FlowFill: A Fast and Energy-Efficient Streaming Framework for Recovering Missing Values in Industrial Sensor Data
Hao Huang, Scott Evans · 2025
Industrial systems such as wind turbines and nuclear facilities depend on continuous sensor monitoring to ensure operational safety and efficiency. However, missing data—caused by sensor malfunctions or environmental interference—can compromise real-time analyses, delaying critical maintenance decisions and system optimizations. Existing imputation methods often struggle to efficiently handle large data volumes in a streaming manner, typically requiring full access to historical time series and consuming excessive energy and processing time. To address these limitations, we propose FlowFill, a fast and energy-efficient streaming framework for recovering missing values in industrial sensor data. FlowFill leverages multiple dynamic sketches to capture local temporal patterns and encodes long-term and diverse data characteristics. Operating incrementally, FlowFill updates its sketches with new data samples, ensuring real-time adaptability. An attention mechanism dynamically prioritizes relevant sketches, dynamically weighting their contributions for precise imputations. This framework excels in computational efficiency, resilience to diverse missing patterns, and energy conservation, making it ideal for industrial applications that demand high-speed processing and energy efficiency. Experiments on industrial datasets demonstrate that FlowFill surpasses state-of-the-art methods in both accuracy and efficiency, establishing a new benchmark for streaming imputation in industrial contexts.