Hardware Failure Prediction Monitor

Inspiration

Inspiration that me and my team had was from the software called Prophes which software is a closed source for maintenance and reliability.

What it does

The software will initially act as a system monitor, with an additional feature of deep learning prediction. It can utilize known hardware specs on the current devices, and report any faults within these devices.

How we built it

We built this using Python, PyQt5, and the tool PsUtil to give us information on disk, memory, CPU, and GPU usage. Including device temperatures. Allowing us to be able to showcase our idea of how it would work under the circumstances that hardware may fail and would require diagnostics.

Challenges we ran into

Challenges we ran into for this project were parsing our datasets, and rightfully being able to adapt our machine learning model sending data to the System monitor UI. That was built using PyQt5, purposefully to visualize our incoming data and represent how our ML model was supposed to detect hardware failure.

Accomplishments that we're proud of

Accomplishments that we are proud of include being able to have an open-source project that is stable enough to work without the machine learning portion. Gives us an idea of how our data will be visualized when analyzing embedded systems.

What we learned

The learning portion of developing this project was collaboration work. Topics that were learned were improving on development using Machine learning topics. Including developing a more easier workable User interface. Allowing for everyone in the team, to modify things within the back end of the UI, and the codebase would not break entirely.

What's next for Hardware Failure Prediction Monitor

The next steps that would be considered in the future are developing and making improvements to our Machine learning model. Where we will be able to train and optimize our model.

Built With

• psutil
• pyqt5
• python