arXiv subjects

Zhi-Sheng Ye

Publications and source records attributed to Zhi-Sheng Ye.

4 recordsLinked to original sources

arXiv2025

Joint Bundle Design and Pricing for Extended Warranty Providers Servicing Multi-Tier Products

Extended warranties (EWs) constitute a significant source of revenue for capital-intensive products. Such products comprise multiple subsystems, enabling flexible EW design. For example, providers can bundle tailored sets of subsystems within different EW contracts, facilitating the creation of a service menu with differentiated warranty options. From the perspective of a third-party EW provider servicing multi-tier products, we develop a novel model to jointly optimize bundle design and pricing for EW options in order to maximize the expected total profit. Specifically, the problem involves determining which contracts-each containing a differentiated bundle of subsystems-to recommend for the multi-tier products and identifying the appropriate price for each contract. As the complexity of the joint optimization problem increases exponentially with the number of subsystems, we devise two solution approaches. The first approach leverages a mixed-integer second-order cone programming reformulation, which guarantees optimality but is applicable only for a small number of subsystems. The second approach utilizes an iterative two-step process, offering enhanced computational efficiency for scenarios involving a large number of subsystems. Numerical experiments validate the effectiveness of our model, particularly in scenarios characterized by high failure probabilities and a large number of subsystems.

math.OC↗

arXiv2025

Optimal Abort Policy for Mission-Critical Systems under Imperfect Condition Monitoring

While most on-demand mission-critical systems are engineered to be reliable to support critical tasks, occasional failures may still occur during missions. To increase system survivability, a common practice is to abort the mission before an imminent failure. We consider optimal mission abort for a system whose deterioration follows a general three-state (normal, defective, failed) semi-Markov chain. The failure is assumed self-revealed, while the healthy and defective states have to be {inferred} from imperfect condition monitoring data. Due to the non-Markovian process dynamics, optimal mission abort for this partially observable system is an intractable stopping problem. For a tractable solution, we introduce a novel tool of Erlang mixtures to approximate non-exponential sojourn times in the semi-Markov chain. This allows us to approximate the original process by a surrogate continuous-time Markov chain whose optimal control policy can be solved through a partially observable Markov decision process (POMDP). We show that the POMDP optimal policies converge almost surely to the optimal abort decision rules when the Erlang rate parameter diverges. This implies that the expected cost by adopting the POMDP solution converges to the optimal expected cost. Next, we provide comprehensive structural results on the optimal policy of the surrogate POMDP. Based on the results, we develop a modified point-based value iteration algorithm to numerically solve the surrogate POMDP. We further consider mission abort in a multi-task setting where a system executes several tasks consecutively before a thorough inspection. Through a case study on an unmanned aerial vehicle, we demonstrate the capability of real-time implementation of our model, even when the condition-monitoring signals are generated with high frequency.

math.OC↗

arXiv2023

Physics-Informed Neural Networks for Prognostics and Health Management of Lithium-Ion Batteries

For Prognostics and Health Management (PHM) of Lithium-ion (Li-ion) batteries, many models have been established to characterize their degradation process. The existing empirical or physical models can reveal important information regarding the degradation dynamics. However, there are no general and flexible methods to fuse the information represented by those models. Physics-Informed Neural Network (PINN) is an efficient tool to fuse empirical or physical dynamic models with data-driven models. To take full advantage of various information sources, we propose a model fusion scheme based on PINN. It is implemented by developing a semi-empirical semi-physical Partial Differential Equation (PDE) to model the degradation dynamics of Li-ion batteries. When there is little prior knowledge about the dynamics, we leverage the data-driven Deep Hidden Physics Model (DeepHPM) to discover the underlying governing dynamic models. The uncovered dynamics information is then fused with that mined by the surrogate neural network in the PINN framework. Moreover, an uncertainty-based adaptive weighting method is employed to balance the multiple learning tasks when training the PINN. The proposed methods are verified on a public dataset of Li-ion Phosphate (LFP)/graphite batteries.

eess.SP↗

arXiv2014

How do heterogeneities in operating environments affect field failure predictions and test planning?

The main objective of accelerated life tests (ALTs) is to predict fraction failings of products in the field. However, there are often discrepancies between the predicted fraction failing from the lab testing data and that from the field failure data, due to the yet unobserved heterogeneities in usage and operating conditions. Most previous research on ALT planning and data analysis ignores the discrepancies, resulting in inferior test plans and biased predictions. In this paper we model the heterogeneous environments together with their effects on the product failures as a frailty term to link the lab failure time distribution and field failure time distribution of a product. We show that in the presence of the heterogeneous operating conditions, the hazard rate function of the field failure time distribution exhibits a range of shapes. Statistical inference procedure for the frailty models is developed when both the ALT data and the field failure data are available. Based on the frailty models, optimal ALT plans aimed at predicting the field failure time distribution are obtained. The developed methods are demonstrated through a real life example.

stat.AP↗