Application of Machine Learning and Spatial Bootstrapping to Image Processing for Predictive Maintenance. Krishnamurthy, V., Nezafati, K., & Singh, V. In 2019 IEEE International Conference on Big Data (Big Data), pages 4395–4401, December, 2019. doi abstract bibtex Image processing and machine learning have become valuable tools for predictive maintenance applications for a wide variety of industrial and commercial components. We present a novel light transmission image processing methodology utilizing statistical distance algorithms (Wasserstein distance (WD), Kolmogorov-Smirnov statistic (K-S)) for physical attribute correlation combined with Bayesian linear regression to estimate wear level and lifetime prediction for air filters. Robustness of this machine learning algorithm was evaluated using spatial block bootstrapping to generate synthetic training data to estimate the 95% prediction interval for air filter lifetime. Validation of this lifetime prediction was performed using imaging measurements on a test air filter, which showed good agreement with the machine learning model. The proposed machine learning based image analytics framework effectively enables robust predictions of component wear for predictive maintenance.
@inproceedings{krishnamurthy_application_2019,
title = {Application of {Machine} {Learning} and {Spatial} {Bootstrapping} to {Image} {Processing} for {Predictive} {Maintenance}},
doi = {10.1109/BigData47090.2019.9006439},
abstract = {Image processing and machine learning have become valuable tools for predictive maintenance applications for a wide variety of industrial and commercial components. We present a novel light transmission image processing methodology utilizing statistical distance algorithms (Wasserstein distance (WD), Kolmogorov-Smirnov statistic (K-S)) for physical attribute correlation combined with Bayesian linear regression to estimate wear level and lifetime prediction for air filters. Robustness of this machine learning algorithm was evaluated using spatial block bootstrapping to generate synthetic training data to estimate the 95\% prediction interval for air filter lifetime. Validation of this lifetime prediction was performed using imaging measurements on a test air filter, which showed good agreement with the machine learning model. The proposed machine learning based image analytics framework effectively enables robust predictions of component wear for predictive maintenance.},
booktitle = {2019 {IEEE} {International} {Conference} on {Big} {Data} ({Big} {Data})},
author = {Krishnamurthy, Vikram and Nezafati, Kusha and Singh, Vikrant},
month = dec,
year = {2019},
keywords = {Image Processing, Imaging, Kolmogorov-Smirnov statistic, Machine Learning, Machine learning, Measurement, Predictive Maintenance, Predictive maintenance, Sensors, Spatial Bootstrapping, Training, Training data, Wasserstein Distance},
pages = {4395--4401},
}
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Robustness of this machine learning algorithm was evaluated using spatial block bootstrapping to generate synthetic training data to estimate the 95% prediction interval for air filter lifetime. Validation of this lifetime prediction was performed using imaging measurements on a test air filter, which showed good agreement with the machine learning model. 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