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Fridge Maid is a proposal to use capabilities of SmartEdge Agile device from AVNET which is a fantastic platform to verify in the quick and efficient way proposal for IoT solution.
Using this device we have end to end solution for rapid prototyping concepts along with the most innovative elements like Cloud computing, AI etc.
It is simple and great start for delivering PoC (Proof of Concept). More details you can find here: https://www.avnet.com/wps/portal/us/solutions/iot/building-blocks/smartedge-agile/
Fridge Maid is one of this PoC type of project where I wanted to use SmartEdge device to monitor the operation of the fridge at my home. Refrigerator is the most fundamental home appliance which you can find almost in every home.
Principle of operation is simple: control temperature inside the appliance in order to allow extending expiration date of the food which we store in it. However this temperature regulators due to difference in design, brands, algorithms etc. works differently. This is the reason why I would like to propose the Fridge Maid solution will monitor fundamental parameters of my fridge and will allow me to understand following aspects:
- any deviation from correct operation like i.e. seal issue, temperature issue, compressor issue etc.,
- how to use my fridge in a more efficient way (i.e. reduction number of times when compressor is on),
- plan in advance some maintenance work to keep it working.
I have installed the Smart Edge device inside the fridge. It is tight to one of the walls which allows me to detect any vibration coming from compressor. In addition I can also check the ambient light sensor and detect when the door is open and for how long. On top of that I will constantly measure temperature and humidity. It should give me an information about how conditions for the safe storing food are obtained and how using this product can affect it.
HardwareFor this project I have used the Smart Edge Agile device.
At the beginning I was thinking to use the the Smart Edge device in the following way: place device inside the fridge to monitor: temperature, humidity, vibration and ambient light which will allow me to check how temperature/humidity is changing when compressor works (vibrations present - yes/no). Also I will be able to know how much time while door is open can affect the stability of temperature/humidity (ambient light sensor allows me to detect when the light in the fridge is on/off which respectively detects if someone opened or closed the door).
Unfortunately I reach the two limitations which I was not aware while I scoped this project:
- Vibrations generated by compressor are not possible to detect from inside the fridge. Also they are affected by fan which exchange the air between freezer and fridge so it is not working. Saying that I was not able to use motion sensing or predictive maintenance.
- Maximum recording time for the Smart Edge device is ONE hour so with the cooling system to detect the real stability is definitely too short.
However these limitations could be bypassed :-). To resolve the first problem was simply to get a second Smart Edge device and since Brainium allows to use up to 2 devices per project I created the following dashboard:
First Smart Edge Device called Fridge In - measured temperature / humidity / visible light / battery level and it was placed on the top shelf inside the fridge.
Second Smart Edge called Fridge Out - measured temperature / humidity / compressor status / battery level and it was placed on the back of the refrigerator close to compressor* to detect when it is working or not.
* Note: with vibration sensor I was not able to detect reliably if compressor was working or not but measuring the sound generated by it I was able to catch it correctly.
By using these two devices I was able to build entire system to get data I needed to prepare my analysis.
Since the recording time is maximum one hour I decided to update my previous way of collecting data in order to get some useful information. Because of that I gather the data of how the situation looks like:
- When I set the "cooling" between the levels (in my fridge there is 7 levels where the 1 is the warmest and 7 is the coldest) and then try to detect the differences between levels;
- I wanted to understand what is the tripping point between opened door and compressor engage to cool down the compartment interior.
Since the data capture allows me to get only one hour of data below there are some results I would like to achieve:
- how much temperature setting changes the temperature inside the refrigerator,
- how opened door affects the temperature in the refrigerator,
- how long compressors need to operate in order to bring the temperature conditions to the same level.
In order to analyse the data first of all I had to extract from *.csv file the timestamp.
The timestamp came with the format : 1565473878447 which is quite difficult to translate to the time. However with some help from Brainium technical support (thank you Kirill Oleshkevich) I got an explanation of how to translate it.
So first of all the last 3 digits are miliseconds [ms] which in my case are not needed, so I removed them in Excel using formula:
C2 timestamp cell
D2 cell =LEFT(C2, LEN(C2)-3)
Then the result from this formula I translated to time using following suggestion:
E2 =(((D2/60)/60)/24)+TIME(1, 1, 1)
E2 cell was formatted to TIME and I get exact timestamp in resolution of second [s].
A)
Reading the data looks like that the differences between settings vary from:
Level "1" - around 12*C (the warmest setting)
Level "3" - around 5*C
Level "7" - around -1*C (the coldest setting)
Unfortunately the stabilization of the temperature was a very long process especially for the lower temperatures so the SmartAgile system is not the best option for it since the maximum time to collect data is 1 hour.
You can also see that the scale is not linear so between level 1 and level 3 I have 7*C difference while between level 3 and level 7 = 6*C. This means it could be that the scale is not linear or the time to stabilize the required temperature setting is a very long process (especially for low temperatures).
Please consider to set the level to your need (based on what are the requirements for your food).
B)
Extracting data helps to understand how my refrigerator works @ level = 3 (mid setting in my case). When the door is opened for about 7 minutes it causes temperature inrease from 1.7*C to 5.2*C. It looks that it is tripping point in my case (ambient temperature was measured and it was constantly around 30*C).
C)
It is obvious that if we want to obtain lower temperatures the compressor needs to work longer. However from the observations which I was able to get using SmartAgile in the summer time when the ambient temperature is high trying to reach the lowest level requires the compressor to work almost constantly.
Saying that if we want to improve the electricity bill, try to regulate the temperature settings to something "warmer" and avoid the food which needs to be stored in lower temperature.
VideoSummaryPotential improvements / next steps- The system could be equipped with connection to the Power Grid in order to plan the cooling session in the cheaper tariffs,
- Automatic data collection once the door is opened and push it to the cloud,
- Suggestions how to use the fridge based on the compressor on/off time,
- Take into account seasons during a year how it affects usage of the fridge,
- Help to find the best spot for the fridge in a house from energy stand point,
- Share the data for specific model with the community of the users of the same refrigerator,
- Scan expiration date from food and send a notification to the customer if validity time is exceeded.
- Have a pre-defined timers for specific raw food for how long and in what temperature we can keep it safely.
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TO136-0202100001000FA8,HUMIDITY_TEMPERATURE,1565480901198,1.948500633239746,
TO136-0202100001000FA8,HUMIDITY_TEMPERATURE,1565480901365,1.948500633239746,
TO136-0202100001000FA8,HUMIDITY_TEMPERATURE,1565480901532,1.9302477836608887,
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TO136-0202100001000FA8,HUMIDITY_TEMPERATURE,1565480902031,1.9119949340820312,
...
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