Industry 4.0 and all the technologies related to it start to be more and more presents in our world. The digital twin is part of those technologies and can be used in many cases.
According to the EFNMS (European Federation of National Maintenance Societies) the maintenance can be defined as : All actions which have the objective of retaining or restoring an item in or to a state in which it can perform its required function. The actions include the combination of all technical and corresponding administrative, managerial, and supervision actions.
Maintenance includes the actions of troubleshooting and repair, setting, overhaul, control, and verification of material equipment or even software. There are two main categories of the maintenance: corrective maintenance and preventive maintenance.
In case of a breakdown, the maintenance staff needs to react as fast as possible to restore the equipment to a state it can fulfil its function. The digital twin can help to diagnose the root cause of the problem. It can highlight the specific faulty location.
The staff also need in case of breakdown to access documentation which are not always stored in the same location. All those documents can be integrated in the digital twin, in the virtual representation of the equipment. This makes it easier and faster to reach the relevant documentation. There is no waste of time looking for the information.
Technicians can use augmented reality to be guided step by step in the operations to do. This guidance guarantees performance and safety during the task and avoid human mistakes. With digital twins, remote experts can help and guide the technicians through remote diagnostic. They can indeed access to all the relevant data from the digital twin and don’t necessarily need to come on site for that purpose.
It would be fair to say that digital twins can add value to corrective maintenance. In case of a breakdown, the access to the information (either the cause or the documentation) is facilitated with a digital twin. The downtime can therefore be minimized, which will allow to save both time and money.
Digital twins have the capability to help in the case of preventive maintenance as well, according to their level of maturity. In a first step, it is feasible to know the status of an equipment by collecting data. It is therefore possible to make condition-based maintenance by following critical data (such as speed, temperature, …), which allows to detect anomalies and to react before the breakdown. The digital twin can also be used to send alerts to operators and/or to provide reports of those critical data.
In a second step, it is possible to run simulations with those data. The digital twin can replicate the operation conditions, including the wear. It is thereby possible to predict failures and to anticipate interventions to replace the component(s) close to failure. The intervention downtime can then be planned and is not unexpected.
In the third step, the predictive maintenance can be achieved by adding some artificial intelligence to the digital twin. The twin itself will have the capability to predict failures and identify the cause of the failures. The remaining lifetime of an equipment can be estimated, based on the historical data and the future usage of the equipment.
In the last step, the artificial intelligence is even more present, and the digital twin takes itself the decisions. It can predict failures and take actions to avoid them. Depending on the case, the digital twin can react itself to restore the operations in case of breakdown. Even more, the digital twin can handle the replacement parts ordering based on the failure predictions.
In conclusion, the digital twin can add value to preventive maintenance. It is possible to monitor the equipment, even remotely and it helps to predict and diagnose problems. It is as well possible to predict the equipment lifetime through its current status, its breakdown history, and its usage (past, current and future).