Multi-Sensor Collaborative Sampling Schemes to Reconstruct Undersampled Mechanical System Signals for Machinery Fault Detection

Structural health monitoring (SHM) is imperative to the safety of structures, but can be costly and limited to a few locations for coverage. Many advancements have been made in the field of SHM over the past years such as the use of wireless sensors, implementation of compressed sensing, and event-based monitoring. These advancements are all pursued with the combined goal of collecting relevant data from the structure in a cost-effective manner, as well as taking into account the limitations placed on the system including size, energy consumption, safety, and bandwidth. Developments in wireless sensor systems enable greater coverage of structure monitoring since wired systems are costly to install. For wireless systems, it is ideal to use as little power as possible to process signals to reduce the sensor battery use and costly battery replacement. Previous research has focused on compressive sensing to reduce the size of data transferred while maintaining high-fidelity signal analysis. More recent work has focused on event-based monitoring, which collects data based on non-uniformly spaced triggers. Both compressive sensing techniques and event-based monitoring focus on improving sampling for multi-sensor systems, but are restricted to lower sampling rates or are prone to missed events. In order to further advance the application of compressive sensing techniques and event-based monitoring, this project will focus on triggering schemes for multi-sensor systems with signals above the Nyquist frequency of the individual sensors, assuming the signal is present across all sensor recordings. The problem with detecting frequencies above the Nyquist frequency is that the signals will alias and show up as a disguised frequency. Using existing sensors to determine the true frequencies of the signal requires a new method of detection. Multiple sensors were placed around a structure of interest and the optimal time delay of each sensor was determined through simulation and affirmed by experimentation.