Degradation Level Of System Research Articles

Sequential Bayesian Planning for Accelerated Degradation Tests Considering Sensor Degradation

Most classical accelerated degradation test (ADT) planning models implicitly overlook the errors when measuring the degradation levels of the test units. However, the sensor measurement errors are inevitable and the magnitude of the errors may have a trend to increase over time due to sensor degradation. As a consequence improperly overlooking the sensor degradation in ADT planning could result in a test plan with unsatisfactory performance. This article addresses this issue by proposing a sequential ADT planning model that factors in sensor degradation. The system degradation level is periodically measured, based on which we dynamically adjust the stress level during ADT. We adopt a Bayesian framework that periodically updates the posterior distribution of model parameters considering the sensor degradation. An approximate Bayesian computation algorithm is developed to circumvent the difficulty of directly evaluating the complicated likelihood function in our problem. Numerical studies on a gas turbine reveal that our sequential model outperforms several traditional ADT designs that overlook the sensor degradation.

System-Level Performance Prediction for Infrared Systems Based on Energy Redistribution in Infrared Images

The infrared ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IR</i> ) system has an abroad application in industry, military, medical science, and many aspects of society life. Performance prediction of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IR</i> system has great significance to advance maintenance decisions before system severe degradation. This article presents a system-level prognostic method to predict <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IR</i> system performance. The proposed method treats <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IR</i> system degradation as energy redistribution between different degradation levels, and considers the system-level degradation as joint effect of degradations from different subsystems. To quantify system degradation level, a health indicator (HI) whose value can be measured from system output <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IR</i> images is constructed. Then, prediction of the HI is achieved by a modulation transfer function (MTF)-based prognostic model established based on the degradation mechanism of subsystems. Degradation modeling in the proposed MTF-based prognostic model is realized in a just-in-time learning framework, in which the model with simple structure is built locally and dynamically using a limited amount of relevant data from historical dataset. Finally, a Gaussian particle filter-based online inference is utilized to update prediction results sequentially as observations of HI become available. The effectiveness and advantages of the proposed method is evaluated and verified through simulations and real <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IR</i> data collected from flight experiments, with associated comparative analysis.

Accelerated degradation tests with inspection effects

This study proposes a framework to analyze accelerated degradation testing (ADT) data in the presence of inspection effects. Motivated by a real dataset from the electric industry, we study two types of effects induced by inspections. After each inspection, the system degradation level instantaneously reduces by a random value. Meanwhile, the degrading rate is elevated afterwards. Considering the absence of observations due to practical reasons, we employ the expectation–maximization (EM) algorithm to analytically estimate the unknown parameters in a stepwise Wiener degradation process with covariates. Moreover, to maintain the level of generality for the adaption of the method in various scenarios, a confidence density approach is utilized to hierarchically estimate the parameters in the acceleration link function. The proposed methods can provide efficient parameter estimation under complex link functions with multiple stress factors. Further, confidence intervals are derived based on the large-sample approximation. Simulation studies and a case study from Schneider Electric are used to illustrate the proposed methods. The results show that the proposed model yields a remarkably better fit to the Schneider data in comparison to the conventional Wiener ADT model.

Condition-based maintenance with imperfect inspections for continuous degradation processes

A condition-based maintenance (CBM) strategy is now recognized as an efficient approach to perform maintenance at the best time before failures so as to save lifetime cycle cost. For continuous degradation processes, a significant source of variability lies in measurement errors caused by imperfect inspections, and this may lead to “false positive” or “false negative” observations, and consequently to inopportune maintenance decisions. To the best of our knowledge, researches on CBM optimization with imperfect inspections remain limited for continuous degradation processes, even though the subject is of practical interest for the implementation of a CBM policy. Imperfect inspections are indeed imperfect but still return interesting information on the system degradation level, and making them perfect can be expensive. Therefore, we analyze the economic performance of a maintenance policy with imperfect inspections, and compare it with the classical policy with perfect inspections to see which policy offers the best benefit in a given situation. Furthermore, a CBM policy with a two-stage inspection scheme is proposed to take benefit of mixing both perfect and imperfect inspections in the same maintenance policy. Through numerical experiments and a real case study, it is shown that the policy with imperfect inspections can be better than the classical one, and that the proposed policy with a two-stage inspection scheme always leads to the minimum long run maintenance cost rate.

Reliability analysis of a system with two-stage degradation using Wiener processes with piecewise linear drift

Abstract We study a model of a two-stage degradation process in a dynamic environment. The two stages, the normal stage and the defective stage, are separated by the first hitting time of the alarm threshold by the degradation level. Wiener processes with piecewise linear drift are used in each stage to describe the degradation level in a dynamic environment. System failure is triggered in two ways: the system degradation level reaches the defect-based failure threshold; the duration in the defective stage is larger than the duration-based failure threshold. Explicit expressions for the system reliability for different duration-based failure thresholds are obtained. These include when the duration-based failure threshold is zero, when it is a positive constant and when it tends to infinity. A simulation procedure is described for the case in which the duration-based failure threshold is a random variable. Finally, some numerical examples are presented to illustrate the proposed reliability assessment method. The modelling method and the results can be used for reliability evaluation, residual life prediction and maintenance decision optimization of a system with two-stage degradation in a dynamic environment.

Optimal overhaul\u2013replacement policy for a multi-degraded repairable system sold with warranty

In this research, we study an optimal overhaul–replacement policy of a multi-degraded repairable system sold with a free replacement warranty. In the proposed replacement policy, a maintenance action and failure are dependent on a system degradation level and the system age, and hence the replacement model will provide more effective maintenance decisions. Failure of the system is modeled using a rate of occurrence of failure for the system, which is as a function of a degradation level of the system and its age. We develop the optimal replacement policy for a multi-degraded repairable system from the buyer’s point of view, who plans to use the system for a horizon planning T. The buyer conducts a periodic evaluation and selects an appropriate maintenance option based on the revealed system condition together with the system operational age. At each evaluation point, three alternative decisions are available, i.e., keep running the system, overhaul, or replace it with a new one. We formulate the sequential decision (keep, overhaul, or replace) problem as a dynamic programming model and obtain an optimal policy that minimizes total cost over T. Numerical examples are presented using several hypothetical data sets to illustrate the structure of optimal solution and its sensitivity against the change in parameter values. The main contribution of the paper is to offer a decision tool that will help in deciding the overhaul–replacement action based on the degradation level and the operational age of the system.

A rolling horizon approach for production planning and condition-based maintenance under uncertain demand

In reality, the forecast of uncertainties often becomes more accurate with the approaching of the forecasted period. This article proposes a rolling horizon approach to dynamically determine the production plan and the maintenance plan for a degradation system under uncertain environment. In each rolling horizon, demand forecasts are updated with new information from customers, and the degradation level of system is confirmed by inspection. By taking advantage of the updated uncertainties, at each decision point, the maintenance plan is determined by an advance-postpone balancing approach and the production plan is optimized by a heuristic algorithm in a two-stage stochastic model. Numerical results validate that the rolling horizon approach has great superiority over traditional stochastic programming approach in terms of real total cost and service level.

Gamma process based optimal mission abort policy

Various safety-critical systems such as aircrafts, submarines and space stations are required to perform missions continually in harsh environment. To enhance the survivability of such systems, a mission can be aborted when the mission completion becomes problematic and a rescue procedure is immediately initiated. This paper investigates the optimal mission abort policy based on a two-stage Gamma process which can be separated into normal and defective stages. Degradation-based abort policy where a mission is aborted if the system degradation level is higher than a threshold and duration-based abort policy where a mission is aborted if the duration in defective stage is larger than a threshold are considered. The mission success probability and system survivability under these two policies are evaluated and the optimal mission abort thresholds balancing the tradeoff between the mission success probability and system survivability are investigated. A numerical example on an unmanned aerial vehicle (UAV) is presented to illustrate the applicability of the proposed abort policies.

Joint optimization of monitoring quality and replacement decisions in condition-based maintenance

The quality of condition monitoring is an important factor affecting the effectiveness of a condition-based maintenance program. It depends closely on implemented inspection and instrument technologies, and eventually on investment costs, i.e., a more accurate condition monitoring information requires a more sophisticated inspection, hence a higher cost. While numerous works in the literature have considered problems related to condition monitoring quality, (e.g., imperfect inspection models, detection and localization techniques, etc.) few of them focus on adjusting condition monitoring quality for condition-based maintenance optimization. In this paper, we investigate how such an adjustment can help to reduce the total cost of a condition-based maintenance program. The condition monitoring quality is characterized by the observation noises on the system degradation level returned by an inspection. A dynamic condition-based maintenance and inspection policy adapted to such a observation information is proposed and formulated based on Partially Observable Markov Decision Processes. The use and advantages of the proposed joint inspection and maintenance model are numerically discussed and compared to several inspection-maintenance policies through numerical examples.

Optimization of partial software rejuvenation policy

This paper models a real-time software system performing state-based partial rejuvenations for counteracting performance deterioration effects caused by software aging. The decision on performing each rejuvenation, recovery state and corresponding rejuvenation time depend on both the system degradation level (state) and task operations completed immediately before the rejuvenation action. Full rejuvenations where the system performance is recovered to the peak level appear as a special case of the considered rejuvenation model when the recovery state after performing the rejuvenation is the initial perfect state. We suggest an iterative numerical method based on event transitions for assessing the successful completion probability of a real-time task performed by the considered software system. The proposed method has no limitation on the distribution type of any state sojourn time (or state transition time). We further optimize the state-based partial rejuvenation policy for maximizing the probability of completing a particular real-time task. Impacts of different parameters on the optimization solution are demonstrated through examples, including the discretization parameter used in the suggested numerical algorithm, real-time task deadline, and rejuvenation time parameter.

Optimal maintenance strategy of deteriorating system under imperfect maintenance and inspection using mixed inspectionscheduling

An operating system suffers from degradation. Each degradation level can be represented by a state, making the system a multi-state system. In many situations, there are no apparent symptoms indicating the system's state, and system's degradation level can only be known through thorough inspection. Through condition monitoring, the system's state can be estimated with some uncertainty. In this work, we investigated inspection-maintenance schemes for such a system. Our assumptions are that the maintenance is imperfect and the degradation is a continuous-time Markov process. We proposed a strategy that combines both inspection and continuous monitoring to reduce unnecessary thorough inspection and to improve the system's reliability. Optimal maintenance strategy is derived based on an iterative algorithm to minimize the mean long-run costrate.

A Multi-Objective Optimization of Imperfect Preventive Maintenance Policy for Dependent Competing Risk Systems With Hidden Failure

This paper studies a multi-objective maintenance optimization embedded within the imperfect preventive maintenance (PM) for one single-unit system subject to the dependent competing risks of degradation wear and random shocks. We consider two kinds of random shocks in the system: 1) fatal shocks that will cause the system to fail immediately, and 2) nonfatal shocks that will increase the system degradation level by a certain cumulative shock amount. Also, an improvement factor in the form of quasi-renewal sequences is introduced to modulate the imperfect maintenance by raising the degradation critical threshold proportionally. Finally, the two decision variables for maintenance scheduling, the number of PMs to replacement, and the initial PM interval, are determined by simultaneously maximizing the system asymptotic availability, and minimizing the system cost rate using the fast elitist non-dominated Sorting Genetic Algorithm (NSGA-II). Sensitivity analysis for two parameters, including imperfect PM degree, and quasi-renewal coefficient of imperfect PM interval, is performed to provide insight into the behavior of the proposed maintenance policies. The comparison results show that the optimization solution is consistent between one-objective and multi-objective optimization, and the Pareto frontier for the maintenance optimization problem can provide alternative solutions according to customer preference and resource constraints.