Intelligent Techniques in Condition Monitoring of Electromechanical Energy Conversion Systems

The steps involved in condition monitoring is illustrated. The first step is to collect raw data using sensors in the case of conventional condition monitoring while the raw data is collected using simulation model in our approach. The next step is data pre-processing as the data may contain noise or there is data scarcity and also for feature selection. The next step is to choose an appropriate classifier algorithm that is typically machine learning based or deep learning algorithm. The next step is to train the classifier with 70% of the available data and use the remaining 30 % data for evaluating the classifier. Finally, an algorithm that is capable of fault classification is developed.

Artificial Intelligence (AI) based condition monitoring of Electro hydraulic systems (EHS)

This research project aims at developing modern artificial intelligence-based methods for condition monitoring of electro-hydraulic systems. To ensure safe and efficient operation of these systems, it is essential to predict their incipient faulty operations at an early stage.

Description of Project

This research project aims at developing modern artificial intelligence-based methods for condition monitoring of electromechanical energy conversion systems, or powertrains. To ensure safe and efficient operation of these powertrains, it is essential to predict their incipient faulty operations at an early stage. By combining experimental results on hardware with simulation results, we will produce synthetic augmented data to be used to train the artificial intelligence (AI) algorithms. These algorithms will also combine data from different application domains, allowing the transfer learning. Moreover, AI will guide the simulation setups to optimally invest the computational resources into relevant simulations. The results of the project are expected to produce new knowledge on how to optimally leverage AI algorithms for energy conversion systems. We will also build a variety of simulation models, which can be used for other purposes such as system optimization and control design