Building a Digital Twin Powered Intelligent Predictive Maintenance System for Industrial AC Machines

Abstract

Predictive maintenance is a system’s competency in distinguishing future scenarios where the machine is likely to fail and schedule repairs just prior to this happening. A heuristic technology to enable efficient predictive maintenance is digital twin technology. The development of a twin system between real-time machinery and the virtual world is made possible by digital twin technology, which is ideal for predictive maintenance. Induction motors, which are the core of industrial machinery, are sparsely represented in the digital twin domain. Therefore, this study created a digital twin of a squirrel cage induction motor, utilizing data-driven modeling and multiple physics, and integrated it with a custom predictive maintenance system. The purpose of this study is to implement digital twin technology for induction motors for fault diagnosis and predictive maintenance. This framework can extrapolate running parameters to presciently detect motor remaining useful lifetime as well as erratic fault diagnosis. The experimental setup for the 2.2 kW squirrel cage induction motor has been integrated into the digital workspace via the dSPACE MicroLabBox controller to allow frequent calibration and reference signal setup. The resultant digital framework deployed on MATLAB Simulink provided high accuracy without placing a great computational load on the processor. The proposed model’s commercial application may open the way for computational intelligence in Industry 4.0 adoption of induction motors.

Systematic review of predictive maintenance and digital twin technologies challenges, opportunities, and best practices

“…Notably, this study indicated that eight studies applied ML techniques for PdM through data-driven modelling across various sectors such as aeroplane maintenance, automobile brake pads, and induction motors ( Altun & Tavli, 2019 ; Avornu et al, 2022 ; Heim et al, 2020 ; Mubarak et al, 2022 ; Rajesh et al, 2019 ; Rossini et al, 2020 ; Siddiqui, Kahandawa & Hewawasam, 2023 ; Singh et al, 2023 ). Additionally, performance prediction studies have demonstrated the potential of ML as a useful technique.…”

“…8 , the percentage of PM in previous studies was 41%. The 14 studies involved in PM are Aivaliotis et al (2023) , Aivaliotis, Georgoulias & Chryssolouris (2019) , Aivaliotis et al (2019) , Altun & Tavli (2019) , Avornu et al (2022) , Centomo, Dall’Ora & Fummi (2020) , Heim et al (2020) , Liu et al (2019) , Mubarak et al (2022) , Rajesh et al (2019) , Rossini et al (2020) , Siddiqui, Kahandawa & Hewawasam (2023) , Singh et al (2023) and Yakhni et al (2022) . PMs also have drawbacks, such as being time-consuming and expensive to destroy and restore.…”

“…ML techniques such as naive Bayes, random forest, support vector machine (SVM), decision tree, and regression models are the most widely used AI approaches ( Khairuddin et al, 2022 , 2023 ; Neo et al, 2022 ), artificial neural network (ANN), and decision trees ( Hosamo et al, 2022 ) and ML is also used for Autoregressive Integrated Moving Average (ARIMA) ( Yang et al, 2021 ). From the review, 19 studies use ML approaches for PdM using DT ( Altun & Tavli, 2019 ; Avornu et al, 2022 ; Das et al, 2021 ; Harries et al, 2023 ; Heim et al, 2020 ; Hosamo et al, 2022 , 2023 ; Hu et al, 2023a ; Luo et al, 2020 ; Mourtzis, Tsoubou & Angelopoulos, 2023 ; Mubarak et al, 2022 ; Panagou et al, 2022a , 2022b ; Rajesh et al, 2019 ; Rossini et al, 2020 ; Siddiqui, Kahandawa & Hewawasam, 2023 ; Singh et al, 2023 ; Wang et al, 2022 ; Werner, Zimmermann & Lentes, 2019 ).…”