This week’s progress:
- Testing Methodology for Causing Damage to the HVAC
I focused on designing a structured methodology for inducing artificial defects in HVAC duct samples in a way that is both experimentally controlled and grounded in industry practice. Rather than introducing arbitrary damage, I reviewed corrosion testing standards (particularly ASTM International guidelines such as ASTM G85 Annex A4) to understand how accelerated degradation is typically evaluated in heat exchangers and ferrous systems. Based on this research, I suggested a two-track approach targeting both chemical and mechanical degradation. For the corrosion component, I designed an accelerated protocol that begins with sanding down the galvanized surface to simulate coating failure followed by an acidified salt spray (5% NaCl and 1% acetic acid) and wet-dry hygroscopic cycling to concentrate ions and promote pitting within a compressed timeframe. This allows us to generate measurable oxidation effects without waiting for long-term natural corrosion.
In parallel, I also worked on figuring out a defect simulation strategy to intentionally alter the duct’s structural stiffness and resonant behavior for classifier training. I chose to introduce longitudinal slots (0.5–5 mm) and partial-depth notches at 25–50% wall thickness to essentially simulate cracking and wall thinning. The goal in structuring the defects this way is to ensure that the resulting acoustic signatures are systematic and scalable.
- Setting Up the Preliminary Model
To prepare for working with our custom HVAC acoustic dataset, I began by exploring a publicly available dataset to build familiarity with audio signal processing and support vector machine (SVM) classification before our experimental data is collected. I selected the Electrical Tactile Dataset because it contains high-frequency sensor recordings with labeled signal patterns, making it a good analog for the kind of time-series data we expect to extract from duct vibration/acoustic sensors. I trained an initial SVM classifier to distinguish between texture categories. This preliminary model was useful in two ways: it gave me hands-on experience working with audio-based features and SVMs and it provided a baseline pretrained model that I can refine once our own HVAC dataset becomes available. Establishing this baseline early allows us to validate our feature extraction and classification pipeline, which will reduce integration time and uncertainty during the more complex stages of our HVAC testing.
Electrical Tactile Dataset (Piezoelectric and Accelerometer) for textures: https://figshare.com/articles/dataset/Electrical_Tactile_Dataset_Piezoelectric_and_Accelerometer_for_textures/28033589
My work for the week can be found here:
https://docs.google.com/document/d/1NJO_i_av8JNN0vTtEvd5TdrTgKmLdeheiv4WQtrmINs/edit?usp=sharing
Things to do for next week:
We will be getting most of our materials next week so I think I’ll work on collecting data and getting the hardware and embedded systems control logic setup with Adithi. So the tasks will be as follows:
- Receive and inventory all incoming materials needed for the experimental setup.
- Begin collecting initial acoustic and vibration data from the hardware system.
- Set up and integrate sensors with the data acquisition hardware.
- Collaborate with Aditi to develop and test the embedded systems control logic for synchronized and reliable data capture.