Data-Centric Engineering

<p></p><p><strong style=color: rgba(36 36 36 1)>This book explores the transformation of engineering through the integration of data science and machine learning emphasizing a practical hands-on approach for students and professionals. It addresses the shift from traditional intuition-based engineering to a data-centric paradigm where sensors and digital systems generate vast streams of information that drive innovation efficiency and safety.</strong></p><p><strong style=color: rgba(36 36 36 1)>Foundations and Data Characteristics:</strong></p><p><strong style=color: rgba(36 36 36 1)>The text begins by outlining the evolving landscape of engineering data highlighting the proliferation of sensors in infrastructure vehicles and machinery. It discusses the unique challenges of engineering data such as its temporal and spatial nature heterogeneity multimodality noise uncertainty missing values and the sheer scale and volume encountered in real-world applications.</strong></p><p><strong style=color: rgba(36 36 36 1)>Signal Processing and Feature Engineering:</strong></p><p><strong style=color: rgba(36 36 36 1)>A core focus is on transforming raw sensor data into actionable insights. The book details foundational signal processing techniques (time and frequency domain analysis filtering wavelet transforms) and the importance of feature engineering-extracting meaningful variables that capture system behavior. It covers statistical temporal frequency-based and domain-specific features emphasizing their role in improving model performance and interpretability</strong><strong style=color: rgba(0 0 238 1)><u>1</u></strong><strong style=color: rgba(36 36 36 1)>.</strong></p><p><strong style=color: rgba(36 36 36 1)>Data Quality Pipelines and Labeling:</strong></p><p><strong style=color: rgba(36 36 36 1)>Ensuring data quality is presented as a critical engineering discipline. The book covers strategies for outlier detection handling missing data validation and monitoring. It also addresses the art of data labeling discussing labeling strategies tools quality control and the challenges of annotating complex engineering datasets.</strong></p><p><strong style=color: rgba(36 36 36 1)>Building Production-Ready ML Pipelines:</strong></p><p><strong style=color: rgba(36 36 36 1)>The text provides a comprehensive guide to designing robust scalable and maintainable machine learning pipelines. It covers data ingestion real-time vs. batch processing automated cleaning feature engineering model training evaluation deployment (batch real-time edge) and continuous monitoring for drift and performance degradation. Emphasis is placed on reproducibility version control and the use of MLOps practices</strong><strong style=color: rgba(0 0 238 1)><u>1</u></strong><strong style=color: rgba(36 36 36 1)>.</strong></p><p><strong style=color: rgba(36 36 36 1)>Case Studies and Advanced Topics:</strong></p><p><strong style=color: rgba(36 36 36 1)>Practical case studies such as structural health monitoring illustrate the end-to-end application of these concepts. Advanced topics include explainable AI reinforcement learning federated learning simulation and digital twins-showing how AI augments engineering decision-making and system optimization. </strong></p><p><strong style=color: rgba(36 36 36 1)>Best Practices and Cultural Shift:</strong></p><p><strong style=color: rgba(36 36 36 1)>The book concludes by advocating for a data-driven engineering culture ethical considerations continuous learning and thorough documentation. It encourages engineers to embrace data as a strategic asset fostering collaboration between domain experts and data scientists to drive innovation and ensure the reliability and safety of engineered systems.</strong></p>