A data-driven approach to detect air leakage in a pneumatic system
Typ
Examensarbete för masterexamen
Program
Production engineering (MPPEN), MSc
Publicerad
2021
Författare
Lené, Johan
Rajashekarappa, Mohan
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Maintenance practices in production systems have for the past decades followed a reactive
approach. With the production domain currently shifting towards a new era,
Industry 4.0, maintenance practises have experienced an incremental drift towards
predictive approach. With key enabling technologies such as BigData, Industrial
Digitalization, and Machine Learning that enables data-driven decision making, the
field of production is now more ready than ever to establish new practises in maintenance.
The fast-emerging area of data-driven decision making with key enabling
technologies is where this thesis finds its roots.
This thesis work aims to develop a data driven approach based on machine learning
to identify an early stage of a pneumatic leakage in a production process, before the
leakage is so severe that it would impact the process adversely and cause machine
breakdown. Through two experiments and extraction of historical data coming from
sensors, a supervised machine learning model was built on the extracted significant
statistical features. Adding on, an unsupervised model was developed to distinguish
separate clusters representing the normal working state and leaking state of the machine
for another process in the same production line.
The supervised machine learning model is successful in detecting the early stage of
leakage with an accuracy of 98.2%, thus giving plenty of time to perform appropriate
maintenance on the equipment. The unsupervised model with an accuracy of 99.3%,
is successful in detecting the physically evident stage of leakage. The presented
thesis makes salient recommendations for deployment of this model. Moreover, the
provided valuable insights pertaining to features which were previously believed to
be insignificant for modelling purposes, provide a standardized work methodology
and a concrete platform for future study in the area.
Beskrivning
Ämne/nyckelord
Machine learning, data driven decision making, predictive maintenance, , pneumatic leakage, sensor data