Reducing False Alarms with Multi-modal Sensing

Industrial sensing applications place a premium on costeffectiveness and accuracy. Traditional approaches often use expensive, invasive sensors, because inexpensive sensors suffer from false positive detections. Sensor cost means automation is sparse or avoided when the value of specific sites cannot be justified. In this paper, we show combining different types of sensors can allow low-cost sensors to avoid false positives, enable much greater levels of automation in some applications. We explore this problem by studying a specific application: blockages in oil flowline common in cold weather. We use pipe skin temperature to infer changes in fluid flow, and combine readings with acoustic data to avoid false positives and be robust to environmental changes. We demonstrate that our approach is effective with field experiments. Finally, suggest that this approach generalizes to other classes of problems where false positives from one sensing modality can be resolved by multi-modal sensing.