Test Results
FT.1
Minimum Size of Component Camera Can Detect
Objective: Prove the camera can distinguish multiple 2x3x1.5 inch components at a minimum distance of 55 inches.
Setup: Connect Raspberry Pi to the camera module and place three different components on the test cart. We will then run a series of tests that will assess distance away from the camera and the clearness of pictures.
Pass Criteria: Distinguish multiple 2x3x1.5 inch components at a minimum distance of 55 inches.
Results: Our camera was able to take a clear picture of multiple 2x3x1.5 inch components at distances further than 55 inches.
Conclusion: The camera module was able to clearly take a picture at 73.5 inches. The human eye can easily distinguish the three different components placed on the test table.
FT.2
Email Notification Received by User.
Objective: Send an alert to the user that a change in the environment has passed a threshold via email within an average of 10 seconds or less.
Setup: Have a written python script to simulate sensor threshold and have an email account setup.
Pass Criteria: The alert email notification must be able to receive within ten seconds of it being sent.
Results: Our email notification service sends emails out and they averaged a received time difference of 3.7 seconds with a standard deviation of 0.7.
Conclusion: Our monitoring system sends and the user receives the notification within the 10 second engineering requirement averaging 3.7 seconds from email sent to received.
Temp Sensor Data
FT.3
Website Database Collects Data
Objective: Receive the sensor data within an average of 10 seconds or less and include the date and time.
Setup: Run client code to save sensor data in the database and display the most recent database entry on the website.
Pass Criteria: The system must be able to display data within ten seconds.
Results: After running this test the system was able to log environmental data, allowing it to be displayed graphically relatively quickly. We were able to measure the time the system needed to display the data graphically by using a python script that took note of the reading time of the data as well as the display time. By subtracting the two times, we were able to see how long the system needed to successfully display our data. The time it takes for our system to display new data graphically is faster than ten seconds.
Conclusion: The 4 temperature sensors are reading precisely. We calculated the standard deviation for each reading and the average standard deviation was 0.5℉.
FT.4
Temperature Sensor Calibration
Objective: Prove that temperature sensor readings have an average standard deviation of no more than 2℉.
Setup: We will begin this test by connecting several of the same temperature sensors to a Raspberry Pi, creating a small circuit. We then plan on placing the circuit into a sealed container, limiting the potential for any interference with the test. After that we plan to use a blow dryer to change the temperature inside of the container resulting in a change of temperature. We will then use the collected data from each temperature sensor at different temperature levels to show whether the sensors are all reading similar values, proving that the sensor readings are precise.
Pass Criteria: The sensor readings must have a standard deviation of no more than two
degrees Fahrenheit.
Results: We successfully tested our system of temperature sensors by placing four of them on a small breadboard that was placed inside of a small container. After placing the items in the container we began the test by actively changing the temperature by using a hair blow dryer. This test went on for about a minute, allowing each temperature sensor to read the temperature in the changing environment. The data was then transferred into a google sheet. After comparing the variation in the sensor readings, there was a standard deviation smaller than two degrees Fahrenheit.
Conclusion: The 4 temperature sensors are reading precisely. We calculated the standard deviation for each reading and the average standard deviation was 0.5℉.
FT.5
Humidity Sensor Calibration
Objective: Prove that humidity sensor readings have an average standard deviation of no more than 3%RH.
Setup: We will begin this test by connecting several of the same humidity sensors to a Raspberry Pi, creating a small circuit. We then plan on placing the circuit into a sealed container, limiting the potential for any interference with the test. After that we plan to use a blow dryer to change the humidity inside of the container resulting in a change of humidity. We will then use the collected data from each humidity sensor at different humidity levels to show whether the sensors are all reading similar values, proving that the sensor readings are precise.
Pass Criteria: The sensor readings must have a standard deviation of no more than three
percent.
Results: We successfully tested our group of humidity sensors by placing four of them on a small breadboard that was placed inside of a small container with a small damp rag. After placing the components into the container we began the test by actively changing the humidity by using a hair blow dryer. This test went on for about a minute, allowing each humidity sensor to read the relative humidity in the changing environment. The data was then transferred into a google sheet. After comparing the variation in the sensor readings, there was a standard deviation smaller than three percent.
Conclusion: The 4 humidity sensors are reading precisely. We calculated the standard deviation for each reading and the average standard deviation was 1.1 %RH.
FT.6
Pressure Sensor Calibration
Objective: Prove that pressure sensor readings have an average standard deviation of no more than 2 hPa.
Setup: We will begin this test by connecting several of the same pressure sensors to a Raspberry Pi, creating a small circuit. We then plan on placing the circuit into a sealed container, limiting the potential for any interference with the test. After that we plan to use a vacuum to change the pressure inside of the container. We will then use the collected data from each pressure sensor at different pressure levels to show whether the sensors are all reading similar values, proving that the sensor readings are precise.
Pass Criteria: The sensor readings must have a standard deviation of no more than two hPa.
Results: We successfully tested our pressure sensors by placing four of them on a small breadboard that was placed inside of a small container. After placing the parts into the container we began the test by actively changing the pressure by using a vacuum. This test went on for about a minute, allowing each sensor to read the pressure in the changing environment. The data was then transferred into a google sheet. After comparing the variation in the sensor readings, there was a standard deviation smaller than two hPa.
Conclusion: The 4 pressure sensors are reading precisely. We calculated the standard deviation for each reading and the average standard deviation was 1.3 hPa.
FT.7
Particulate Sensor Calibration
Objective: Prove that particulate sensor readings have an average standard deviation of no more than 2 ppm.
Setup: We will begin this test by connecting several of the same particulate sensors to a Raspberry Pi, creating a small circuit. We then plan on placing the circuit into a sealed environment, limiting the potential for any interference with the test. After that we plan to use a blow dryer to change the particulate inside of the container resulting in a change of particulate. We will then use the collected data from each particulate sensor at different particulate levels to show whether the sensors are all reading similar values, proving that the sensor readings are precise.
Pass Criteria: The sensor readings must have a standard deviation of no more than two
ppm.
Results: We successfully tested our particulate sensors by placing three of them on a small plastic bag that contained vacuum residue. After placing the parts into the bag we began the test by using an air dryer to blow air into the bag, forcing particles to rise into the air. This test went on for about a minute, allowing each sensor to read the ppm value in the changing environment. The data was then transferred into a google sheet. After comparing the variation in the sensor readings, there was a standard deviation smaller than two ppm.
Conclusion: The 3 particulate sensors are reading precisely. We calculated the standard deviation for each reading and the average standard deviation was 0.3 ppm.
ST.1
POE Standard Powers Entire System
Objective: Prove that the system typically draws less than 2000 mA.
Setup: We will start this test by having all components connected to the Raspberry Pi. We will then measure the maximum current draw and power of the entire system. We will then take these values and compare them to the overall theoretical maximum current draw and power of the entire system. With this information, we can deduce whether PoE can sufficiently power our system as well as shed light on unnecessary power and current draws.
Pass Criteria: The entire system must function via power over ethernet.
Results: After connecting all of the components to our system we were able to see that the PoE splitter provides sufficient power to work. We were able to compare our theoretical current values to our actual reading by using an electricity monitor. The system pulls a maximum of 1178 mA while our theoretical current was 1825 mA.
Conclusion: Our system has passed the test because it can be fully powered using PoE, with a current draw of less than 2000 mA.
ST.2
Account for Loss of Internet
Objective: Prove the system alerts the user of internet connection loss in under two seconds.
Setup: We will start this test by having all components connected to the Raspberry Pi. We then proceed to manually disconnect the Raspberry Pi from the internet. After, we will then determine if the LCD indicates whether the internet connection was lost. We also plan to make sure that the system continues to read environmental sensor values without internet connection and resume its data logging function after connecting back to the internet. In order to measure the time it takes for the internet connection loss message to display on the LCD, we will be using a python script that records the time the internet was lost and the time it takes to display.
Pass Criteria: The LCD must indicate loss of internet connection within two seconds and resume system normal functions after connecting back to the internet.
Results: This system test was successful because the raspberry pi was able to display the
error message of internet connection loss. The test also revealed that the system continues to read sensor values without logging them. After successfully connecting to the internet the sensor data will continue to be logged.
Conclusion: Our system has passed the test by displaying an internet loss message on an
average of 1.8 seconds. The system also reads sensor values with or without internet
connection.