System Health Monitoring

Overview

A group of Penn State University engineers are working to support the development of an indexing table and robot arm that will allow for a more efficient COVID-19 testing procedure. Before the testing system can be built, an indexing table design must be completed as well as a supporting base to mount the indexing table. Once the system is complete, the system’s health must be monitored, allowing for the prevention of failures and ensuring that the entire process has limited down-time. The creation of a system health index is vital to outline the ideal conditions for running the testing process and provide quantitative predictions to aid in maintenance decisions.

Objectives

– Complete indexing table mechanical design

– Build Simscape simulation of indexing table

– Support in the implementation of

– Performing Accelerated Life Testing

– Utilizing IoT sensors to mitigate system failure

– Having a minimum capacity of 19,000 tests/day

– Create Simscape Guide for future use

– Research cross contamination reduction strategies during testing process

Approach

1. Project Scoping: Define the goals of the project, research strategies for monitoring system health and creating health index, and become familiar with required software.

2. Simscape Modeling: Use Matlab-based Simscape program to develop an indexing table simulation model for acquiring failure data.

3. Indexing Table/Supportive Base Designing: Understand the requirements for the indexing table and supportive base, brainstorm designs, complete table and build structure using SolidWorks, and make continuous changes to adapt to sponsor feedback.

4. Supporting Project Continuation: Assess current state of project and deliverables and provide Simscape guide to assist future development of the project.

Outcomes

– The indexing table and its supportive structure were completed. The indexing table will be mounted on a workbench next to the robotic arm in order to support the COVID-19 testing process.

– A Simscape guide highlights the state of the system simulation and provides instruction/tips based on the team’s experience with the program. This will improve the Simscape learning curve for future continuation of the project.

– The research regarding system health data, accelerated life testing, and sensor utilization allowed the team to make informed recommendations to the project sponsors that will help guide the project moving forward.