Modern vehicle engines emit distinct sounds when operating normally or malfunctioning. AI-driven continuous monitoring detects abnormalities early, preventing costly breakdowns.
This system leverages Google Teachable Machine to train an AI model to differentiate between normal and faulty engine sounds. Designed for predictive maintenance, the system offers a low-cost, efficient solution.
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Table of Contents
Teachable Machine Model
A machine learning model is trained to recognise normal and abnormal engine sounds. It runs in a browser, continuously monitoring the engine’s sound. If an anomaly is detected, it immediately triggers an alert.
When the AI model detects an abnormal condition, it sends an HTTP request to the IndusBoard Coin. The IndusBoard Coin, acting as a web server, receives and processes the alert.
The IndusBoard Coin hosts a webpage that visually displays the system status. If a fault is detected, the page turns red and shows an alert message. Simultaneously, the IndusBoard Coin activates a blinking LED as a warning indicator.
Fig. 1 shows the webpage UI running the trained ML model. Fig. 2 shows the prototype with an IndusBoard Coin. The components required to build this system are listed in the Bill of Materials table. Fig. 3 shows the ML model being trained in Teachable Machine.
| Bill of Materials | ||
| Component | Specification | Quantity |
| IndusBoard Coin | Coin V2 development board | 1 |
| Laptop | For simulation | 1 |
| Car engine | Any working vehicle engine or motor for testing | 1 |
Creating the AI model using Teachable Machine
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