We’re excited to share these insightful graphs that serve a practical purpose for our service engineering team.
Our team utilizes scatter diagrams to swiftly identify potentially faulty air quality sensors during preliminary testing and maintenance.
When multiple sensors are placed under identical conditions, comparing their raw signals (mV) can quickly highlight any "doubtful" sensors that may require further attention.
We construct an NxN matrix, where N represents the number of sensors at a single location. For each cell in the matrix [i, j], we plot the relationship between the readings of sensor i and sensor j. If the data points align along a line at a 45° angle, all is well. However, if the points spread across a significant area of the diagram, it indicates that at least one sensor is "suspicious."
In the provided figure, sensor number 1 is definitely "suspicious." There are also concerns regarding sensors numbered 2 and 4, while the others appear to be functioning correctly.
For sensors located outdoors, a single daily cycle of temperature and humidity data is usually sufficient for this rapid diagnostic approach.
Our team utilizes scatter diagrams to swiftly identify potentially faulty air quality sensors during preliminary testing and maintenance.
When multiple sensors are placed under identical conditions, comparing their raw signals (mV) can quickly highlight any "doubtful" sensors that may require further attention.
We construct an NxN matrix, where N represents the number of sensors at a single location. For each cell in the matrix [i, j], we plot the relationship between the readings of sensor i and sensor j. If the data points align along a line at a 45° angle, all is well. However, if the points spread across a significant area of the diagram, it indicates that at least one sensor is "suspicious."
In the provided figure, sensor number 1 is definitely "suspicious." There are also concerns regarding sensors numbered 2 and 4, while the others appear to be functioning correctly.
For sensors located outdoors, a single daily cycle of temperature and humidity data is usually sufficient for this rapid diagnostic approach.
