A Data-Driven Approach to Air Leakage Detection in Pneumatic Systems
Paper in proceeding, 2021

During the transition phase of traditional manufacturing companies towards smart factories, they are likely to experience challenges like lack of prehistoric data recordings or events on which the machine learning models need to be trained. This paper introduces a novel approach of artificially induced anomalies for data labelling. Moreover, for newly installed systems or a setup, which has not seen any kind of malfunction yet, the combination of artificially induced anomalies by experiments and machine learning model help to proactively prepare for any kind of future hindrance of the production systems. Two experiments were performed for detection of air leakage. The first one was designed to identify 'sensitive feature' and understand the behaviour of the sensor readings with respect to different state of the machine. The second one was performed to capture more data points pertaining to leaking state of machine on a normal production day since the first one was conducted on a maintenance break). RUSBoosted bagged trees model was built as a supervised machine-learning model, which was yielded 98.73% accuracy, 99.40% precision, recall of 99.21%, and F1 score of 99.30% on test data for detecting pneumatic leakage. As a conclusion, previously unknown hidden patterns and insights regarding temperature feature along with a standardized and systematic methodology are the important deliverables of this study.

Artificial anomalies

Machine learning

Pneumatic leakage

Data labelling

Predictive maintenance

Data-driven decision-making


Mohan Rajashekarappa

Student at Chalmers

Johan Lene

Student at Chalmers

Ebru Turanoglu Bekar

Chalmers, Industrial and Materials Science, Production Systems

Anders Skoogh

Chalmers, Industrial and Materials Science, Production Systems

Alexander Karlsson

University of Skövde

2021 Global Reliability and Prognostics and Health Management, PHM-Nanjing 2021

9781665401302 (ISBN)

12th IEEE Global Reliability and Prognostics and Health Management, PHM-Nanjing 2021
Nanjing, China,

Predictive Maintenance using Advanced Cluster Analysis (PACA)

VINNOVA (2019-00789), 2019-03-01 -- 2022-02-28.

Subject Categories

Production Engineering, Human Work Science and Ergonomics

Other Computer and Information Science

Other Engineering and Technologies not elsewhere specified

Driving Forces

Sustainable development

Areas of Advance




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