@article{choi_zhu_kang_jeong_2024, title={Convolutional neural network based multi-input multi-output model for multi-sensor multivariate virtual metrology in semiconductor manufacturing}, ISSN={["1572-9338"]}, DOI={10.1007/s10479-024-05902-z}, journal={ANNALS OF OPERATIONS RESEARCH}, author={Choi, Jeongsub and Zhu, Mengmeng and Kang, Jihoon and Jeong, Myong K.}, year={2024}, month={Mar} }
 @article{wang_zhu_zhang_2024, title={Lifetime prediction and maintenance assessment of Lithium-ion batteries based on combined information of discharge voltage curves and capacity fade}, volume={81}, ISSN={["2352-1538"]}, DOI={10.1016/j.est.2023.110376}, abstractNote={Current two-stage Lithium-ion battery degradation models commonly treat the change point (CP) in two ways. First, it is a random variable, which increases model complexity and the computational cost for predicting the remaining useful life (RUL). Second, it is a deterministic value, which will simplify the degradation models. The value of CP needs to be well identified for an accurate description of the degradation process. However, the capacity data is generally non-monotonic due to energy regeneration, which makes it hard to use in determining the CP. In addition, our laboratory data on the discharge voltage profile shows a potential for CP detection. Thus, we developed a hybrid method that combines dynamic time warping and nonnegative matrix factorization to detect the CPs of battery cells by using voltage discharge profiles. Then, we proposed a two-stage Wiener process incorporating CP detection method to describe the battery capacity degradation pattern. The proposed model is applied to our lab data and NASA data to predict RUL. Finally, maintenance strategies are analyzed to enhance the management of Lithium-ion batteries by minimizing the long-term cost rate under different replacement policies.}, journal={JOURNAL OF ENERGY STORAGE}, author={Wang, Rui and Zhu, Mengmeng and Zhang, Xiangwu}, year={2024}, month={Mar} }
 @article{wang_zhu_zhang_pham_2023, title={Lithium-ion battery remaining useful life prediction using a two-phase degradation model with a dynamic change point}, volume={59}, ISSN={["2352-1538"]}, DOI={10.1016/j.est.2022.106457}, abstractNote={An accurate remaining useful life (RUL) prediction plays a crucial role in the prognostics and health management of lithium-ion (Li-ion) batteries. Current studies on the RUL prediction of Li-ion batteries commonly use single-phase degradation models, which result in inaccurate RUL predictions due to their insufficient capabilities in capturing various degradation patterns. The existing two-phase degradation models can divide battery degradation into two phases using a change point, a slowly decreasing phase, and a rapidly decreasing phase. The change point in the current two-phase degradation models is usually modeled in two ways. First, the change point is treated as a random variable and that however greatly increases the computational complexity. Second, a fixed change point is assigned for all battery cells for model simplification, which may not be realistic in practice. For example, battery cells' degradation data collected from our laboratory tests show a two-phase degradation pattern with different change points. By considering such differences in change points, this study first utilizes binary segmentation to identify the change point of a battery cell and then proposes a two-phase capacity degradation model with a dynamic change point. Further, variations have been observed in the degradation behaviors of tested battery cells. Therefore, by using the proposed two-phase degradation model, we develop a particle filtering-based framework considering uncertainties to predict the RULs of battery cells. Finally, the proposed framework shows superior prediction performance compared with the existing degradation models by providing the RUL prediction with an average absolute estimation error percentage of 27 % for laboratory data and an average absolute estimation error percentage of 24 % for NASA battery data.}, journal={JOURNAL OF ENERGY STORAGE}, author={Wang, Rui and Zhu, Mengmeng and Zhang, Xiangwu and Pham, Hoang}, year={2023}, month={Mar} }
 @article{hu_wang_zhu_chen_2023, title={Modeling Human-Machine Interaction System Reliability with Multiple Dependent Degradation Processes and Situation Awareness}, volume={7}, ISSN={["1793-6446"]}, DOI={10.1142/S0218539323500146}, abstractNote={With the advancement in automation, the roles of machine operators have shifted from traditional physical commitments to controlling the automation process. The operators need to understand and assess the incoming information and make decisions accordingly, which will determine the next move of the process and further affect the machine’s health status/performance. Such awareness and understanding of the situation, as one of the critical prerequisites of decision-making and known as situation awareness (SA), needs to be addressed appropriately in accessing the reliability of the system with human–machine interaction (HMI). Hence, we propose the HMI system reliability model considering the machine has multiple dependent degradation processes, and each degradation process is affected by the interactions of internal machine degradation, random shocks, and SA. In addition, the impacts of SA and external factors on the system are reflected in the amount and rate of the machine degradation process. The proposed model is demonstrated by a simulated case, and the sensitivity analysis is conducted to analyze the impacts of model parameters on system reliability prediction. Finally, the performance of the proposed model is demonstrated by comparing it with the existing model.}, journal={INTERNATIONAL JOURNAL OF RELIABILITY QUALITY AND SAFETY ENGINEERING}, author={Hu, Yuhan and Wang, Rui and Zhu, Mengmeng and Chen, Karen B. B.}, year={2023}, month={Jul} }
 @article{zhu_pham_2022, title={A generalized multiple environmental factors software reliability model with stochastic fault detection process}, volume={311}, ISSN={["1572-9338"]}, DOI={10.1007/s10479-020-03732-3}, number={1}, journal={ANNALS OF OPERATIONS RESEARCH}, author={Zhu, Mengmeng and Pham, Hoang}, year={2022}, month={Apr}, pages={525–546} }
 @article{zhu_2022, title={A new framework of complex system reliability with imperfect maintenance policy}, volume={312}, ISSN={["1572-9338"]}, DOI={10.1007/s10479-020-03852-w}, number={1}, journal={ANNALS OF OPERATIONS RESEARCH}, author={Zhu, Mengmeng}, year={2022}, month={May}, pages={553–579} }
 @article{wang_zhu_2022, title={Shock-Loading-Based Reliability Modeling with Dependent Degradation Processes and Random Shocks}, volume={29}, ISSN={["1793-6446"]}, DOI={10.1142/S0218539322500024}, abstractNote={In general, a system deteriorates due to internal physical degradation and external random shocks. Previous studies mainly focused on establishing dependent competing risk models to evaluate mutual effects of degradation processes and random shocks affecting system health. However, there is a lack of consideration about the magnitude of impacts caused by random shocks on degradation processes. Thus, a shock-loading-based degradation model is proposed to classify the magnitude of impacts from random shocks on degradation processes based on the threshold of the cumulative shock loading. Copula methods are utilized to derive joint reliability function from multiple marginal distributions of degradation processes. Two numerical examples are utilized to demonstrate the reliability prediction performance of the proposed model. First, a simulated example is used. The second example employs the turbofan engine degradation data from the NASA Prognostic Data Repository to show the performance of the proposed shock-loading-based degradation process and its corresponding system reliability model.}, number={03}, journal={INTERNATIONAL JOURNAL OF RELIABILITY QUALITY AND SAFETY ENGINEERING}, author={Wang, Rui and Zhu, Mengmeng}, year={2022}, month={Jun} }
 @article{zhu_huang_pham_2021, title={A Random-Field-Environment-Based Multidimensional Time-Dependent Resilience Modeling of Complex Systems}, volume={8}, ISSN={["2329-924X"]}, DOI={10.1109/TCSS.2021.3083515}, abstractNote={Over the past few decades, many research efforts have been dedicated to qualitatively and quantitatively evaluate resilience in different domains. As compared with research areas in social science and ecology, the concept of resilience in the engineering domain is relatively new. In the engineering domain, studies on resilience mostly focus on civil infrastructure. It is important to extend the concept of resilience to a broad range of engineering applications. The field environments of complex engineering systems vary with different applications. Even with the same component/system applied in different field environments, the ability, time, and resources required by failure detection, diagnosis, and restoration can be different. Hence, it is critical to introduce a new dimension, random field environment (RFE), into the development of the mathematical model for quantifying resilience. This article first introduces a new definition of resilience and then proposes a general RFE-based multidimensional time-dependent resilience model connecting reliability, vulnerability, and recoverability. Besides, we present a specific RFE-based multidimensional time-dependent resilience model by considering the specified functions of the impact of the RFE on system performance and recovery. Furthermore, we extend the proposed resilience model by incorporating multiple failure paths of complex systems. Finally, we apply the proposed resilience model to vehicular edge computing networks to evaluate the vehicular network resilience with the disruptive events on the communication links.}, number={6}, journal={IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS}, author={Zhu, Mengmeng and Huang, Xueqing and Pham, Hoang}, year={2021}, month={Dec}, pages={1427–1437} }
 @article{zhu_pham_2020, title={An Empirical Study of Factor Identification in Smart Health-Monitoring Wearable Device}, volume={7}, ISSN={["2329-924X"]}, DOI={10.1109/TCSS.2020.2967749}, abstractNote={Smart Health-Monitoring Wearable Device (SHMWD) is one of the solutions to improve health-care quality and accessibility through early detection and prevention. A comprehensive framework of factor identification of SHMWD from wide-ranging considerations is needed. Indeed, quantitative support of identifying significant factors/features affecting product rating and review needs to be studied as well. This article aims to identify 123 environmental factors (EFs) and their associated categories of SHMWD from various perspectives, determine the important EFs based on customers’ interest, and investigate the significant levels of EFs and each category on product rating, the significant EFs of each category, the correlation among EFs, and the principle components of the data set. Data analysis was conducted based on real data collected online ( $n = 769$ ). Statistical learning methods, including relative weighted method, analysis of variance, hypothesis testing, multiple regression analysis, backward elimination method, and principle component analysis, are employed to analyze the collected data. We also identify the top 15 important EFs which can be further incorporated in product development and maintenance. For researchers, this article points to the improvement directions of the current technologies applied in SHMWD as well as the new technologies to be implemented in the area of human–machine interactions. For practitioners, this article provides the managerial suggestions of time and resource allocation to the development team given each team may have different focused category and a general guide of feature selection for product development and maintenance and improvement for customer satisfaction.}, number={2}, journal={IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS}, author={Zhu, Mengmeng and Pham, Hoang}, year={2020}, month={Apr}, pages={404–416} }
 @article{zhu_pham_2019, title={A Novel System Reliability Modeling of Hardware, Software, and Interactions of Hardware and Software}, volume={7}, ISSN={["2227-7390"]}, DOI={10.3390/math7111049}, abstractNote={In the past few decades, a great number of hardware and software reliability models have been proposed to address hardware failures in hardware subsystems and software failures in software subsystems, respectively. The interactions between hardware and software subsystems are often neglected in order to simplify reliability modeling, and hence, most existing reliability models assumed hardware subsystems and software subsystem are independent of each other. However, this may not be true in reality. In this study, system failures are classified into three categories, which are hardware failures, software failures, and hardware-software interaction failures. The main contribution of our research is that we further classify hardware-software interaction failures into two groups: software-induced hardware failures and hardware-induced software failures. A Markov-based unified system reliability modeling incorporating all three categories of system failures is developed in this research, which provides a novel and practical perspective to define system failures and further improve reliability prediction accuracy. Comparison of system reliability estimation between the reliability models with and without considering hardware-software interactions is elucidated in the numerical example. The impacts on system reliability prediction as the changes of transition parameters are also illustrated by the numerical examples.}, number={11}, journal={MATHEMATICS}, author={Zhu, Mengmeng and Pham, Hoang}, year={2019}, month={Nov} }