Sparse Coding-Based Failure Prediction for Prudent Operation of LED Manufacturing Equipment
A sudden failure of a critical component in light-emitting diode (LED) manufacturing equipment would result in unscheduled downtime, leading to a possibly significant loss in productivity for the manufacturer. It is therefore important to be able to predict upcoming failures. A major obstacle to failure prediction is the limited amount of equipment lifecycle data available for training, as equipment failure is not expected to be frequent. This calls for machine learning techniques capable of making accurate failure predictions with limited training data. This paper describes such a method based on sparse coding. We demonstrate the prediction performance of the method on a real-world dataset from LED manufacturing equipment. We show that sparse coding can draw out salient features associated with failure cases, and can thus produce accurate failure predictions. We also analyze how sparse coding-based failure prediction can lead to significant efficiency improvements in equipment operation.
How to Cite
Sparse coding, Data-driven model, failure prediction
Chen H. C., Comiter M., Kung H. T., & McDanel B. (2015). Sparse Coding Trees with Application to Emotion Recognition, In Proceedings of IEEE Workshop on Analysis and Modeling of Faces and Gestures, pp. 77- 86.
Dietterich T. G. (2000). Ensemble Methods in Machine Learning, Multiple Classifier Systems, Lecture Notes in Computer Science, Vol. 1857, pp. 1-15.
Efron B., Hastie T., Johnstone I., & Tibshirani, R. (2004). Least angle regression, The Annals of Statistics, Vol. 32, No. 2, pp. 407-499.
Huang K., & Aviyente S. (2006). Sparse Representation for Signal Classification, Advances in Neural Information Processing Systems, Vol. 19, pp. 131-138.
Lee J., Wu F., Zhao W., Ghaffari M., Liao L., & Siegel D. (2014). Prognostics and Health Management Design for Rotary Machinery Systems—Reviews, Methodology and Applications, Mechanical Systems and Signal Processing, Vol. 42, pp. 314-334.
Koh K., Kim S. J., & Boyd S. (2007). An Interior-Point Method for Large-Scale l1-Regularized Logistic Regression, The Journal of Machine Learning Research, Vol. 8, pp.1519-1555.
Mairal J., Bach F., Ponce J., & Sapiro G. (2009). Online Dictionary Learning for Sparse Coding, In Proceedings of the 26th Annual International Conference on Machine Learning, pp. 689-696.
Mairal J., Bach F., Ponce J., & Sapiro G. (2010). Online Learning for Matrix Factorization and Sparse Coding, The Journal of Machine Learning Research, Vol. 11, pp. 19-60.
Orchard M. E., & Vachtsevanos G. J. (2007). A Particle Filtering-based Framework for Real-time Fault Diagnosis and Failure Prognosis in a Turbine Engine, Mediterranean Conference on Control and Automation, pp. 1-6.
Saxena A., Roychoudhury I., Celaya J., Saha S., Saha B., & Goebel K. (2010). Requirements Specifications for Prognostics: An Overview, AIAA Infotech@Aerospace Conference.
Salfner F., Lenk M., & Malek M. (2010). A survey of Online Failure Prediction Methods, ACM Computing Surveys, Vol. 42, No. 3, pp. 1-42.
Scoville J. (2011). Predictability as a Key Component of Productivity, ISMI Manufacturing Week 2011, Austin, TX.
Tarsa S. J., Comiter M., Crouse M., McDanel B., & Kung H. T. (2015). Taming Wireless Fluctuations by Predictive Queuing Using a Sparse-Coding Link-State Model, ACM MobiHoc, pp. 287-296.
Wright J., Yang A. Y., Ganesh A., Sastry S. S., & Yi M. (2009). Robust Face Recognition via Sparse Representation, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 31, No. 2, pp. 210-227.
Wright J., Yi M., Mairal J., Sapiro G., Huang, T. S., & Yan S. (2010). Sparse Representation for Computer Vision and Pattern Recognition, Proceedings of the IEEE, Vol. 98, No. 6, pp. 1031-1044.
Zhu J., Nostrand T., Spiegel C., & Morton B. (2014). Mechanical Diagnostics System Engineering in IMS HUMS, In Proceedings of Annual Conference of the Prognostics and Health Management Society, pp. 635- 647.
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