Maintenance Action Recommendation Using Collaborative Filtering



Published Nov 1, 2020
Santanu Das


The problem we were trying to solve in 2013 PHM Society Conference Data Challenge competition 1 is closely related to remote monitoring and diagnostics in industrial applications. This data was generated from an industrial piece of equipment with a sensor network to measure several parameters and an onboard condition monitoring system. The measured data goes through a control logic in order to monitor the equipment’s operating regime. At any time instant when some of these parameters meet a specific condition, the control system generates an unique event id/code. Each case is described by a set of event codes which characterize the atypical operating condition of the equipment. Some of these cases with specific event code combinations may be operationally significant and could be indicative of “Problem Types”, some of which are assumed to be known to the subject matter experts. As a response to these problems, domain experts recommend appropriate diagnostic measures (or maintenance actions) depending on the problem types. The goal of this data competition is to build an automated system that can recommend particular maintenance action(s) to mitigate these problem(s).

Abstract 181 | PDF Downloads 339



filtering, diagnostics and prognostics, PHM, monitoring

Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 17(6), 734–749.
Berry, M. W., Browne, M., Langville, A. N., Pauca, V. P., & Plemmons, R. J. (2006). Algorithms and applications for approximate nonnegative matrix factorization. In Computational statistics and data analysis (pp. 155–173).
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. J. Mach. Learn. Res., 3, 993–1022.
Cichocki, A., & Zdunek, R. (2007). Regularized Alternating Least Squares Algorithms for Non-negative Matrix/ Tensor Factorization. In (pp. 793–802).
Condliff, M. K., Lewis, D. D., & Madigan, D. (1999). Bayesian mixed-effects models for recommender systems. In In acm sigir 99 workshop on recommender systems: Algorithms and evaluation.
Hoyer, P. (2002). Non-negative sparse coding. In Neural networks for signal processing, 2002. proceedings of the 2002 12th ieee workshop on (p. 557-565).
Hoyer, P. (2004, December). Non-negative matrix factorization with sparseness constraints. J. Mach. Learn. Res., 5, 1457–1469.
Koren, Y., Bell, R., & Volinsky, C. (2009, August). Matrix factorization techniques for recommender systems. Computer, 42(8), 30–37. doi: 10.1109/MC.2009.263
Koren, Y., & Bell, R. M. (2011). Advances in collaborative filtering. In Recommender systems handbook (p. 145- 186).
Lee, D. D., & Seung, H. S. (2001). Algorithms for nonnegative matrix factorization. In In nips (pp. 556–562). MIT Press.
Papineni, K. (2001). Why inverse document frequency. In Proceedings of the naacl (pp. 25–32).
Srivastava, A., & Buntine, W. (1995). Data analysis of components in the optical plume of the space shuttle main engine. In Proceedings of the aiaa.
Su, X., & Khoshgoftaar, T. M. (2009, January). A survey of collaborative filtering techniques. Adv. in Artif. Intell., 2009, 4:2–4:2.
Wold, S., Esbensen, K., & Geladi, P. (1987). Principal component analysis. Chemometrics and Intelligent Laboratory Systems, 2(13), 37 - 52.
Technical Papers