Abstract
The study of reliability, availability and control of industrial manufacturing machines is a constant challenge in the industrial environment. This paper compares the results offered by several maintenance strategies for multi-stage industrial manufacturing machines by analysing a real case of a multi-stage thermoforming machine. Specifically, two strategies based on preventive maintenance, Preventive Programming Maintenance (PPM) and Improve Preventive Programming Maintenance (IPPM) are compared with two new strategies based on predictive maintenance, namely Algorithm Life Optimisation Programming (ALOP) and Digital Behaviour Twin (DBT). The condition of machine components can be assessed with the latter two proposals (ALOP and DBT) using sensors and algorithms, thus providing a warning value for early decision-making before unexpected faults occur. The study shows that the ALOP and DBT models detect unexpected failures early enough, while the PPM and IPPM strategies warn of scheduled component replacement at the end of their life cycle. The ALOP and DBT strategies algorithms can also be valid for managing the maintenance of other multi-stage industrial manufacturing machines. The authors consider that the combination of preventive and predictive maintenance strategies may be an ideal approach because operating conditions affect the mechanical, electrical, electronic and pneumatic components of multi-stage industrial manufacturing machines differently.
Highlights
The industrial production environment is becoming increasingly competitive, reliable and optimised
Table 1); Proposition of individual maintenance times per component, as well as equations for calculating reliability, efficiency and availability; Proposition and location of appropriate sensors whose values are associated with the proper functioning of the components; Proposition and development of algorithms for Algorithm Life Optimisation Programming (ALOP) and Digital Behaviour Twin (DBT) strategies; Location of a master linear axis for the case of DBT, by means of which the study is related to the position of the encoder and subsequently converted to units of time; Configuration of the Programmable Logic Controller (PLC) datalogger function and record all the relevant values in each strategy; Recording of the failures and errors detected in ALOP and DBT; Evaluation of the results obtained
The ALOP and DBT strategies have been tested on the multi-stage thermoforming machine working continuously 8 h a day, Monday to Friday, for a year
Summary
The industrial production environment is becoming increasingly competitive, reliable and optimised. IPPM is used for all components or for components with a high replenishment time This is a proposed maintenance strategy that aims to improve the maintenance of the machines by making decisions based on analysing sensor signals and a predictive algorithm of the state of the most relevant components. In manufacturing multi-stage machines, DBT allows the study of new strategies based on collecting and processing data and defining standard behaviour patterns, which are compared with real behaviours. This strategy provides essential information for decision-making based on the analysis of current behaviour and comparison of sensor readings. Few references dedicated to maintenance management in industrial manufacturing multi-stage machines have been found during the search for references
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
