Towards a Cloud-based Machine Learning for Health Monitoring and Fault Diagnosis



Samir Khan Takehisa Yairi Mariam Kiran


Large complex engineered systems collect large amounts of varied data sets, making it often difficult to process and analyze these for diagnosing, isolating, and predicting faults during operation. To recognize symptoms with standard testing tools, infer potential faults and eventually diagnose causes needs constant maintenance support. This problem is particularly faced in the aerospace industry, where it is essential to analyze and maintain assets to prevent potential failures or loss both technological and human. Recent usage of Cloud computing provides infinite computing resources to quickly process and troubleshoot, reducing ‘time-to-fix’ problems. Exploiting artificial intelligence (AI) algorithms, with Cloud resources, can help build an integrated fault diagnostic platform to provide resilient and scalable resources for data acquisition, processing and decision making. This paper presents an industrial perspective and problems when using machine learning methods for fault diagnosis, particularly using Cloud resources in the aerospace industry. Special attention is paid to the benefits; with potential future research on technical diagnosis being enumerated.

Abstract 26 | PDF Downloads 26




Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., & Ghemawat, S. (2016). Tensorflow: Largescale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467.
Buyya, R., Yeo, C. S., Venugopal, S., Broberg, J., & Brandic, I. (2009). Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Generation computer systems, 25(6), 599-616.
Cai, G., Gross, G., Llinas, J., & Hall, D. (2014, May). A visual analytic framework for data fusion in investigative intelligence. In SPIE Sensing Technology+ Applications (pp. 91220A-91220A). International Society for Optics and Photonics.
Campbell, J. D., & Reyes-Picknell, J. V. (2015). Uptime: Strategies for excellence in maintenance management. CRC Press.
Cao, W., Mecrow, B. C., Atkinson, G. J., Bennett, J. W., & Atkinson, D. J. (2012). Overview of electric motor technologies used for more electric aircraft (MEA). IEEE Transactions on Industrial Electronics, 59(9), 3523-3531.
Chen, C. P., & Zhang, C. Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275, 314-347.
Cope, G. and Kaufman, G., (2003). Flight data transmission via satellite link and ground storage of data. U.S. Patent Application 10/441,441.
Do, P., Voisin, A., Levrat, E., & Iung, B. (2015). A proactive condition-based maintenance strategy with both perfect and imperfect maintenance actions. Reliability Engineering & System Safety, 133, 22-32.
Foster, I., Zhao, Y., Raicu, I., & Lu, S. (2008, November). Cloud computing and grid computing 360-degree compared. In Grid Computing Environments Workshop, 2008. GCE'08 (pp. 1-10). Ieee.
Jazdi, N. (2014, May). Cyber physical systems in the context of Industry 4.0. In Automation, Quality and Testing, Robotics, 2014 IEEE International Conference on (pp. 1- 4). IEEE.
Jung, J. J. (2011). Service chain-based business alliance formation in service-oriented architecture. Expert Systems with Applications, 38(3), 2206-2211.
Khan, S. (2015). Maintenance requirements in aerospace systems. 4th International Conference in Through-life Engineering Services, Cranfield, UK November 2015.
Khan, S., Phillips, P., Jennions, I., & Hockley, C. (2014a). No Fault Found events in maintenance engineering Part 1: Current trends, implications and organizational practices. Reliability Engineering & System Safety, 123, 183-195.
Khan, S., Phillips, P., Hockley, C., & Jennions, I. (2014b). No Fault Found events in maintenance engineering Part 2: Root causes, technical developments and future research. Reliability Engineering & System Safety, 123, 196-208.
Khan, S., Phillips, P., Hockley, C., Jennions, I. (2015). No Fault Found: The search for the root cause. SAE publishing.
Kiran, M., Murphy, P., Monga, I., Dugan, J., & Baveja, S. S. (2015, October). Lambda architecture for cost-effective batch and speed big data processing. In Big Data (Big Data), 2015 IEEE International Conference on (pp. 2785-2792). IEEE.
Kwon, D., Hodkiewicz, M. R., Fan, J., Shibutani, T., & Pecht, M. G. (2016). IoT-based prognostics and systems health management for industrial applications. IEEE Access, 4, 3659-3670.
Lee, J., Kao, H. A., & Yang, S. (2014). Service innovation and smart analytics for industry 4.0 and big data environment. Procedia CIRP, 16, 3-8.
Lee, J., Bagheri, B., & Kao, H. A. (2015). A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manufacturing Letters, 3, 18-23.
Lightfoot, H., Baines, T., & Smart, P. (2013). The servitization of manufacturing: A systematic literature review of interdependent trends. International Journal of Operations & Production Management, 33(11/12), 1408-1434.
Michalski, R. S., Carbonell, J. G., & Mitchell, T. M. (Eds.). (2013). Machine learning: An artificial intelligence approach. Springer Science & Business Media.
Navarro, C. A., Hitschfeld-Kahler, N., & Mateu, L. (2014). A survey on parallel computing and its applications in data-parallel problems using GPU architectures. Communications in Computational Physics, 15(02), 285-329.
Othman, M., Madani, S. A., & Khan, S. U. (2014). A survey of mobile cloud computing application models. IEEE Communications Surveys & Tutorials, 16(1), 393-413.
Pascual, D., G. (2015). Artificial Intelligence Tools: Decision Support Systems in Condition Monitoring and Diagnosis. April 22, 2015 by CRC Press, Pages - 188, ISBN 9781466584051.
Roemer, M. J., Nwadiogbu, E. O., & Bloor, G. (2001). Development of diagnostic and prognostic technologies for aerospace health management applications. In Aerospace Conference, 2001, IEEE Proceedings. (Vol. 6, pp. 3139-3147). IEEE.
Sharma, A. K., Patel, S. K., & Gupta, G. (2013). Mobile Sensor Networks: A Review. Research Journal of Science and Technology, 5(3), 295-299.
Wan, J., Zhang, D., Zhao, S., Yang, L. T., & Lloret, J. (2014). Context-aware vehicular cyber-physical systems with cloud support: architecture, challenges, and solutions. IEEE Communications Magazine, 52(8), 106-113.
Womack, J. P., & Jones, D. T. (2015). Lean solutions: how companies and customers can create value and wealth together. Simon and Schuster.
Xu, X. (2012). From cloud computing to cloud manufacturing. Robotics and computer-integrated manufacturing, 28(1), 75-86.
Yasumoto, K., Yamaguchi, H., & Shigeno, H. (2016). Survey of real-time processing technologies of iot data streams. Journal of Information Processing, 24(2), 195-202.
Zio, E. (2009). Reliability engineering: Old problems and new challenges. Reliability Engineering & System Safety, 94(2), 125-141.
Regular Session Papers