EMEA 2021 https://www.tinyml.org/event/emea-2021
Predicting Faults in a Water Pump and its Pipeline using TinyML
Mayank MATHUR, Senior Solutions Architect, NA
Monitoring and maintenance of rotating machineries like water pumps, wind turbines, electric motors etc. tends to be laborious, time-consuming and costly; especially monitoring the ones installed in remote locations by the utility companies for power generation, water supply & distribution or at the oil mines. The breakdowns not only heavily costs the business but the resulting disruption also causes inconvenience to the consumers. While some IoT sensors today can monitor the condition of the equipment but their major draw-back is that they rely on cloud based analytics for which they need to be connected to the internet most of the time resulting in significant power consumption. Such sensors are not at all suitable to be installed in remote locations with limited power and network availability.
Relevance to tinyML
Installing small and cheap sensors that can not only monitor but also analyse the vibrations generated by these machineries can help predict lots of faults well in advance before they become significant enough to cause a breakdown. A TinyML model built for the purpose and deployed on these sensors reduces the dependency on the cloud making it more suitable for installations in remote areas. A ML model deployed on the device itself also has multiple other benefits like low latency, low network bandwidth consumption, and improved data security.
The solution to the stated problem is to design a sensor that is capable of capturing the vibrations generated by a machinery and predicting faults in near real-time. Instead of continuously sending the vibrational data, the sensor only sends the results of the inferencing to the cloud. The technical approach and its novelty to validate if the proposed solution of deploying a ML model to a small microcontroller for PdM of machineries would practically work or not a sensor prototype with a STM32F411 microcontroller and a LIS3DH MEMS accelerometer was built. A separate setup with a small water pump was also created to test the sensor by installing it on top of the pump.
The novelty of the technical approach is in creating a setup where the faults can be manually and repeatedly generated to demonstrate and validate the reliability of the TinyML model running on a microcontroller. The sensor although installed on top of the pump is able to predict the faults in the water pipeline.
Results and their significance to the tinyML community
The result is a highly reliable model with 96.8% accuracy. This solution helps in establishing how TinyML can be leveraged to design better and more efficient solutions for remote monitoring in Industrial IoT.