Critical Industrial Sensor Data Analysis & Forecasting for Predictive Maintenance

**I'll start interviews in 3 days.** (1st July) Project Overview Develop a data analytics and forecasting framework for industrial sensor data collected from an instrumented structural component. The objective of this phase is to characterize sensor behavior, identify degradation-related trends, and build forecasting models capable of predicting future system responses. Scope of Work Data Exploration & Preprocessing • Data quality assessment • Missing data handling • Signal normalization and preprocessing • Exploratory analysis of sensor channels • Correlation and trend analysis Feature Engineering • Statistical feature extraction • Time-domain indicators • Rolling-window degradation metrics • Trend and drift characterization Forecasting Model Development Evaluate and compare forecasting approaches including: • ARIMA/SARIMA • Gradient boosting methods (XGBoost) • LSTM-based forecasting (if data volume supports) Deliverables • Cleaned and documented analysis notebooks • Feature engineering pipeline • Forecasting model implementation • Forecast accuracy evaluation report • Visualization package summarizing results