Proposing a streaming Big Data analytics (SBDA) platform for condition based maintenance (CBM) and monitoring transportation systems

Authors

  • Jamal Maktoubian International School Of Information Management

DOI:

https://doi.org/10.4108/eai.28-6-2017.152750

Keywords:

Condition-Based Maintenance (CBM), Apache Kafka, Spark Streaming, Real-Time Data, Sensor, Streaming Big Data Analysis (SBDA)

Abstract

Statistics demonstrate that public transportation plays a significant role in people’s movement in metropolises. However, transit systems are aging and are facing rising maintenance costs. Technologies such as Condition-Based Maintenance (CBM) could be used in order to monitor performance conditions of transportation and industrial assets in real-time to detect when and what maintenance is required. CBMs could help to identify risk scenarios in real-time, enhance reliability, reduce call out costs, increase productivity, and better asset functioning visibility. Since the high volume of maintenance data is generated from the different source, managing assets conditions with traditional inspection system such as planned maintenance (PM) is impossible. Therefore, providing a comprehensive performance management program is essential. My research is motivated by interesting challenges increasing from the growing size, variety, and complexity of maintenance data in CBM systems. This paper presents a knowledge-based approach of CBM using streaming big data analysis (SBDA) in order to solve real-time big data management, storage and computation challenges and predictive data analytics in CBM systems. This platform could detect changes in asset’s behavior before they stop.

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Published

28-06-2017

How to Cite

1.
Maktoubian J. Proposing a streaming Big Data analytics (SBDA) platform for condition based maintenance (CBM) and monitoring transportation systems. EAI Endorsed Scal Inf Syst [Internet]. 2017 Jun. 28 [cited 2024 Nov. 22];4(13):e4. Available from: https://publications.eai.eu/index.php/sis/article/view/2241