This project develops a sound-based fault detection system for internal combustion engines using machine learning. Engine sounds are recorded, processed, and classified as healthy or faulty (e.g., misfire). The goal is a non-invasive, low-cost, near-real-time diagnostic tool for automotive maintenance.
- Overview
- Objectives
- System Workflow
- Technologies
- Setup & Installation
- Usage
- Dataset
- Results
- Future Work
- Team
- License
- Contact
- Detect and classify engine faults from audio signals.
- Apply signal processing and ML techniques for accurate prediction.
- Provide an accessible diagnostic alternative to expensive tools.
- Data Acquisition
- Recordings from a Ford EcoSport engine: 20 samples (15 healthy, 5 misfire).
- Preprocessing & Denoising
- Filtering, segmentation, normalization.
- Feature Extraction
- MFCC, DWT, SWT, cepstrum, spectral centroid, chroma, bispectrum.
- Model Training & Evaluation
- Algorithms: Random Forest, SVM, simple CNN.
- Metrics: Accuracy, Precision, Recall, F1-score.
- Prediction
- Real-time inference pipeline for audio input.
- Python 3.x
- NumPy, Pandas
- Librosa, PyWavelets
- Scikit-learn
- TensorFlow / Keras (optional for deep learning)
- Matplotlib
# clone repository
git clone https://github.com/<your-username>/<repo-name>.git
cd <repo-name>
# create virtual environment (recommended)
python -m venv venv
# activate venv:
# Windows: venv\Scripts\activate
# macOS/Linux: source venv/bin/activate
pip install --upgrade pip
pip install -r requirements.txt