Real-time Reliability Assessment Research Articles

An In-Depth Study of Ring Oscillator Reliability under Accelerated Degradation and Annealing to Unveil Integrated Circuit Usage.

The reliability and durability of integrated circuits (ICs), present in almost every electronic system, from consumer electronics to the automotive or aerospace industries, have been and will continue to be critical concerns for IC chip makers, especially in scaled nanometer technologies. In this context, ICs are expected to deliver optimal performance and reliability throughout their projected lifetime. However, real-time reliability assessment and remaining lifetime projections during in-field IC operation remain unknown due to the absence of trustworthy on-chip reliability monitors. The integration of such on-chip monitors has recently gained significant importance because they can provide real-time IC reliability extraction by exploiting the fundamental physics of two of the major reliability degradation phenomena: bias temperature instability (BTI) and hot carrier degradation (HCD). In this work, we present an extensive study of ring oscillator (RO)-based degradation and annealing monitors designed on our latest 28 nm versatile array chip. This test vehicle, along with a dedicated test setup, enabled the reliable statistical characterization of BTI- and HCD-stressed as well as annealed RO monitor circuits. The versatility of the test vehicle presented in this work permits the execution of accelerated degradation tests together with annealing experiments conducted on RO-based reliability monitor circuits. From these experiments, we have constructed precise annealing maps that provide detailed insights into the annealing behavior of our monitors as a function of temperature and time, ultimately revealing the usage history of the IC.

A digital twin based reliability assessment of Superbuck converters

Addressing the challenge of assessing the reliability of ‘long-life, high-reliability’ DC/DC analog converters in submarine observation networks and space power systems, which have limited internal observation points and insufficient valid information, and initial assessment rendered unreliable with the long-term operation of the converter. This paper proposes a novel method based on digital twin (DT) technology. The method eliminates the need for measuring internal variables, and only requires the measurement of the converter’s ripple peak-to-peak. A digital twin (DT) is constructed based on the underlying mechanism, and the parameters of the operating converter are identified through the collection of ripple peak-to-peak, which are then injected into the DT for dynamic tracking of the physical entity. By obtaining internal current and voltage of the DT and utilizing the Markov model, the reliability of the converter can be assessed. This method offers real-time reliability assessment without the need for additional measurement points. To validate the effectiveness of the proposed approach, a Superbuck converter is used as an example to demonstrate the high-fidelity of the DT through isomorphic single-sample repeatability and isomorphic multiple-sample consistency experiments.

MULTIAGENT APPROACH TO COMPUTER MANAGEMENT IN A HETEROGENEOUS DISTRIBUTED COMPUTER ENVIRONMENT

The reliability of modern computing control systems in a heterogeneous distributed computing environment, along with efficiency, survivability, security, control efficiency, is an important component of their quality. Increasingly, these systems fall into the category of "critical", have an absolute impact on the activities of organizations and enterprises within which they operate. The loss of such systems, even for a short time, leads to serious problems related to loss of income, unforeseen costs, downtime of production and personnel, loss of time, and sometimes man-made disasters. As you know, the greatest impact on the reliability of control systems has the reliability and fault tolerance of a set of software and hardware. Therefore, solving problems related to improving the reliability of the software part of the systems is the most urgent task. Currently, significant results have been obtained in the field of evaluation and forecasting of reliability indicators of elements and typical software packages at the stage of their design; a large number of methods known to algorithms and programs are known; a number of normative documents on project reliability assessment have been developed. However, the task of real-time reliability assessment, when accurate and operational accounting of a number of factors is required, has not been sufficiently solved. To solve the problem of multi-agent approach to computing control in a heterogeneous distributed computing environment used methods of systems analysis, set theory - to develop models of task distribution, models of tasks and computing resources, general systems theory - to study and develop methods of task distribution, logic-theory theory. for modeling computational processes. The article considers a multi-agent approach to computing control in a heterogeneous distributed computing environment. The algorithm is based on the use of economic mechanisms to regulate the supply and demand of resources in the computing environment. The architecture of the multi-agent approach and the functions of the agents are described. Particular attention is paid to calculating the reliability of the task plan based on the logical-probabilistic method.

Learning for Predictions: Real-Time Reliability Assessment of Aerospace Systems

Prognostics and health management aim to predict the remaining useful life (RUL) of a system and to allow a timely planning of replacement of components, limiting the need for corrective maintenance and the downtime of equipment. A major challenge in system prognostics is the availability of accurate physics-based representations of the faults dynamics. Additionally, the analysis of data acquired during flight operations is traditionally time consuming and expensive. This work proposes a computational method to overcome these limitations through the dynamic adaptation of the state-space model of fault propagation to onboard observations of the system’s health. Our approach aims at enabling real-time assessment of systems’ health and reliability through fast predictions of the remaining useful life that accounts for uncertainty. The strategy combines physics-based knowledge of the system damage propagation rate, machine learning. and real-time measurements of the health status to obtain an accurate estimate of the RUL of aerospace systems. The original method is demonstrated for the RUL prediction of an electromechanical actuator for aircraft flight controls. We observe that the strategy allows us to refine rapid predictions of the RUL in fractions of seconds by progressively learning from onboard acquisitions.

Real-time Reliability Evaluation Based on Stochastic Degradation Process and Sequential Joint Distribution Estimation

According to the different performance degradation paths, reliability curves and parameters of each product in service phase, the reliability and remaining life of components at current moment can be evaluated in real-time reliability assessment by integrating real-time status, historical information and service time of components. The realtime reliability evaluation is valued because it is more personalized, precise, real-time and lean than traditional methods. Furthermore, the results from real-time reliability assessment can also represent the health status of component at current time. In the current research, real-time reliability assessment mainly relies on regression analysis and time series analysis, but these two methods are mainly used to describe the component degradation process, and cannot reflect the influence of external random environment on the component state change. At the same time, due to these limitations of time, economy and test conditions, it is also worth studying how to obtain more accurate and practical reliability distribution and determine a detection interval under the condition of less data. Therefore, based on the analysis of real-time reliability evaluation principle, a more appropriate real-time reliability evaluation method and the detection interval decision model are proposed by means of random degradation process, parameter sequence test joint distribution estimation and failure risk. The result from this method is a quantized value, which is convenient for the direct application of the follow-up maintenance decision research. Therefore, the research in this paper has extensive reference value and practical application prospect.

Optimising Monitoring: Standards, Reliability Basis and Application to Assessment of Roof Snow Load Risks

This paper addresses the optimisation of monitoring and decision-making criteria regarding the future use of structures. The current state of practice in standards and reliability framework are initially summarised. A general methodology for obtaining cost-optimal decisions is then presented on the basis of limit state design, probabilistic reliability analysis and cost estimates. The implementation of the risk-based approach is illustrated in a case study dealing with the roof of a stadium located in northern Italy. As the roof fails to comply with the requirements of the Eurocodes, the installation of a permanent monitoring system is recommended to allow for a real-time reliability assessment. The results demonstrate the potential of monitoring systems and probabilistic reliability analysis for supporting decisions regarding safety measures such as snow removal or temporary closure of the stadium, as well as reflecting the need to implement the discussed procedures in future standards.

Evaluation of Reliability Function and Mean Residual Life for Degrading Systems Subject to Condition Monitoring and Random Failure

This paper presents a new general method for evaluating the reliability function and the mean residual life of degrading systems subject to condition monitoring and random failure. In the proposed method, the degradation process of the system is characterized by a continuous-time Markov chain, which is then incorporated into the proportional hazards model as a stochastic covariate process to describe the hazard rate of the time to system failure. Unlike the conventional method based on conditioning, which is applicable only for a small number of degradation states, the proposed method is capable of tackling the case with a general number of degradation states. Using the developed approximation techniques, closed-form formulas for related reliability characteristics are obtained in terms of the appropriate transition probability matrix. The proposed evaluation algorithm is computationally efficient and embeddable to support real-time reliability assessment of the system subject to condition monitoring for developing the optimal maintenance policy. The effectiveness and the accuracy of the method are validated by a numerical study and compared with the conventional method. A general case where the degradation path can be discretized up to ten states is also studied to illustrate the appealing general features.

Real-time reliability assessment based on acceleration monitoring for bridge

Bridge health monitoring (BHM) has become increasingly significant in the life-cycle of the structure such as maintenance, repair and rehabilitation. It is necessary to use BHM information efficiently to assess the working conditions of the bridge. The main objective of this study is to develop an effective method and establish a framework for the real-time reliability assessment based on BHM acceleration information. The first-passage probability and its further development have been proposed to assess the reliability probability. The first-passage probability shows the probability of that a scalar process exceeds a designated threshold during a given time interval. The advantage of the proposed method is the assessment of the real-time reliability probability based on the monitoring information during an assessment reference period. Furthermore, the velocity data and displacement data are calculated from the acceleration monitoring data using the relationships between their power spectral density (PSD) functions. The real-time reliability assessment of Donghai Bridge, which is the first large scale cross-sea bridge in China, demonstrates that the proposed method is efficient and effective.