Long-lifetime Products Research Articles

A new universal multi-stress acceleration model and multi-parameter estimation method based on particle swarm optimization

High reliability and long-lifetime products usually work in multi-stress environment such as temperature, humidity, electricity, and vibration. How to evaluate the reliability of the product under multi-stress condition is an urgent problem to ensure the safe and reliable operation of the product. Accelerated test provides an efficient and feasible way; however, the existing acceleration models have some shortcomings, such as less stress type, neglecting the stress coupling, and multi-parameter estimation difficulties. Therefore, in this article, first, a new universal multi-stress acceleration model is derived based on the classical Arrhenius model. Second, a multi-parameter estimation method for multi-stress model is proposed by combining particle swarm optimization and maximum likelihood estimation. Six simulation cases are used to verify the effectiveness of the proposed multi-parameter estimation method. The results of Case 1 to Case 3 show that the maximum mean square error of five parameters in the multi-stress model without considering stress coupling is 3.71%. The results of Case 4 to Case 6 show that the maximum mean square error of nine parameters in the multi-stress model considering stress coupling is 7.69%. Finally, an application example is performed to investigate the performance of the universal multi-stress acceleration model and multi-parameter estimation method.

RETRACTED: A Review on Modeling and Analysis of Accelerated Degradation Data for Reliability Assessment

Abstract Accelerated degradation testing (ADT) has been recognized as an essential and effective approach for the reliability assessment of high reliability long-lifetime products, and can provide various health management strategies to ensure the safe and economical operation of deteriorating products. Studies on how to best assess such reliability have recently gained popularity owing to the rapid advances in accelerated degradation data monitoring techniques. However, because of its complicated relationship with observable degradation information, there have been no systematic reviews focused particularly on reliability assessment based on accelerated degradation data. As such, this paper reviews recent developments in the modeling and analysis of accelerated degradation data for reliability assessment. After a brief introduction of the fundamentals of ADT, this paper concentrates on a review of the reliability modeling and statistical analysis of ADT technology from four aspects, namely performance degradation modeling, acceleration modeling, identification of the consistency in failure mechanism and the model parameter estimation. We systematically review the models and approaches reported in the latest literature, and finally highlight future research challenges and possible development trends.

Preferable utilisation patterns of wood product industries' by-products in Finland

We explored varying preferable utilisation patterns of wood product industries' by-products, and which actions are needed to implement them. We: (i) concluded scenarios with varying justifications in Finland towards 2030; (ii) analysed the key drivers and barriers, and (iii) evaluated the likelihood of these scenarios, and their advantages and disadvantages. The study adopted a scenario analysis approach using qualitative and quantitative data, where the industry, research, interest groups, and policy experts formed and reviewed the scenarios. The scenarios were compiled by grouping similar opinions by using cluster analysis, and the qualitative content was synthesised by using a futures table, yielding three scenarios: (I) Pulp and Bioenergy; (II) Versatile uses; and (III) Long-lifetime products. In scenario I, closest to the current industry structure in Finland, the aim was to respond to existing needs. Scenario II suggested material circulation and economic risk diversification, and fossil-to-bio substitution potential, but experts considered it less probable to implement by 2030. Scenario III highlighted the long-term carbon storage in wood products, but was perceived as the most unlikely, due to significant technical and market requirements related to a massive shift from energy to long-lifetime products. The main drivers across all scenarios relate to international policy, investment in R&D, and cross-sectoral cooperation to boost material- and energy efficiency. Policies need to set more explicitly the targets behind the utilisation of by-products, but consider the industry structure dynamics too. The study points to a need for industrial transformations towards ambitious circulation, and smart regulation encouraging markets towards the targets.

A dynamic programming method for product upgrade planning incorporating technology development and end-of-life decisions

Product life ends when a product cannot adapt itself to new requirements, such as advances of technology, changes in customers’ preferences, etc. Product upgrade is an effective means to extend the product life, especially for high-investment, low-volume, and long-lifetime products; however, upgrade planning is complex both for product developers and product users. A product upgrade problem can be modeled as a series of decisions over time, and involve trade-offs between product performance and operation costs. These upgrade decisions might also be influenced by future technology changes. This paper proposes a quantitative method based on dynamic programming to help upgrade planners and product users to find an optimal upgrade plan incorporating the forecasts for technology development and end-of-life decisions. Through this method, total useful life cost can be minimized and sustainability can be also improved by upgrading appropriate modules at the right time with minimal impact on the system performance.

Optimal design of constant stress accelerated degradation test plan with multiple stresses and multiple degradation measures

High-reliability and long-lifetime products are often exposed to a variety of stresses in use condition, and there are usually multiple performance indicators of the products which gradually degrade over time. The technology of constant stress accelerated degradation test with multiple stresses and multiple degradation measures provides an efficient and feasible way for lifetime prediction of such products. It is one of the core issues in accelerated degradation test research how to design the plan of constant stress accelerated degradation test with multiple stresses and multiple degradation measures to result in the most accurate lifetime prediction for such products under the constraint of test budget. Existing methods of optimal design for accelerated degradation test are only for cases of a single stress or a single degradation measure which cannot be applied to solve the complex problem of optimal design for test plans under an occasion of multiple stresses and multiple degradation measures. Aimed at an occasion that analytical form of optimization objective function is difficult to derive or even does not exist, a novelty method of optimal design for constant stress accelerated degradation test with multiple stresses and multiple degradation measures is presented by incorporating Monte Carlo simulation into dealing with this problem, setting up a optimization model including design variables, optimization objective function and constraints, presenting optimization algorithm based on Monte Carlo simulation and surface fitting. Optimal design for constant stress accelerated degradation test with multiple stresses and multiple degradation measures of rubber sealing O-ring is taken as an illustrative example to demonstrate validity of the presented method. The results of sensitivity analysis of the optimal test plan show that the plan is robust when the deviation of parameters of the model is within 10%.

Study on Reliability Assessment of Degradation Date Based on Pseudo Life Distribution

In order to develop the reliability assessment of high-reliability and long-lifetime products, analyses for accelerated degradation test is discussed in this article. First, a method of building degradation path is presented; second, the means of degradation data processing is analyzed; finally, an example is presented and validity of this method is verified.

Reliability demonstration based on accelerated degradation testing for unknown model parameters

Conventional reliability demonstration tests are becoming increasingly difficult to apply for high-reliability and long-lifetime products owing to the excessive test duration, which conflicts with the marketplace demands for decreased development time. Accelerated degradation testing has been strongly recommended to reduce test time, but few studies employ this method for reliability demonstrations. In this article, a test methodology based on accelerated degradation testing is developed to demonstrate the reliability target for the case in which the model parameters for the test plan design are unknown. In the proposed procedure, step-stress accelerated degradation testing is employed as a preliminary test to estimate the model parameters. The data from step-stress accelerated degradation testing are converted based on equivalent degradation criterion and then used to make decisions by the method of two steps accelerated reliability demonstration test. If there are not enough converted data to make a decision, an accelerated degradation testing as a complementary test is conducted to complete the demonstration according to the estimates of the model parameters from the preliminary test; the two steps accelerated reliability demonstration test method is applied. The proposed methodology incorporates step-stress accelerated degradation testing with two steps accelerated reliability demonstration test to permit the test duration to be reduced as much as possible. It is useful to demonstrate a high-reliability target at long mission times within a feasible test duration when the design parameters are unknown.