Design and Development of Predictive Data Acquisition System with Real-Time Anomaly Detection and Alert Mechanism

Conventional data acquisition (DAQ) systems are designed primarily for sensing, digitization, and storage of data, but they often fail to provide timely intelligence when abnormal conditions arise. This paper presents the design and development of a predictive DAQ system that combines real-time sensing, lightweight analytics, anomaly detection, and alert generation in a single framework. The proposed architecture processes sensor streams at the edge, applies statistical and trend-based detection methods, and issues alerts through dashboard, email, or message-based channels when abnormal patterns are detected. The system is intended for industrial monitoring, environmental sensing, and smart infrastructure applications where delay-sensitive decisions are critical. The paper further discusses hardware selection, software architecture, anomaly scoring logic, notification workflow, and evaluation metrics. The resulting design improves responsiveness, reduces operator dependency, and supports proactive maintenance and safety monitoring.