AI enables the detection and diagnosis of faults in electrical systems by analysing real-time sensor data and historical information. Advanced AI algorithms identify abnormal behaviour, predict potential failures, and generate early warnings, allowing companies to move from reactive to predictive maintenance strategies. This results in reduced downtime, improved system reliability, and optimised maintenance costs.
To ensure reliable deployment, validating these algorithms under realistic conditions is essential. This service provides a comprehensive validation framework combining virtual prototyping, digital twins, and experimental testing. It allows companies to assess and optimise AI-based fault detection and diagnosis solutions before integration into real systems.
Validation is carried out using digital twins specifically developed for the target electrical system and algorithm. These models are implemented using advanced simulation tools and enable the reproduction of fault scenarios, degradation processes, and varying operating conditions in a controlled environment.
In addition, experimental testing is performed through the development of dedicated test setups and real prototypes. These tests replicate real electrical faults and ageing conditions, enabling the evaluation of algorithm performance in realistic scenarios and ensuring robustness and reliability.
The service follows a structured process:
– Definition of the use case, system characteristics, and fault scenarios
– Collection and analysis of available data (e.g. sensor data, operational history)
– Development of digital twins and simulation environments
– Integration and validation of AI algorithms in virtual scenarios
– Experimental validation using physical test setups and prototypes
– Performance analysis and optimisation recommendations
Performance is evaluated using measurable indicators such as fault detection accuracy, false positive rate, response time, prediction horizon, and impact on system availability. Typical outcomes include significant reductions in unplanned downtime, improved fault detection rates, and enhanced maintenance efficiency.
Typical applications include electric motors, power converters, battery systems, and grid-connected equipment. For example, manufacturers of electric drives can improve fault diagnosis in motors, while power electronics companies can enhance reliability in converters and inverters.
To carry out the service, customers are expected to provide access to their AI algorithms, system specifications, and available datasets (e.g. sensor measurements, failure records). Where required, ITA supports data acquisition and test setup definition. ITA provides simulation tools, digital twin development capabilities, and laboratory infrastructure for electrical testing.
This service is designed for:
– Electrical component manufacturers (e.g. motor manufacturers, power electronics companies, battery system providers) seeking to improve reliability and reduce maintenance costs
– Technology developers working on AI-based diagnostic and predictive maintenance solutions for electrical systems
To start a project, companies can contact ITA to define the use case and available data. This initial step includes a feasibility assessment, followed by a tailored proposal, validation activities, and delivery of results.
