Maintenance Strategies for Industrial Multi-Stage Machines: The Study of a Thermoforming Machine

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.

An Approach to Zero-Failures Maintenance Using Industry 4.0 in Network Connected Multistage Industrial Machines

“…-Preventive maintenance [2] by individual maintenance times, as MTTF, MTBF, MTTR, and Time To Provisioning (TTRP) as the last required time to get a new component from the supplier. -Predictive maintenance [2] by machine new sensors assembly and digital behaviour twin (DBT) algorithm used for big data processing and warning advises.…”

“…-Preventive maintenance [2] by individual maintenance times, as MTTF, MTBF, MTTR, and Time To Provisioning (TTRP) as the last required time to get a new component from the supplier. -Predictive maintenance [2] by machine new sensors assembly and digital behaviour twin (DBT) algorithm used for big data processing and warning advises. Also, the machine has a procedure to evaluating the Global Operation Condition (GOC) of the components [1] and a methodology [3] to use predictive maintenance when a component fails, and the cause is unknow.…”

“…Preventive maintenance study global operation condition [1] and can perform the reliability and availability of the MSM, by using key performance indicators related to individual maintenance times of each component. Now, predictive maintenance strategies based on statistical, neuronal, and behavioural functioning pattern as digital behaviour twin, [2] are providing new ways to take decisions to avoid unexpected faults. MSM uses predictive maintenance based on machine distributed sensors and algorithms to provide warnings faults.…”

Digital twins for metrology; metrology for digital twins

“…Digital twins for maintenance [43] require the various faults that can occur within a machine to be identified and implemented within a model that can use measured data to estimate either the likelihood of a failure occurring within a given period or the expected time to the next failure. These estimations are likely to have similar statistical qualities to radioactive decay events, and may be able to benefit from data analysis techniques used to calculate half-lives [44].…”