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



Published Jul 14, 2017
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.

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