Energy-Based Prognostics for Gradual Loss of Conveyor Belt Tension in Discrete Manufacturing Systems

Mahboob Elahi, Samuel Olaiya Afolaranmi, Wael M. Mohammed and Jose Luis Martinez Lastra
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Mahboob Elahi: FAST-Lab., Faculty of Engineering and Natural Sciences, Tampere University, 33720 Tampere, Finland
Samuel Olaiya Afolaranmi: FAST-Lab., Faculty of Engineering and Natural Sciences, Tampere University, 33720 Tampere, Finland
Wael M. Mohammed: FAST-Lab., Faculty of Engineering and Natural Sciences, Tampere University, 33720 Tampere, Finland
Jose Luis Martinez Lastra: FAST-Lab., Faculty of Engineering and Natural Sciences, Tampere University, 33720 Tampere, Finland

Energies, 2022, vol. 15, issue 13, 1-18

Abstract: This paper presents a data-driven approach for the prognosis of the gradual behavioural deterioration of conveyor belts used for the transportation of pallets between processing workstations of discrete manufacturing systems. The approach relies on the knowledge of the power consumption of a conveyor belt motor driver. Data are collected for two separate cases: the static case and dynamic case. In the static case, power consumption data are collected under different loads and belt tension. These data are used by a prognostic model (artificial neural network (ANN)) to learn the conveyor belt motor driver’s power consumption pattern under different belt tensions and load conditions. The data collected during the dynamic case are used to investigate how the belt tension affects the movement of pallets between conveyor zones. During the run time, the trained prognostic model takes real-time power consumption measurements and load information from a testbench (a discrete multirobot mobile assembling line) and predicts a belt tension class. A consecutive mismatch between the predicted belt tension class and optimal belt tension class is an indication of failure, i.e., a gradual loss of belt tension. Hence, maintenance steps must be taken to avoid further catastrophic situations such as belt slippages on head pulleys, material slippages and belt wear and tear.

Keywords: energy-based prognostics model; predictive maintenance; power consumption; conveyor belt deterioration; belt tension; discrete manufacturing systems (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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