ENBIS Spring Meeting 2019 in Barcelona
13 – 14 June 2019
Abstract submission: 15 January – 20 May 2019
Statistical engineering challenges from big data in industry
13 June 2019, 15:35 – 16:00
- Submitted by
- Biagio Palumbo
- Biagio Palumbo (Univ. of Naples Federico II), Antonio Lepore (Univ. of Naples Federico II)
- Statistical Engineering is based on a sequential learning strategy that aims to provide great value to industrial companies through the development and execution of ad-hoc tactics and final solutions. Achieving this goal in industry requires the ability to merge the proper blend of subject matter skills and the smart application of the available tools together.
The talk will focus on presenting scenarios where the authors have witnessed the virtuous integration of technological skills, traditional statistical methods and modern analytics to face challenges from complex industrial contexts.
In particular, the first scenario regards the CO2 emission prediction and monitoring problem in the shipping industry that was triggered by the recent EU regulation 2015/757 which came into force in January 2018. Shipping operators have installed on-board large-scale multi-sensor systems for daily monitoring, reporting and verification (MRV) of CO2 emissions for their individual ship. The resulting huge amount of navigation data available has opened up new challenges to translate the statistical engineering paradigm into this context.
The second scenario regards the manufacturing industry, in the view of the current revolution and modern sensing technologies that naturally benefit from the cross-fertilization of traditional statistical quality control and experiment analysis procedures with modern analytics and data visualization tools. In particular, we aim to provide value to the context of modern additive manufacturing processes, according to the statistical engineering paradigm.
The third scenario concerns the high-speed rail industry that is acting as a catalyst for industry growth. To improve the performance of critical systems, railway companies have been collecting and storing data to monitor rolling stock, understand his behaviour, and improve his reliability, maintenance and safety. In particular, we introduce a statistical approach for detecting anomalies in critical systems and to allow operators to take prompt actions to reduce, or even avoid, the occurrence of failures.
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