Detecting Calibration Drift in Flow Meters with Python: A Hands-On Guide

I ran into the problem of detecting calibration drift in flow meters when our clients started complaining about inaccurate readings. We have over 2,500 IoT devices scattered across remote locations in Kenya, and dealing with real infrastructure constraints like intermittent connectivity and budget hardware often makes managing these devices a challenge. Detecting calibration drift in flow meters is important because inaccurate readings can result in significant operational inefficiencies and potentially large financial losses. Understanding calibration drift The first step in tackling this issue was understanding what calibration drift actually looks like. Over time, flow meters can deviate from their calibrated settings due to environmental factors, wear and tear, or simply because the sensor ages. This drift usually shows up as a steady deviation from expected readings over a period of time. To put it simply, you might expect a certain volume of flow per hour, say 100 liters, but ove