Author: rramouta

At the beginning of this week, I read through all of the academic papers and datasets that Adithi found last week. I found these papers to have useful information regarding data collection and processing. For instance, I found that all these papers used acoustic emission (AE) sensors for structural health monitoring (SHM). AE sensors are specialized, high-frequency piezoelectric devices that detect transient stress waves (ultrasonic energy) in materials caused by structural, mechanical, or manufacturing defects. They provide real-time monitoring to identify leaks, cracks, and friction. I began looking into how AE sensors work and finding one within our budget constraints for purchase. I also continued my search for an existing dataset. My minimum requirement for a dataset was that it must include acoustic data labeled with the defects we are focusing on (corrosion and air leaks) from the duct material we are focusing on (sheet metal and fiberglass duct board). I was able to find two datasets that directly used AE sensors to collect acoustic data of corrosion and air leaks on galvanized steel. 

However, when I presented my findings to our supervising professor on Wednesday, he said we should create our own dataset. In this way, our robot would perfectly interact with our physical system and we wouldn’t have to worry about fitting our physical system to that which was experimented on in the studies that produced the existing datasets we found. I shared our professor’s opinion with the rest of my group, who were unable to attend the meeting. Together, we have decided to focus all our energy on purchasing the robotic base and AE sensor as well as the HVAC duct system. I found an AE sensor that fits our frequency needs and I found an HVAC duct that fits our measurement requirements. We have put in the purchase orders for these.

This week, I also looked into acoustic features we can pull from the acoustic data to make meaningful conclusions about structural defects. I found a bias shift method and a denoising process specific to AE sensor data. I also discussed with my team that our visual odometry system may not be required, since we are able to calculate the distance our robot has travelled relative to its starting position based on its speed and the amount of time it moves. The visual odometry system would add unnecessary complications. My teammates agreed with me, so this system is not included in the design anymore. However, we still require a camera to allow the remote-control user to see where the robot is and whether the robot needs to turn. I also started creating the design presentation that we must deliver next week.

 

Next Week Goals:

• In the coming week, our purchases will come in and we can begin construction of our robot and HVAC duct system. 
• Then, we can begin the process of data collection. 
• Simultaneously, I can continue looking into data processing techniques. 
• We must also have our design presentation ready for Monday. 

 

My work for this week can be found here: https://docs.google.com/document/d/1ixLqQD6Fis3x61WQpO9HzGfW2eAyp14MxrCl1U2Xk7k/edit?tab=t.0

 

This week, I presented our Proposal. I spent last weekend sorting out the elements we wanted to include with my team, specifically the use case requirements. I created some of the figures and worked on the technical challenges, solution approaches, and testing, verification, and metrics slides. I also practiced and revised how I should present and how I should answer questions. We were able to get a clear picture of the project we want to accomplish this semester.

I also looked into locating HVAC ducts for physical testing. I called a couple of HVAC duct repair and replace companies and inquired about where they store damaged ducts, but they were focused on business, i.e. they wanted me to get my ducts repaired even though I explained that I have no ducts. We, as a team, are continuing our search for used ducts but as a backup, I have found HVAC ducts of different sizes to buy on Amazon. I have also found fiberglass board to buy on Amazon to apply to our sheet metal ducts so that we are able to test our system on fiberglass duct board. Fiberglass duct board consists of standard sheet metal ducts that have a fiberglass lining that serves as an extra layer of insulation

I looked extensively into acoustic features that indicate corrosion and leaks. Although I had a general understanding of the type of acoustic signature that indicates these defects, I was able to find more specific information as well as some signal processing techniques to employ from research papers. These papers were done on a variety of sheet metal materials, but I was not able to find research papers on the acoustic signatures of defects on fiberglass duct board. Fiberglass duct board helps to dampen noise. My worry is that it will be difficult to generalize the ML model to fiberglass duct board because the acoustic signature that we capture with the robot will be dampened, and it may be hard to extract the acoustic features from the data. I am also worried about the conversation my team and our supervising professor have been having regarding the data to train the ML model. We had previously assumed that we could find a dataset with labeled acoustic data from the duct materials of our choice, but this is proving difficult. Our professor has recommended that we gather our own data based on our robot’s specific ability to create and collect acoustic data. Currently, we are working on getting our robot together while looking at potential datasets. Whichever dataset we use must, at the very least, include acoustic data labeled with the defects we are focusing on (corrosion and air leaks) from the duct material we are focusing on (sheet metal and fiberglass duct board). 

Next Week Goals:

Looking through datasets to choose one that meets the minimum requirements stated above

Finding a viable HVAC duct source and purchasing/obtaining the ducts

Developing the design of the acoustic data processing pipeline

My work for this week can be found here: https://docs.google.com/document/d/1RLk7pQB5754Au6vyzFaVA-jm3ABx_Y89l1xmLTPi_bc/edit?tab=t.0