ehsanulhaque92/multimodal-prescriptive-pdm

1{% extends "layout.html" %}2 3{% block content %}4    <article>5        <header>6            <h1>About This Project</h1>7        </header>8        9        <h2>Robust and Interpretable Predictive Maintenance for Evolving Industrial Systems</h2>10        11        <p>12            This is an independent research and development project by <strong>Md. Ehsanul Haque Kanan</strong>, created to showcase an end-to-end, industry-standard approach to predictive maintenance. 13        </p>14 15        <h3>Core Objectives:</h3>16        <ul>17            <li><strong>Predictive Power:</strong> To accurately predict both Remaining Useful Life (RUL) for degrading systems and specific fault types for event-based failures.</li>18            <li><strong>Interpretability (XAI):</strong> To move beyond "black box" models by providing clear, human-understandable explanations for each prediction using SHAP (SHapley Additive exPlanations).</li>19            <li><strong>Robustness & Adaptivity:</strong> To demonstrate how a system can monitor model performance and detect concept drift, which is critical for real-world deployment in "evolving" industrial environments.</li>20        </ul>21        22        <h3>Technology Stack:</h3>23        <ul>24            <li><strong>Backend:</strong> Python, Flask</li>25            <li><strong>Machine Learning:</strong> Scikit-learn, XGBoost, LightGBM, SHAP, Imbalanced-learn</li>26            <li><strong>Data Handling:</strong> Pandas, NumPy</li>27            <li><strong>Frontend:</strong> HTML, Pico.css, Plotly.js</li>28            <li><strong>Deployment:</strong> Docker (to be implemented)</li>29        </ul>30    </article>31{% endblock %}