In modern power plants, machinery operates under high stress and extreme conditions, making fault detection a critical component for maintaining efficiency and safety. This project introduces an AI-based fault detection system tailored specifically for power generation units such as turbines, motors, and boiler feed pumps.

The core idea is to harness the power of machine learning algorithms to monitor physical parameters like vibration, temperature, and electrical current. These parameters are key indicators of machine health, and even subtle changes in their behavior can signal early signs of malfunction. By continuously analyzing this data, the system can distinguish between normal operation and conditions that may lead to equipment failure.

The model is trained to classify the status of the machinery into two categories: Healthy and Faulty. This binary classification allows operators to take timely preventive actions. For example, if the system detects abnormal vibration or overheating, it can immediately flag it as a potential fault, prompting maintenance before a breakdown occurs.

This intelligent monitoring system reduces the dependency on manual inspection and routine maintenance schedules. Instead of fixing problems after failure, this approach promotes predictive maintenance, saving valuable time, reducing costs, and preventing unplanned shutdowns.

One of the strengths of this approach is its ability to learn and improve over time. As more data is collected from operational machines, the model can be retrained to enhance its accuracy and adapt to different types of equipment or environmental conditions. This makes the system highly scalable and customizable for various types of power plants — from thermal to nuclear and beyond.

In addition to real-time monitoring, the system can also integrate with control systems to trigger automated safety protocols, such as shutting down equipment or alerting engineers, further enhancing plant safety.

The adoption of AI in fault detection not only improves reliability but also supports the long-term sustainability goals of the energy sector by optimizing energy usage and reducing equipment waste.

This solution is designed to be practical, efficient, and applicable in real industrial settings. It represents a shift toward smarter infrastructure, where machine learning becomes a silent guardian of heavy-duty equipment, working continuously to ensure smooth and uninterrupted power generation.