Image-Based Prognostics Using Penalized Tensor Regression

Fang, Xiaolei; Paynabar, Kamran; Gebraeel, Nagi

doi:10.1080/00401706.2018.1527727

Cited by 31 publications

(6 citation statements)

References 36 publications

(44 reference statements)

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“…The authors use advanced dynamic identification techniques to process vibrations data coupled with deep learning methods to produce their final predictive model. , Fang et al, 2019 develops tensor-based methods for RUL prediction from streams of infrared images of bearings. When stacked, these images form a rank-3 tensor.…”

Section: Bearings Spinning and Cuttingmentioning

confidence: 99%

System-Level Predictive Maintenance: Review of Research Literature and Gap Analysis

Miller¹,

Dubrawski²

2020

Preprint

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This paper reviews current literature in the field of predictive maintenance from the system point of view. We differentiate the existing capabilities of condition estimation and failure risk forecasting as currently applied to simple components, from the capabilities needed to solve the same tasks for complex assets. System-level analysis faces more complex latent degradation states, it has to comprehensively account for active maintenance programs at each component level and consider coupling between different maintenance actions, while reflecting increased monetary and safety costs for system failures. As a result, methods that are effective for forecasting risk and informing maintenance decisions regarding individual components do not readily scale to provide reliable sub-system or system level insights. A novel holistic modeling approach is needed to incorporate available structural and physical knowledge and naturally handle the complexities of actively fielded and maintained assets.

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Section: Bearings Spinning and Cuttingmentioning

confidence: 99%

System-Level Predictive Maintenance: Review of Research Literature and Gap Analysis

Miller¹,

Dubrawski²

2020

Preprint

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“…Statistical modeling of spatio-temporal data arises from a spectrum of scientific and engineering applications, including environmental and natural processes (Stroud et al 2010, Guinness and Stein 2013, Liu et al 2016, Wikle 2019, Ezzat et al 2019, quality and reliability engineering (Liu et al 2018a, Yan et al 2018, Fang et al 2019, medical informatics (Yao et al 2017, Yang andQiu. 2018), and so on.…”

Section: Introductionmentioning

confidence: 99%

Physics-Informed Statistical Modeling for Wildfire Aerosols Process Using Multi-Source Geostationary Satellite Remote-Sensing Data Streams

Wei¹,

Krishnan²,

Xie³

et al. 2022

Preprint

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Increasingly frequent wildfires significantly affect solar energy production as the atmospheric aerosols generated by wildfires diminish the incoming solar radiation to the earth. Atmospheric aerosols are measured by Aerosol Optical Depth (AOD), and AOD data streams can be retrieved and monitored by geostationary satellites. However, multi-source remote-sensing data streams often present heterogeneous characteristics, including different data missing rates, measurement errors, systematic biases, and so on. To accurately estimate and predict the underlying AOD propagation process, there exist practical needs and theoretical interests to propose a physics-informed statistical approach for modeling wildfire AOD propagation by simultaneously utilizing, or fusing, multi-source heterogeneous satellite remote-sensing data streams. Leveraging a spectral approach, the proposed approach integrates multi-source satellite data streams with a fundamental advection-diffusion equation that governs the AOD propagation process. A bias correction process is included in the statistical model to account for the bias of the physics model and the truncation error of the Fourier series. The proposed approach is applied to California wildfires AOD data streams obtained from the National Oceanic and Atmospheric Administration. Comprehensive numerical examples are provided to demonstrate the predictive capabilities and model interpretability of the proposed approach. Computer code has been made available on GitHub.

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“…Many prognostic models have been developed in the literature, most of which focus on using time series-based degradation data Meeker, 2010, 2013;Shu et al, 2015;Liu et al, 2013;Gebraeel et al, 2005). Recently, prognostic models with imaging-based degradation data have been investigated and attracted more and more attention (Fang et al, 2019;Aydemir and Paynabar, 2019;Yang et al, 2021;Dong et al, 2021;Tang et al, 2021;Jiang et al, 2022). This is because comparing with time-series data, imaging data usually contains much richer information of the object being monitored, and imaging sensing technologies are noncontact and thus they can usually be easily deployed.…”

Section: Introductionmentioning

confidence: 99%

“…One example of imaging-based degradation data is the infrared image stream that measures the change of temperature distribution of a thrust bearing during its degradation process over time (Fang et al, 2019;Aydemir and Paynabar, 2019;Dong et al, 2021;Jiang et al, 2022). Another example is the images used to measure the performance degradation of infrared systems such as rotary-wing drones (Dong et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

A Supervised Tensor Dimension Reduction-Based Prognostics Model for Applications with Incomplete Imaging Data

Zhou¹,

Fang²

2022

Preprint

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Imaging data-based prognostic models focus on using an asset's degradation images to predict its time-to-failure (TTF). Most image-based prognostic models have two common limitations. First, they require degradation images to be complete (i.e., images are observed continuously and regularly over time). Second, they usually employ an unsupervised dimension reduction method to extract low-dimensional features, and then use the features for TTF prediction. Since unsupervised dimension reduction is conducted on the degradation images without the involvement of TTFs, there is no guarantee that the extracted features are effective for failure time prediction. To address these challenges, this article develops a supervised tensor dimension reduction-based prognostic model. The model first proposes a supervised dimension reduction method for tensor data. It uses historical TTFs to guide the detection of a tensor subspace to extract low-dimensional features from high-dimensional incomplete degradation imaging data. Next, the extracted features are used to construct a prognostic model based on (log)-location-scale regression. An optimization algorithm is proposed for parameter estimation, and closed-form solutions are derived under certain distributions. Simulated data and a real-world data set are used to validate the performance of the proposed model.

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Image-Based Prognostics Using Penalized Tensor Regression

Cited by 31 publications

References 36 publications

System-Level Predictive Maintenance: Review of Research Literature and Gap Analysis

System-Level Predictive Maintenance: Review of Research Literature and Gap Analysis

Physics-Informed Statistical Modeling for Wildfire Aerosols Process Using Multi-Source Geostationary Satellite Remote-Sensing Data Streams

A Supervised Tensor Dimension Reduction-Based Prognostics Model for Applications with Incomplete Imaging Data

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