A Predictive Maintenance Application for A Robot Cell using LSTM Model
Paper in proceeding, 2022

Maintaining equipment is critical for increasing production capacity and decreasing production time. With the advent of digitalization, industries are able to access massive amounts of data that can be used to ensure their long-term viability and competitive advantage by implementing predictive maintenance. Therefore, this study aims to demonstrate a predictive maintenance application for a robot cell using real-world manufacturing big data coming from a company in the automotive industry. A hyperparameter tuned Long Short-Term Memory (LSTM) model is developed, and the results show that this model is capable of predicting the day of failure with good accuracy. The difficulties inherent in conducting real-world industrial initiatives are analyzed, and recommendations for improvement are presented. Copyright (C) 2022 The Authors.

Manufacturing

CRISP-DM

Machine Learning

Predictive Maintenance

Long Short-Term Memory (LSTM)

Smart Maintenance

Industrial Robots

Author

Doyel Joseph

Student at Chalmers

Tilani Gallege

Student at Chalmers

Ebru Turanoglu Bekar

Chalmers, Industrial and Materials Science, Production Systems

Catarina Dudas

Volvo Cars

Anders Skoogh

Chalmers, Industrial and Materials Science, Production Systems

IFAC-PapersOnLine

2405-8963 (ISSN) 24058963 (eISSN)

Vol. 55 19 115-120

5th International-Federation-of-Automatic-Control (IFAC) Workshop on Advanced Maintenance Engineering, Services and Technologies (AMEST)
Bogota, Colombia,

Subject Categories

Production Engineering, Human Work Science and Ergonomics

Reliability and Maintenance

Robotics

DOI

10.1016/j.ifacol.2022.09.193

More information

Latest update

7/3/2024 1