The Germany-based IOTA Foundation has reached a milestone and becomes part of a major Japanese project. The project, supported by the Japanese government, will implement the world’s first predictive maintenance system for industrial factories based on artificial intelligence. Decentralized and secure data storage will be an important part of the project.
In this article we take a look at the role of the IOTA Foundation in the context of the project and briefly address the basic problem.
IOTA Foundation for an innovative technology project
Our report is based on a current article on the news portal JapanToday . This shows that a project supported by the Japanese government is to be implemented in close cooperation with the IOTA Foundation.
The project is financially supported by the „Japanese Organization for New Energies and Industrial Technology“. It is a national research institute that reports directly to the Japanese Ministry of Economic Affairs and Trade.
The subject of the project is the development of a predictive maintenance system for industrial plants. The aim of this system is to improve and extend the „durability“ of critical industrial plants. The system is used in sectors such as energy, manufacturing and the chemical sector.
A somewhat simpler formulation for the purpose of the project is as follows:
With the project, Japan would like to digitize data on the condition of machines and their maintenance. This data is evaluated by algorithms based on artificial intelligence. This allows predictions to be made when such machines and systems need maintenance.
Decentralized data storage based on the Tangle
The current status quo is problematic, as the data just described for the maintenance of systems has been saved locally up to now. This creates challenges with the cross-location sharing of information and the associated resource planning.
This is exactly where the IOTA Foundation comes in with its distributed ledger, namely the Tangle. The Tangle is used as a decentralized database to store the data just described. Artificial intelligence should carry out the evaluation of this data in order to be able to schedule employees as efficiently as possible for maintenance.
The replication of the data to different nodes and the associated distributed storage are characteristic of DLT. This consequently also leads to increased robustness against cyber attacks.