Doctoral Thesis: Diagnostics for Periodically Operated Actuators


Event Speaker: 

Lukasz Huchel

Event Location: 

via Zoom, see details below

Event Date/Time: 

Monday, May 3, 2021 - 12:00pm

Increasing constraints on quality, reliability and minimum downtime require the revision of existing maintenance approaches. Preventive maintenance, or even more reactive maintenance, have to be supplemented with information about the system’s condition in order to meet the tight requirements, i.e., create a predictive maintenance approach. Condition monitoring can be characterized by the sensing approach and data processing for extraction of condition-related signal features. Modern advances of connectivity and embedded systems enable a wide range of possibilities in the field of condition monitoring, both in sensing and data processing.
This thesis provides signal processing tools and hardware solutions optimized for, but not limited to, diagnostics of periodically operated actuators, i.e., any kind of mechanical or electromechanical system that experiences non-uniform load during the operating cycle. A developed embedded platform for state-of-the-art vibration and acoustic measurements combines quality of high-end acquisition systems with portability of IoT devices, thus, allowing for temporary field installations and monitoring of critical industrial equipment. Cyclostationary analysis enables diagnostics based on signals with strong random component by extracting modulation signatures otherwise unattainable by conventional time or frequency domain analysis, as demonstrated with applications to diaphragm pumps and cutting tools. An extension to the Integrated-Electronics-Piezoelectric (IEPE) industry standard for vibration measurements stretches the applications to a wide range of measurands like temperature, pressure or mechanical strain. These stretched capabilities enable more unified sensing strategy and decrease complexity of the condition monitoring systems; thus, it further supports miniaturization and on-the-edge applications.
Prof. Steven Leeb (Thesis Supervisor)
Prof. James Kirtley
Prof. Jan Helsen (Vrije Universiteit Brussel)
To attend this defense, please contact the doctoral candidate at lhuchel at mit dot edu