The problem Vehicle Health Monitoring solves

One of the main problems the code solves is it analyses the sound of an engine (not exhaust) of a vehicle to analyse its health. A bad vehicle produces an engine sound which is much more amplified than the sound of a good vehicle. It may also include sounds of bearings drying up due to less oil or pristine cranking with each other due to excessive overheating or hydraulics maybe being faulty. Hence, in the automotive industry, such monitoring can also be useful for detecting potential problems in a vehicle's engine, transmission, or other systems. By analyzing the noise and vibration patterns of a vehicle, it may be possible to detect abnormalities that could indicate an impending failure. Acoustic analysis involves using microphones to capture the sound of the bike and analyzing it to detect any abnormalities in the noise patterns. For instance, certain types of engine faults may produce distinct noise patterns that can be detected through acoustic analysis. A microphone can capture the sound waves produced by the vehicle, and they are commonly used in acoustic analysis to detect faults such as abnormal engine noise or exhaust leaks. On the software side, we have used python, and libraries to record, process audio files to convert it to numerical values from which we can form graphs to analyse vehicle health.

Challenges we ran into

There were many challenges we faced but at the end we came up to solutions for almost each and every one of them. One of the main challenges we faced was the unfortunate luck where we lacked the proper means of hardware required for this project. Secondly we faced issues with graphical output analysis, where we could not bring an output in the graphical format.. We did not have proper hardware requirements to record pure form of audio. We did not have a premade data set to work upon. So, we had to record a few audio sample files to test our project.