Predicting the drift of a gas sensor using fault detection techniques

The value of sensors is growing quickly in the modern world. People are adopting sensors to carry out tasks more easily and intelligently. The gas sensor is among the frequently utilized sensors. Gas sensors can be used to identify harmful gasses, leaks of gas, and airborne pollutants. Sensor drift is one of the most important problems with gas sensor readings. Drift will cause a significant variation in gas sensor readings even in the case of all other conditions staying unchanged. This issue has the potential to cause major problems. Fault detection algorithms can be used to identify sensor drift. In order to reduce measurement errors, the suggested method offers a system that forecasts drifted sensor results. This method predicts gas sensor drift by utilizing machine learning methods in the SparkR big data platform. Both problems that vary from typical behavior and faults that are caused usually are identified.