Browsing by Author "Wu, Jianguo"
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- A Deep Branched Network for Failure Mode Diagnostics and Remaining Useful Life PredictionLi, Zhen; Li, Yongxiang; Yue, Xiaowei; Zio, Enrico; Wu, Jianguo (IEEE, 2022-08)In complex systems, the operating units often suffer from multiple failure modes, and each failure mode results in distinct degradation path and service life. Thus, it is critical to perform the failure mode diagnostics and predict the remaining useful life (RUL) accordingly in modern industrial systems. However, most of the existing approaches consider the prognostic problem under a single failure mode or treat the failure mode classification and RUL prediction as two independent tasks, despite the fact that they are closely related and should be synergistically performed to enhance the generalization performance. Motivated by these issues, we propose a deep branched network (DBNet) for failure mode classification and RUL prediction. In this approach, the two tasks are jointly learned in a sequential manner, in which the feature extraction layers are shared by both tasks, while the neural network branches into individualized subnetworks for RUL prediction of each mode based on the output of the diagnostic subnetwork. Different from the traditional multitask learning-based methods, where the diagnostics and RUL prediction are performed in parallel, the proposed DBNet innovatively couples these two tasks sequentially to boost the prognostic accuracy. The effectiveness of the proposed method is thoroughly demonstrated and evaluated on an aircraft gas turbine engine with multiple failure modes.
- Failure-Averse Active Learning for Physics-Constrained SystemsLee, Cheolhei; Wang, Xing; Wu, Jianguo; Yue, Xiaowei (IEEE, 2022-10)Active learning is a subfield of machine learning that is devised for the design and modeling of systems with highly expensive sampling costs. Industrial and engineering systems are generally subject to physics constraints that may induce fatal failures when they are violated, while such constraints are frequently underestimated in active learning. In this paper, we develop a novel active learning method that avoids failures considering implicit physics constraints that govern the system. The proposed approach is driven by two tasks: safe variance reduction explores the safe region to reduce the variance of the target model, and safe region expansion aims to extend the explorable region. The integrated acquisition function is devised to conflate two tasks and judiciously optimize them. The proposed method is applied to the composite fuselage assembly process with consideration of material failure using the Tsai-Wu criterion, and it is able to achieve zero failure without the knowledge of explicit failure regions. Note to Practitioners—This paper is motivated by engineering systems with implicit physics constraints related to system failures. Implicit physics constraints refer to failure processes in which explicit analytic forms do not exist, so demanding numerical simulations or real experiments are required to check one’s safety. The main objective of this paper is to develop an active learning strategy that safely learns the target process in the system by minimizing failures without preliminary reliability analysis. The proposed method mainly targets real systems whose failure conditions are not thoroughly investigated or uncertain. We applied the proposed method to the predictive modeling of composite fuselage deformation in the aircraft manufacturing process, and it achieved zero failure in sampling by considering the composite failure criterion.
- Hierarchical Modeling of Microstructural Images for Porosity Prediction in Metal Additive Manufacturing via Two-point Correlation FunctionGao, Yuanyuan; Wang, Xinming; Son, Junbo; Yue, Xiaowei; Wu, Jianguo (Taylor & Francis, 2022-08)Porosity is one of the most critical quality issues in Additive Manufacturing (AM). As process parameters are closely related to porosity formation, it is vitally important to study their relationship for better process optimization. In this article, motivated by the emerging application of metal AM, a three-level hierarchical mixed-effects modeling approach is proposed to characterize the relationship between microstructural images and process parameters for porosity prediction and microstructure reconstruction. Specifically, a Two-Point Correlation Function (TPCF) is used to capture the morphology of the pores quantitatively. Then, the relationship between the TPCF profile and process parameters is established. A blocked Gibbs sampling approach is developed for parameter inference. Our modeling framework can reconstruct the microstructure based on the predicted TPCF through a simulated annealing optimization algorithm. The effectiveness and advantageous features of our method are demonstrated by both the simulation study and the case study with real-world data from metal AM applications.
- Online Structural Change-point Detection of High-dimensional Streaming Data via Dynamic Sparse Subspace LearningXu, Ruiyu; Wu, Jianguo; Yue, Xiaowei; Li, Yongxiang (Taylor & Francis, 2022-03-30)High-dimensional streaming data are becoming increasingly ubiquitous in many fields. They often lie in multiple low-dimensional subspaces, and the manifold structures may change abruptly on the time scale due to pattern shift or occurrence of anomalies. However, the problem of detecting the structural changes in a real-time manner has not been well studied. To fill this gap, we propose a dynamic sparse subspace learning approach for online structural change-point detection of high-dimensional streaming data. A novel multiple structural change-point model is proposed and the asymptotic properties of the estimators are investigated. A tuning method based on Bayesian information criterion and change-point detection accuracy is proposed for penalty coefficients selection. An efficient Pruned Exact Linear Time based algorithm is proposed for online optimization and change-point detection. The effectiveness of the proposed method is demonstrated through several simulation studies and a real case study on gesture data for motion tracking. Supplementary materials for this article are available online.
- Partitioned Active Learning for Heterogeneous SystemsLee, Cheolhei; Wang, Kaiwen; Wu, Jianguo; Cai, Wenjun; Yue, Xiaowei (ASME, 2023-08)Active learning is a subfield of machine learning that focuses on improving the data collection efficiency in expensive-to-evaluate systems. Active learning-applied surrogate modeling facilitates cost-efficient analysis of demanding engineering systems, while the existence of heterogeneity in underlying systems may adversely affect the performance. In this article, we propose the partitioned active learning that quantifies informativeness of new design points by circumventing heterogeneity in systems. The proposed method partitions the design space based on heterogeneous features and searches for the next design point with two systematic steps. The global searching scheme accelerates exploration by identifying the most uncertain subregion, and the local searching utilizes circumscribed information induced by the local Gaussian process (GP). We also propose Cholesky update-driven numerical remedies for our active learning to address the computational complexity challenge. The proposed method consistently outperforms existing active learning methods in three real-world cases with better prediction and computation time.
- A Shape-Constrained Neural Data Fusion Network for Health Index Construction and Residual Life PredictionLi, Zhen; Wu, Jianguo; Yue, Xiaowei (IEEE, 2021-11-01)With the rapid development of sensor technologies, multisensor signals are now readily available for health condition monitoring and remaining useful life (RUL) prediction. To fully utilize these signals for a better health condition assessment and RUL prediction, health indices are often constructed through various data fusion techniques. Nevertheless, most of the existing methods fuse signals linearly, which may not be sufficient to characterize the health status for RUL prediction. To address this issue and improve the predictability, this article proposes a novel nonlinear data fusion approach, namely, a shape-constrained neural data fusion network for health index construction. Especially, a neural network-based structure is employed, and a novel loss function is formulated by simultaneously considering the monotonicity and curvature of the constructed health index and its variability at the failure time. A tailored adaptive moment estimation algorithm (Adam) is proposed for model parameter estimation. The effectiveness of the proposed method is demonstrated and compared through a case study using the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) data set.