@article{zhou_fang_2023, title={A convex two-dimensional variable selection method for the root-cause diagnostics of product defects}, volume={229}, ISSN={["1879-0836"]}, DOI={10.1016/j.ress.2022.108827}, abstractNote={Many multistage manufacturing processes consist of multiple identical stages. The root cause diagnostic of the product quality defects of these processes often involves the simultaneous identification of crucial stages and process variables that are related to product anomalies. In the literature, this is typically achieved by using penalized matrix regression that regresses the index of product defect against a matrix whose rows and columns respectively represent the stages and process variables. However, most existing models have some limitations that compromise their applicability and/or performance. For example, some models have an assumption on the rank of the coefficient, which often cannot be satisfied; some others formulate a nonconvex optimization criterion that easily results in a local optimum. Also, most models only provide diagnostics results with group-wise (i.e., stage- and variable-wise) sparsity. To address these challenges, this article proposes a novel convex two-dimensional variable selection method that can inspire both group-wise and element-wise sparsity. This is accomplished by proposing a new generalized matrix regression model and simultaneously penalizing the rows, columns, and elements of the regression coefficient matrix using an ℓ2, ℓ2, and ℓ1 norm, respectively. Simulated and real-world data are used to validate the effectiveness of the proposed method.}, journal={RELIABILITY ENGINEERING & SYSTEM SAFETY}, author={Zhou, Chengyu and Fang, Xiaolei}, year={2023}, month={Jan} }
 @article{jiang_xia_fang_wang_pan_xi_2023, title={Sparse Hierarchical Parallel Residual Networks Ensemble for Infrared Image Stream-Based Remaining Useful Life Prediction}, volume={19}, ISSN={["1941-0050"]}, DOI={10.1109/TII.2022.3229493}, abstractNote={Infrared thermography captures real-time degradation temperature information, facilitating noncontact machine health monitoring. However, the inherent multiscale characteristics and spatiotemporal degradation discrepancy in infrared images pose a challenge in learning discriminative degradation features and adaptive prognostic analytics. This article presents a sparse hierarchical parallel residual networks ensemble (SHPRNE) method to tackle this challenge. First, the hierarchical parallel residual network (HPRN) leverages parallel multiscale kernels to capture complementary degradation patterns separately and embeds a hierarchical residual connection procedure to facilitate the interactivity between coarse-to-fine level features. Moreover, SHPRNE develops a sparse ensemble algorithm integrated with a synergy of network pruning and local minima perpetuation to derive diverse HPRNs while alleviating the parameter storage budget. Pruned HPRNs with varying sparsity and local minima are further integrated into an ensemble learner with higher generalization. Case studies on two infrared image datasets are conducted to demonstrate the effectiveness and superiority of the proposed method.}, number={10}, journal={IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS}, author={Jiang, Yimin and Xia, Tangbin and Fang, Xiaolei and Wang, Dong and Pan, Ershun and Xi, Lifeng}, year={2023}, month={Oct}, pages={10613–10623} }
 @article{koprov_gadhwala_walimbe_fang_starly_2023, title={Systems and methods for authenticating manufacturing Machines through an unobservable fingerprinting system}, volume={35}, ISSN={2213-8463}, url={http://dx.doi.org/10.1016/j.mfglet.2023.08.051}, DOI={10.1016/j.mfglet.2023.08.051}, abstractNote={Digital transformation leads to the inevitable change in the security paradigm for machines on a factory production floor. A unified namespace for machines in an Industrial Internet of Things (IIoT) network is only reliable when machine assets can trust and verify the identity of assets connected to the IIoT system. Current methods of asset authentication do not consider physical unclonable functions (PUFs) and can easily be spoofed or misused. Our work proposes using PUFs for industrial equipment such as CNC machines, robots, and 3D printers for identifying machines on a network and providing authentication procedures. In this work, we chose to use the vibration associated with machines and its embedded moving parts as a means to identify machine assets on a network. It is hypothesized that the vibrations associated with specific machine movements will be unique to each machine even when machines look exactly the same. The moving parts within a machine may produce a unique vibration pattern that can be used for machine identification throughout the working cycle. Our method requires light computing and relatively cheap measuring devices to capture the ‘fingerprints’ of machines and verify the signal's integrity. An adequate number of equipment has been tested for the worst-case scenario, i.e. when two machines look exactly the same with the same moving parts and produce exactly similar motion to generate the vibration signal. Data preprocessing and standard machine learning techniques like RF, LASSO, and SVM show great performance on raw time series data, enabling 100% TPR and more than 94% TNR in detecting the false class of the machines.}, journal={Manufacturing Letters}, publisher={Elsevier BV}, author={Koprov, Pavel and Gadhwala, Shyam and Walimbe, Aniket and Fang, Xiaolei and Starly, Binil}, year={2023}, month={Aug}, pages={1009–1018} }
 @article{jiang_xia_wang_fang_xi_2022, title={Adversarial Regressive Domain Adaptation Approach for Infrared Thermography-Based Unsupervised Remaining Useful Life Prediction}, volume={18}, ISSN={["1941-0050"]}, DOI={10.1109/TII.2022.3154789}, abstractNote={Infrared thermography provides abundant spatiotemporal degradation information, facilitating non-contact condition monitoring. Reducing domain shift between simulated and industrial infrared images is significantly desired for leveraging labeled simulated data to tackle practical insufficiency of run-to-failure samples. Recently, adversarial-based domain adaptation (DA) techniques have aroused broad concern in solving domain shifts. However, simultaneously aligning marginal and conditional distributions in cross-domain remaining useful life (RUL) prediction is rarely researched in adversarial-based DA. In this article, an adversarial regressive domain adaptation (ARDA) approach is, thus, put forward to address this challenge. First, a regressive disparity discrepancy is designed to describe the dissimilarity between distributions and derive the generalization bound for cross-domain prognostics. Guided by this bound, the ARDA effectively aligns marginal and conditional distributions by learning indistinguishable features and considering the relationship between samples and prediction tasks. Simulated and experimental infrared degradation image datasets are used to demonstrate the effectiveness and superiority of the proposed approach over existing methods for cross-domain RUL prediction.}, number={10}, journal={IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS}, author={Jiang, Yimin and Xia, Tangbin and Wang, Dong and Fang, Xiaolei and Xi, Lifeng}, year={2022}, month={Oct}, pages={7219–7229} }
 @misc{lin_fang_gao_2022, title={DISTRIBUTIONALLY ROBUST OPTIMIZATION: A REVIEW ON THEORY AND APPLICATIONS}, volume={12}, ISSN={["2155-3297"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85121985630&partnerID=MN8TOARS}, DOI={10.3934/naco.2021057}, abstractNote={<p style='text-indent:20px;'>In this paper, we survey the primary research on the theory and applications of distributionally robust optimization (DRO). We start with reviewing the modeling power and computational attractiveness of DRO approaches, induced by the ambiguity sets structure and tractable robust counterpart reformulations. Next, we summarize the efficient solution methods, out-of-sample performance guarantee, and convergence analysis. Then, we illustrate some applications of DRO in machine learning and operations research, and finally, we discuss the future research directions.</p>}, number={1}, journal={NUMERICAL ALGEBRA CONTROL AND OPTIMIZATION}, author={Lin, Fengming and Fang, Xiaolei and Gao, Zheming}, year={2022}, month={Mar}, pages={159–212} }
 @article{jiang_xia_wang_fang_xi_2022, title={Spatiotemporal denoising wavelet network for infrared thermography-based machine prognostics integrating ensemble uncertainty}, volume={173}, ISSN={["1096-1216"]}, DOI={10.1016/j.ymssp.2022.109014}, abstractNote={Infrared thermography (IRT) is increasingly deployed in noncontact condition monitoring, and further facilitates the machinery remaining useful life (RUL) prediction. For IRT-based prognostics, the interpretability of degradation features, the spatiotemporal denoising capability, and the effectiveness of uncertainty quantification are crucial issues. Therefore, this paper proposes an ensemble of deep spatiotemporal denoising wavelet networks (EDSDWN). Firstly, a 4D wavelet convolution layer (WCL) is designed to capture crucial degradation-related features with meaningful physical interpretability based on multi-resolution analysis. Secondly, noises existent in features are further filtered out in a restoration process by a proposed deep image stream denoiser (DISD) block with residual learning. A base DSDWN consisting of the WCL, the DISD, and a regressor is comprehensively constructed. Ultimately, the effective uncertainty quantification is implemented by EDSDWN adaptively fitting a RUL density with a Student-t mixture distribution. EDSDWN is constructed by employing an automatically weighted algorithm to incorporate multiple base DSDWNs. Experimental results on a thermal image dataset acquired from rotating machinery and a simulated dataset have proven that EDSDWN is tailored to IRT-based machine prognostics. This proposed method implements considerably lower prediction errors and higher effectiveness of uncertainty quantification than several advanced prognostics models.}, journal={MECHANICAL SYSTEMS AND SIGNAL PROCESSING}, author={Jiang, Yimin and Xia, Tangbin and Wang, Dong and Fang, Xiaolei and Xi, Lifeng}, year={2022}, month={Jul} }
 @article{jeong_fang_2022, title={Two-dimensional variable selection and its applications in the diagnostics of product quality defects}, volume={54}, ISSN={["2472-5862"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85105408386&partnerID=MN8TOARS}, DOI={10.1080/24725854.2021.1904524}, abstractNote={The root cause diagnostics of product quality defects in multistage manufacturing processes often requires a joint identification of crucial stages and process variables. To meet this requirement, this article proposes a novel penalized matrix regression methodology for two-dimensional variable selection. The method regresses a scalar response variable against a matrix-based predictor using a generalized linear model. The unknown regression coefficient matrix is decomposed as a product of two factor matrices. The rows of the first factor matrix and the columns of the second factor matrix are simultaneously penalized to inspire sparsity. To estimate the parameters, we develop a Block Coordinate Proximal Descent (BCPD) optimization algorithm, which cyclically solves two convex sub-optimization problems. We have proved that the BCPD algorithm always converges to a critical point with any initialization. In addition, we have also proved that each of the sub-optimization problems has a closed-form solution if the response variable follows a distribution whose (negative) log-likelihood function has a Lipschitz continuous gradient. A simulation study and a dataset from a real-world application are used to validate the effectiveness of the proposed method.}, number={7}, journal={IISE TRANSACTIONS}, author={Jeong, Cheoljoon and Fang, Xiaolei}, year={2022}, month={Jul}, pages={619–629} }
 @article{dong_xia_wang_fang_xi_2021, title={Infrared image stream based regressors for contactless machine prognostics}, volume={154}, ISSN={["1096-1216"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85098994395&partnerID=MN8TOARS}, DOI={10.1016/j.ymssp.2020.107592}, abstractNote={In a practical production environment, machinery operators would consider using contactless sensing technology to monitor machinery degradation condition due to the concern of interference on production. A stream of time-series infrared images as a contactless sensing technology could capture the spatial and temporal information of a machinery degradation process. However, due to four dimensions (4D) of image stream tensor data, most existing remaining useful life (RUL) prediction methods are not capable of processing this kind of data. To fill this gap, image stream based regressors consisting of a neural network and multiple weighted time window policy are proposed. In the regressors, neural networks are utilized and trained to model the correlation between degradation image stream and its associated RUL. The networks are respectively designed based on some primary neural network blocks, including a fully-connected layer, long short-term memory network (LSTM) and convolutional neural network (CNN). To achieve the processing capability of 4D data, various input preprocessing means of raw image stream data are investigated. Meanwhile, to increase the prediction accuracy of the trained networks, a multiple weighted time window (MWTW) policy is developed. The policy aims to use whole monitoring data rather than a recent time window in existing neural network based RUL prediction methods. The proposed image stream based regressors are validated by using two datasets of degradation infrared images. Results showed that the regressors can predict RUL based on image stream well and MWTW policy has a significant effect on the increase of the prediction accuracy.}, journal={MECHANICAL SYSTEMS AND SIGNAL PROCESSING}, author={Dong, Yifan and Xia, Tangbin and Wang, Dong and Fang, Xiaolei and Xi, Lifeng}, year={2021}, month={Jun} }
 @article{xia_zhang_sun_fang_xi_2021, title={Integrated Remanufacturing and Opportunistic Maintenance Decision-Making for Leased Batch Production Lines}, volume={143}, ISSN={["1528-8935"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85103254308&partnerID=MN8TOARS}, DOI={10.1115/1.4049963}, abstractNote={Abstract}, number={8}, journal={JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME}, author={Xia, Tangbin and Zhang, Kaigan and Sun, Bowen and Fang, Xiaolei and Xi, Lifeng}, year={2021}, month={Aug} }
 @article{fang_yan_gebraeel_paynabar_2021, title={Multi-sensor prognostics modeling for applications with highly incomplete signals}, volume={53}, ISSN={["2472-5862"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85089457348&partnerID=MN8TOARS}, DOI={10.1080/24725854.2020.1789779}, abstractNote={Abstract Multi-stream degradation signals have been widely used to predict the residual useful lifetime of partially degraded systems. To achieve this goal, most of the existing prognostics models assume that degradation signals are complete, i.e., they are observed continuously and frequently at regular time grids. In reality, however, degradation signals are often (highly) incomplete, i.e., containing missing and corrupt observations. Such signal incompleteness poses a significant challenge for the parameter estimation of prognostics models. To address this challenge, this article proposes a prognostics methodology that is capable of using highly incomplete multi-stream degradation signals to predict the residual useful lifetime of partially degraded systems. The method first employs multivariate functional principal components analysis to fuse multi-stream signals. Next, the fused features are regressed against time-to-failure using (log)-location-scale regression. To estimate the fused features using incomplete multi-stream degradation signals, we develop two computationally efficient algorithms: subspace detection and signal recovery. The performance of the proposed prognostics methodology is evaluated using simulated datasets and a degradation dataset of aircraft turbofan engines from the NASA repository.}, number={5}, journal={IISE TRANSACTIONS}, author={Fang, Xiaolei and Yan, Hao and Gebraeel, Nagi and Paynabar, Kamran}, year={2021}, month={May}, pages={597–613} }
 @article{qian_fang_xu_li_2021, title={Multichannel profile-based monitoring method and its application in the basic oxygen furnace steelmaking process}, volume={61}, ISSN={["1878-6642"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85116566818&partnerID=MN8TOARS}, DOI={10.1016/j.jmsy.2021.09.010}, abstractNote={Many industrial processes are equipped with a large number of sensors, which usually generate multichannel high-dimensional profiles that can be used to monitor the health condition and detect anomalies of the processes. However, the data irregularity, information obscurity, complex correlations, and nonlinear structures of the multichannel data pose significant challenges for the development of anomaly detection methodologies. To address these challenges, this article proposes a method, Mahalanobis Distance-based Functional Derivative Support Vector Data Description (MD-FDSVDD), for the process monitoring of applications with multichannel profiles. The proposed method first estimates a smooth function of each profile from its irregularly acquired observations and then takes its derivative function to enhance the characteristics associated with anomalies. Next, the smoothed derivative functions are transformed based on Mahalanobis distance to address the strong linear correlation challenge. Finally, the transformed derivative data are used to construct a functional SVDD model to detect anomalies. The effectiveness of the proposed method is evaluated using a simulated dataset and a real-world dataset from a Basic Oxygen Furnace steelmaking process.}, journal={JOURNAL OF MANUFACTURING SYSTEMS}, author={Qian, Qingting and Fang, Xiaolei and Xu, Jinwu and Li, Min}, year={2021}, month={Oct}, pages={375–390} }
 @inproceedings{li_fang_2021, title={Multistream sensor fusion-based prognostics model for systems under multiple operational conditions}, volume={2}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85112534244&partnerID=MN8TOARS}, DOI={10.1115/MSEC2021-62348}, abstractNote={Abstract}, booktitle={Proceedings of the ASME 2021 16th International Manufacturing Science and Engineering Conference, MSEC 2021}, author={Li, X. and Fang, X.}, year={2021} }
 @article{li_gebraeel_lei_fang_cai_yan_2021, title={Remaining useful life prediction based on a multi-sensor data fusion model}, volume={208}, ISSN={["1879-0836"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85098208039&partnerID=MN8TOARS}, DOI={10.1016/j.ress.2020.107249}, abstractNote={With the rapid development of Industrial Internet of Things, more and more sensors have been used for condition monitoring and prognostics of industrial systems. Big data collected from sensor networks bring abundant information resources as well as technical challenges for remaining useful life (RUL) prediction. The major technical challenges include how to select informative sensors and fuse multi-sensor data to improve the prediction performance. To deal with the challenges, this paper proposes a RUL prediction method based on a multi-sensor data fusion model. In this method, the inherent degradation process of the system state is expressed using a state transition function following a Wiener process. Multi-sensor signals are explicated as various proxies of the inherent system degradation process using a multivariate measurement function. The system state is estimated by fusing multi-sensor signals using particle filtering. Informative sensors are selected by a prioritized sensor group selection algorithm. This algorithm first prioritizes sensors according to their individual performances in RUL prediction, and then selects an optimal sensor group based on their combined performances. The effectiveness of the proposed method is demonstrated using a simulation study and aircraft engine degradation data from NASA repository.}, journal={RELIABILITY ENGINEERING & SYSTEM SAFETY}, author={Li, Naipeng and Gebraeel, Nagi and Lei, Yaguo and Fang, Xiaolei and Cai, Xiao and Yan, Tao}, year={2021}, month={Apr} }
 @article{fang_paynabar_gebraeel_2019, title={Image-Based Prognostics Using Penalized Tensor Regression}, volume={61}, ISSN={["1537-2723"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85062797153&partnerID=MN8TOARS}, DOI={10.1080/00401706.2018.1527727}, abstractNote={ABSTRACT This article proposes a new methodology to predict and update the residual useful lifetime of a system using a sequence of degradation images. The methodology integrates tensor linear algebra with traditional location-scale regression widely used in reliability and prognostics. To address the high dimensionality challenge, the degradation image streams are first projected to a low-dimensional tensor subspace that is able to preserve their information. Next, the projected image tensors are regressed against time-to-failure via penalized location-scale tensor regression. The coefficient tensor is then decomposed using CANDECOMP/PARAFAC (CP) and Tucker decompositions, which enables parameter estimation in a high-dimensional setting. Two optimization algorithms with a global convergence property are developed for model estimation. The effectiveness of our models is validated using two simulated datasets and infrared degradation image streams from a rotating machinery.}, number={3}, journal={TECHNOMETRICS}, author={Fang, Xiaolei and Paynabar, Kamran and Gebraeel, Nagi}, year={2019}, month={Jul}, pages={369–384} }
 @article{xia_fang_gebraeel_xi_pan_2019, title={Online Analytics Framework of Sensor-Driven Prognosis and Opportunistic Maintenance for Mass Customization}, volume={141}, ISSN={["1528-8935"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85063871271&partnerID=MN8TOARS}, DOI={10.1115/1.4043255}, abstractNote={Abstract}, number={5}, journal={JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME}, author={Xia, Tangbin and Fang, Xiaolei and Gebraeel, Nagi and Xi, Lifeng and Pan, Ershun}, year={2019}, month={May} }
 @article{jeong_fang_2019, title={Penalized matrix regression for two-dimensional variable selection}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85098348719&partnerID=MN8TOARS}, journal={arXiv}, author={Jeong, C. and Fang, X.}, year={2019} }
 @article{dong_xia_fang_zhang_xi_2019, title={Prognostic and health management for adaptive manufacturing systems with online sensors and flexible structures}, volume={133}, ISSN={["1879-0550"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85065257741&partnerID=MN8TOARS}, DOI={10.1016/j.cie.2019.04.051}, abstractNote={Real-time monitoring and accurate predictions of machine failures are important in maintenance decision-making. Traditional policies using population-specific reliability characteristics cannot represent degradation processes of individual machines, thus result in less accurate predictions of time-to-failure (TTF). Besides, most of the existing maintenance policies focus on a manufacturing system with its fixed system structure, which means the system is designed with limited flexibility. Nowadays, the flexible structure of an adaptive manufacturing system can be adjustable to meet various product types and changeable market demands. In this paper, we try to fill these gaps and develop a prognostic and health management (PHM) framework for manufacturing systems with online sensors and flexible structures. We integrate a Bayesian updating prognostic model using sensor-based degradation information for computing each machine’s TTFs, with an opportunistic maintenance policy handling flexible system structures for optimizing the maintenance scheduling. This enables the dynamic prognosis updating, the notable cost reduction, and the rapid decision making for adaptive manufacturing systems.}, journal={COMPUTERS & INDUSTRIAL ENGINEERING}, author={Dong, Yifan and Xia, Tangbin and Fang, Xiaolei and Zhang, Zhenguo and Xi, Lifeng}, year={2019}, month={Jul}, pages={57–68} }
 @inproceedings{fang_paynabar_gebraeel_2018, title={Real-Time Predictive Analytics Using Degradation Image Data}, volume={2018-January}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85054152775&partnerID=MN8TOARS}, DOI={10.1109/RAM.2018.8463083}, abstractNote={Prognostics by using degradation image streams is challenging due to the high-dimensionality and complex spatial-temporal structure of images. In this paper, we proposed a penalized (log)-location-scale regression model that can utilize high dimensional tensors to predict the residual useful lifetime of systems. Our method first reduces the dimensionality of tensor covariates by projecting them onto a low tensor space. Next, the coefficient tensor is decomposed by utilizing CANDECOMP/PARAFAC (CP) decomposition. The decomposition facilitates further dimension reduction of coefficient tensors. By doing so, the number of parameters to be estimated is dramatically reduced. Furthermore, a numerical algorithm with global convergence property was developed for the model estimation.}, booktitle={Proceedings - Annual Reliability and Maintainability Symposium}, author={Fang, X. and Paynabar, K. and Gebraeel, N.}, year={2018} }
 @article{fang_paynabar_gebraeel_2017, title={Image-based prognostics using penalized tensor regression}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85092884816&partnerID=MN8TOARS}, journal={arXiv}, author={Fang, X. and Paynabar, K. and Gebraeel, N.}, year={2017} }
 @article{lease-oriented opportunistic maintenance for multi-unit leased systems under product- service paradigm_2017, volume={139}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85015190079&partnerID=MN8TOARS}, DOI={10.1115/1.4035962}, abstractNote={With many industries increasingly relying on leased equipment and machinery, many original equipment manufacturers (OEMs) are turning to product-service packages where they deliver (typically lease) the physical assets. An integrated service contract will be offered for the asset. A classic example being Rolls Royce power-by-the-hour aircraft engines. Service contracts offered by original equipment manufacturers have predominantly focused on maintenance and upkeep activities for a single asset. Interestingly enough, manufacturing industries are beginning to adopt the product-service paradigm. However, one of the unique aspects in manufacturing settings is that the leased system is often not a single asset but instead a multi-unit system (e.g., an entire production line). In this paper, we develop a lease-oriented maintenance methodology for multi-unit leased systems under product-service paradigm. Unlike traditional maintenance models, we propose a leasing profit optimization (LPO) policy to adaptively compute optimal preventive maintenance (PM) schedules that capture the following dynamics: (1) the structural dependencies of the multi-unit system, (2) opportunistic maintenance of multiple system components, and (3) leasing profit savings (LPSs). We demonstrate the performance of our multi-unit maintenance policy by using a leased automotive manufacturing line and investigate its impact on leasing profits.}, number={7}, journal={Journal of Manufacturing Science and Engineering, Transactions of the ASME}, year={2017} }
 @article{fang_paynabar_gebraeel_2017, title={Multistream sensor fusion-based prognostics model for systems with single failure modes}, volume={159}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85002741287&partnerID=MN8TOARS}, DOI={10.1016/j.ress.2016.11.008}, abstractNote={Advances in sensor technology have facilitated the capability of monitoring the degradation of complex engineering systems through the analysis of multistream degradation signals. However, the varying levels of correlation with physical degradation process for different sensors, high-dimensionality of the degradation signals and cross-correlation among different signal streams pose significant challenges in monitoring and prognostics of such systems. To address the foregoing challenges, we develop a three-step multi-sensor prognostic methodology that utilizes multistream signals to predict residual useful lifetimes of partially degraded systems. We first identify the informative sensors via the penalized (log)-location-scale regression. Then, we fuse the degradation signals of the informative sensors using multivariate functional principal component analysis, which is capable of modeling the cross-correlation of signal streams. Finally, the third step focuses on utilizing the fused signal features for prognostics via adaptive penalized (log)-location-scale regression. We validate our multi-sensor prognostic methodology using simulation study as well as a case study of aircraft turbofan engines available from NASA repository.}, journal={Reliability Engineering and System Safety}, author={Fang, X. and Paynabar, K. and Gebraeel, N.}, year={2017}, pages={322–331} }
 @article{fang_gebraeel_paynabar_2017, title={Scalable prognostic models for large-scale condition monitoring applications}, volume={49}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85020624886&partnerID=MN8TOARS}, DOI={10.1080/24725854.2016.1264646}, abstractNote={ABSTRACT High-value engineering assets are often embedded with numerous sensing technologies that monitor and track their performance. Capturing physical and performance degradation entails the use of various types of sensors that generate massive amounts of multivariate data. Building a prognostic model for such large-scale datasets, however, often presents two key challenges: how to effectively fuse the degradation signals from a large number of sensors and how to make the model scalable to the large data size. To address the two challenges, this article presents a scalable semi-parametric statistical framework specifically designed for synthesizing and combining multistream sensor signals using two signal fusion algorithms developed from functional principal component analysis. Using the algorithms, we identify fused signal features and predict (in near real-time) the remaining lifetime of partially degraded systems using an adaptive functional (log)-location-scale regression modeling framework. We validate the proposed multi-sensor prognostic methodology using numerical and data-driven case studies.}, number={7}, journal={IISE Transactions}, author={Fang, X. and Gebraeel, N.Z. and Paynabar, K.}, year={2017}, pages={698–710} }
 @article{fang_zhou_gebraeel_2015, title={An adaptive functional regression-based prognostic model for applications with missing data}, volume={133}, ISSN={["1879-0836"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84908407195&partnerID=MN8TOARS}, DOI={10.1016/j.ress.2014.08.013}, abstractNote={Most prognostic degradation models rely on a relatively accurate and comprehensive database of historical degradation signals. Typically, these signals are used to identify suitable degradation trends that are useful for predicting lifetime. In many real-world applications, these degradation signals are usually incomplete, i.e., contain missing observations. Often the amount of missing data compromises the ability to identify a suitable parametric degradation model. This paper addresses this problem by developing a semi-parametric approach that can be used to predict the remaining lifetime of partially degraded systems. First, key signal features are identified by applying Functional Principal Components Analysis (FPCA) to the available historical data. Next, an adaptive functional regression model is used to model the extracted signal features and the corresponding times-to-failure. The model is then used to predict remaining lifetimes and to update these predictions using real-time signals observed from fielded components. Results show that the proposed approach is relatively robust to significant levels of missing data. The performance of the model is evaluated and shown to provide significantly accurate predictions of residual lifetime using two case studies.}, journal={RELIABILITY ENGINEERING & SYSTEM SAFETY}, author={Fang, Xiaolei and Zhou, Rensheng and Gebraeel, Nagi}, year={2015}, month={Jan}, pages={266–274} }