Analog Frontend for Big Data Compression and Instantaneous Failure Prediction in Power Management Systems

An innovative analog frontend for big data collection and intelligent compression as part of an instantaneous failure prediction platform is presented. Failure prediction in power management systems is crucial for increasing uptime and preventing massive failure. Accurate failure prediction, with real-time decision-making, requires data collection from many wide-bandwidth signals within a system, as low-bandwidth information such as DC output voltage is of limited value for decision-making and failure prediction. Analog compression, data profiling, and anomaly detection methods enabled by the unique analog frontend are presented. The system significantly reduces the demand for high computational power, fast communication, and large storage space required for the task. A real-time compression ratio exceeding 100:1 was achieved by the experimental analog frontend, digitizing the analog signal at a rate of 135 MS/s with a 10-bit resolution. The motivation, existing solutions, performance metrics, and advantages of the analog frontend are demonstrated, along with the details of the circuit operation principle. The process of data collection, its intelligent processing using the analog frontend, and anomaly detection are simulated to validate the theoretical hypotheses. For experimental validation, a laboratory setup that includes a dedicated analog frontend prototype and step-down DC-DC converter was built and evaluated to demonstrate the robust performance in sampling and monitoring wide-bandwidth signals and smart data processing using analog frontend for quick decision-making.