A Data-Driven Approach to Air Leakage Detection in Pneumatic Systems
Paper i 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.

Data-driven decision-making

Predictive maintenance

Pneumatic leakage

Artificial anomalies

Machine learning

Data labelling

Författare

Mohan Rajashekarappa

Student vid Chalmers

Johan Lene

Student vid Chalmers

Ebru Turanoglu Bekar

Chalmers, Industri- och materialvetenskap, Produktionssystem

Anders Skoogh

Chalmers, Industri- och materialvetenskap, Produktionssystem

Alexander Karlsson

Högskolan i 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,

Ämneskategorier

Produktionsteknik, arbetsvetenskap och ergonomi

Annan data- och informationsvetenskap

Övrig annan teknik

DOI

10.1109/PHM-Nanjing52125.2021.9612973

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Senast uppdaterat

2022-02-08