully explicit functionals of joint distributions for the observed lifetime of the system and cumulative damage to the system. We discuss various special cases and modifications including the assumption that D is random (and so is T). A number of examples and numerically drawn figures demonstrate the analytic tractability of the results.

In this paper, we deal with a mixed reliability system decaying from natural wear, occasional soft and hard shocks that eventually lead the system to failure. The aging process alone is linear and it is escalated through soft shocks such that they lead to the system’s soft failure when the combined damage exceeds a threshold M. The other threat is that posed by occasional hard shocks. When the total number of them identified as critical (each critical shock exceeds a fixed threshold H) reaches N, the system becomes disabled. With N=1, a critical shock is extreme. The arrival stream of shocks is a renewal process marked by soft and hard shocks. We establish a formula for a closed form functional containing system’s time-to-failure, the state of the system upon its failure, and other useful statistical characteristics of the system using and embellishing fluctuation analysis and operational calculus. Special cases provide tame expressions that are computed and validated by simulation.

By traditional reliability test and evaluation method in stable condition, it is difficult to reflect the in-service reliability ability of Industrial Robot Servo System (IRSS). In this paper, an in-service simulation test platform for industrial robot servo system is established, and an in-service reliability evaluation framework based on series Wiener processes is proposed. In the proposed framework, the IRSS In-service test platform was applied to carry out performance degradation tests of IRSS under its service conditions, IRSS degradation factor was constructed based on Restricted Boltzmann Machine, and three Wiener process models with random effects were applied to evaluate IRSS reliability. Based on the proposed framework, the in-service failure life of the IRSSs were accurately predicted. Compared with conventional reliability testing and evaluation methods based on stable operating conditions, the proposed approach demonstrates superior performance in the in-service reliability evaluation of IRSS, achieving an 83.05% improvement in assessment accuracy. The proposed method enable precise tracking and assessment of in-service degradation in robotic electrical machines and drives. This provides quantitative assessment results as critical references for implementing health management in high-dynamic electrical machines and drive systems, while establishing a robust foundation for the design and development of high-performance and high-reliability robotic drive systems.

A homogenous continuous-time Markov model (HCTMM) has the advantage of high accuracy and is usually used in reliability analysis. However, the transition intensities (rates) between any two states cannot be accurately obtained due to the discrete data obtained from a mechanical system and little expert knowledge. This paper proposes a method in which the transition intensity matrix of an HCTMM can be obtained indirectly using the actual discrete degradation data of a mechanical system. Firstly, the one-step transition probability matrix can be calculated by a hidden Markov model using the discrete degradation data. Secondly, the unit-time transition intensity matrix can be calculated from the one-step transition probability matrix. At last, an HCTMM is established to assess the reliability of a mechanical system based on the unit-time transition intensity matrix. Moreover, considering the maintenance activities cannot restore a mechanical system as a fire-new one, the random transition of a mechanical system after maintenance is expressed by a quasi-renewal process in this paper. Finally, three cases are studied to validate the proposed method.

Forest degradation is a global phenomenon and while being an important indicator and precursor to further forest loss, carbon emissions due to degradation should also be accounted for in national reporting within the frame of UN REDD+. At regional to country scales, methods have been progressively developed to detect and map forest degradation, with these based on multi-resolution optical, synthetic aperture radar (SAR) and/or LiDAR data. However, there is no one single method that can be applied to monitor forest degradation, largely due to the specific nature of the degradation type or process and the timeframe over which it is observed. The review assesses two main approaches to monitoring forest degradation: first, where detection is indicated by a change in canopy cover or proxies, and second, the quantification of loss (or gain) in above ground biomass (AGB). The discussion only considers degradation that has a visible impact on the forest canopy and is thus detectable by remote sensing. The first approach encompasses methods that characterise the type of degradation and track disturbance, detect gaps in, and fragmentation of, the forest canopy, and proxies that provide evidence of forestry activity. Progress in these topics has seen the extension of methods to higher resolution (both spatial and temporal) data to better capture the disturbance signal, distinguish degraded and intact forest, and monitor regrowth. Improvements in the reliability of mapping methods are anticipated by SAR-optical data fusion and use of very high resolution data. The second approach exploits EO sensors with known sensitivity to forest structure and biomass and discusses monitoring efforts using repeat LiDAR and SAR data. There has been progress in the capacity to discriminate forest age and growth stage using data fusion methods and LiDAR height metrics. Interferometric SAR and LiDAR have found new application in linking forest structure change to degradation in tropical forests. Estimates of AGB change have been demonstrated at national level using SAR and LiDAR-assisted approaches. Future improvements are anticipated with the availability of next generation LiDAR sensors. Improved access to relevant satellite data and best available methods are key to operational forest degradation monitoring. Countries will need to prioritise their monitoring efforts depending on the significance of the degradation, balanced against available resources. A better understanding of the drivers and impacts of degradation will help guide monitoring and restoration efforts. Ultimately we want to restore ecosystem service and function in degraded forests before the change is irreversible.

Purpose The purpose of this paper is to propose new importance measures for degrading components based on Shapley value, which can provide answers about how important players are to the whole cooperative game and what payoff each player can reasonably expect. Design/methodology/approach The proposed importance measure characterizes how a specific degrading component contributes to the degradation of system reliability by using Shapley value. Degradation models are also introduced to assess the reliability of degrading components. The reliability of system consisting independent degrading components is obtained by using structure functions, while reliability of system comprising correlated degrading components is evaluated with a multivariate distribution. Findings The ranking of degrading components according to the newly developed importance measure depends on the degradation parameters of components, system structure and parameters characterizing the association of components. Originality/value Considering the fact that reliability degradation of engineering systems and equipment are often attributed to the degradation of a particular or set of components that are characterized by degrading features. This paper proposes new importance measures for degrading components based on Shapley value to reflect the responsibility of each degrading component for the deterioration of system reliability. The results are also able to give timely feedback of the expected contribution of each degrading component to system reliability degradation.

Rotor-bearing systems play a vital role in machine tools, aero engines, and wind turbines. Generally, worn-induced degradation quantities and manufacturing errors of components are the main error sources that influence the precision reliability of rotor-bearing systems. The current precision reliability evaluation models are focusing on several error sources in only a few key components without agreeable results. Therefore, a precision reliability evaluation model is proposed considering all time-variant error sources and random error sources. Firstly, time-variant wear models for commonly occurred degradation types in a rotor-bearing system are developed. Secondly, the constructed time-variant wear models are inserted into the precision model with all moving components in the rotor-bearing system using meta-action structural decomposition method. Finally, the time-variant stochastic process discretization method is employed to establish the precision reliability evaluation model, and solve the precision reliability of the rotor-bearing systems. Case investigations are carried out to verify the performance of the present model, which provides a more accurate precision reliability evaluation model to estimate the conditions of rotor-bearing systems during the service period.

Traditional methods of characterizing biodiversity are increasingly being supplemented and replaced by approaches based on DNA sequencing alone. These approaches commonly involve extraction and high-throughput sequencing of bulk samples from biologically complex communities or samples of environmental DNA (eDNA). In such cases, vouchers for individual organisms are rarely obtained, often unidentifiable, or unavailable. Thus, identifying these sequences typically relies on comparisons with sequences from genetic databases, particularly GenBank. While concerns have been raised about biases and inaccuracies in laboratory and analytical methods, comparatively little attention has been paid to the taxonomic reliability of GenBank itself. Here we analyze the metazoan mitochondrial sequences of GenBank using a combination of distance-based clustering and phylogenetic analysis. Because of their comparatively rapid evolutionary rates and consequent high taxonomic resolution, mitochondrial sequences represent an invaluable resource for the detection of the many small and often undescribed organisms that represent the bulk of animal diversity.We show that metazoan identifications in GenBank are surprisingly accurate, even at low taxonomic levels (likely <1% error rate at the genus level). This stands in contrast to previously voiced concerns based on limited analyses of particular groups and the fact that individual researchers currently submit annotated sequences to GenBank without significant external taxonomic validation. Our encouraging results suggest that the rapid uptake of DNA-based approaches is supported by a bioinformatic infrastructure capable of assessing both the losses to biodiversity caused by global change and the effectiveness of conservation efforts aimed at slowing or reversing these losses.

Point feature-based visual simultaneous localization and mapping (SLAM) systems are prone to performance degradation in low-texture environments due to insufficient extraction of point features. In this paper, we propose a tightly-coupled stereo visual-inertial SLAM system with point and line features (PLI-SLAM) to enhance the robustness and reliability of systems in low-texture environments. We improve Edge Drawing lines (EDlines) for line feature detection by introducing curvature detection and a new standard for minimum line segment length to improve the accuracy of the line features, while reducing the line feature detection time. We contribute also with an improved adapting factor based on experiment to adjust the error weight of line features, which further improves the localization accuracy of the system. Our system has been tested on the EuRoC dataset. Tests on public datasets and in real environments have shown that PLI-SLAM achieves high accuracy. Furthermore, PLI-SLAM could still operate robustly even in some challenging environments. The processing time of our method is reduced by 28%, compared to the ORB-LINE-SLAM based on point and line, when using Line Segment Detector (LSD).

Improving the long-term stability of perovskite solar cells is critical to the deployment of this technology. Despite the great emphasis laid on stability-related investigations, publications lack consistency in experimental procedures and parameters reported. It is therefore challenging to reproduce and compare results and thereby develop a deep understanding of degradation mechanisms. Here, we report a consensus between researchers in the field on procedures for testing perovskite solar cell stability, which are based on the International Summit on Organic Photovoltaic Stability (ISOS) protocols. We propose additional procedures to account for properties specific to PSCs such as ion redistribution under electric fields, reversible degradation and to distinguish ambient-induced degradation from other stress factors. These protocols are not intended as a replacement of the existing qualification standards, but rather they aim to unify the stability assessment and to understand failure modes. Finally, we identify key procedural information which we suggest reporting in publications to improve reproducibility and enable large data set analysis. Reliability of stability data for perovskite solar cells is undermined by a lack of consistency in the test conditions and reporting. This Consensus Statement outlines practices for testing and reporting stability tailoring ISOS protocols for perovskite devices.