This paper will provide a non-invasive fault detection solution with Artificial Intelligence (AI) techniques. Non-invasive methods would not require system and machine modification. This solution would use the existing system to collect visual, audio, and vibration data for diagnostics. Because the early signs of fault are numerous, subtle, complex, and difficult to classify and detect, with mathematics and signal processing, the collected diagnostic data will be processed and analyzed for unique patterns and features to be used as input to AI tools. These features will be used to train the AI tools. The subtle characteristics are learned through training; when completed, the trained AI tools can detect them in real-time. Each collected data will be able to detect different faults than others. Some early stages of faults are better detected and diagnosed by visual images than by vibration or audio. Others are better with audio because the cause of the fault is embedded deep inside a machine. For example, an Instrumental Panel (IP) showing engine rpm, and a speedometer complemented by engine sound and wheel vibrations can reveal non-obvious anomalies that might not be shown on the IP. Sound and vibration would be able to provide the early telltale signs of anomalies inside the engine and wheels. These theories are experimented with and validated on an actual vehicle and will show that a non-invasive fault detection solution is a viable solution to early fault detection.