The water pump anomaly detection system is an IoT-based water pump detection system with machine learning (anomaly detection) capabilities using a microcontroller

i have school project about this one and I have done for make dashboard in platform but I have no idea where it should go for next step because I cant download csv for google colab machine learning. I’m open for anyone can help me

Machine Learning: Develop machine learning (anomaly detection) for the 5 sensors based on their respective reading history or habits.
Output: Web (refer to the output sheet).
Database: Need advice. Local computer? Possible to use Raspberry Pi?

I think that most people would approach this from a different angle.
The Arduino Uno should be the device that does the machine learning - maybe with some manual input from the user via Blynk.
Also, Blynk is the alert system.

Are you limited to using an Arduino Uno plus ESP-01 as the WiFi gateway?
This is a very privative system, and few people would use an Arduino Uno today.


thanks, pete for the quick reply.
Yes, I am entirely new to this topic. I’m open to any advice because I do not have many info resources I can read.

my background project is machine learning studies. so my teacher wanted an end product for the alert system so I drew back the architecture like that. I quite know how the training model is from CSV data but it’s new and I believe Blynk with do better.

about the Wi-Fi gateway; Im not sure because I use that ( in the picture) and its working for monitoring water level. but I’m open for know detail if that solve my problem

Blynk is not a machine learning product, it’s primarily an interactive user interface.
The machine learning part needs to be written by you in C++, and will run on your MCU device alongside the Blynk library.

The Arduino Uno has very limited memory and processing power (it’s almost 15 years old) has no built-in IoT capability. That’s why you need to add the ESP-01 as a WiFi gateway, but this is very inefficient.

You would be better using something like the ESP32 MCU instead, although it depends to a degree on the sensors you intend to use.


Now it is clear product knowledge, and I have a picture of that.

I’m also interested in " ESP32 MCU ", it will be helpful if there are any possible references for me (as a beginner)to read. On the other side, it brings me worried because teachers’ idea is to project machine learning. is there a possible dashboard from Blynk that can be in CSV format ( because it seems working and can receive real-time data) so I can make a script in Python ( like my idea picture flowchart ) to showcase the learning process of the model and be back deployed in the sensor?

This is a good starting-point…

Not really. The Terminal widget could be used, but it’s not really a suitable solution in my opinion.

Once again, I think you’re approaching this from the wrong angle.
Surely you should be using a closed-loop system where the MCU monitors the pump and handles the machine learning internally, with data outputted to Blynk to keep the user notified of anomalies and how they have been handled, with Blynk also used to allow the user to tweak the parameters used by the ML system.


I think I need a reading reference about “using a closed-loop system where the MCU monitors the pump”. I’m also grateful for the ESP32 reference maybe your guide will help fix my PoV about IoT machine learning ( or maybe there is some example project of your experience so I can learn to start from the beginning)

You can use Edge Impulse to build a ML model to run on device and then use Blynk to monitor and control the device remotely + send alerts.