This project focuses on detecting faults in industrial sensors using machine learning techniques. The system analyzes sensor data and predicts whether a sensor is functioning normally or is faulty.
In industrial environments, faulty sensors can lead to incorrect readings, system failures, and financial losses. Manual monitoring is inefficient and error-prone.
This project aims to automate fault detection using data-driven machine learning models.
- Performed data preprocessing and cleaning
- Handled missing values and outliers
- Conducted Exploratory Data Analysis (EDA)
- Applied machine learning algorithms for classification
- Evaluated model performance using accuracy and other metrics
- Python
- Pandas
- NumPy
- Scikit-learn
- Matplotlib / Seaborn
- Type: Classification Model
- Algorithms Used: Logistic Regression / Random Forest / Decision Tree
- Evaluation Metrics: Accuracy, Precision, Recall
- Detects faulty vs normal sensors
- Data preprocessing pipeline
- Visualization of sensor data
- Easy-to-use and scalable
# Clone repository
git clone https://github.com/your-username/sensor-fault-detection.git
# Navigate to project
cd sensor-fault-detection
# Install dependencies
pip install -r requirements.txt
# Run notebook or script
jupyter notebook