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I would like to share my project and show you how to apply tinyML approach to detect broken tooth conditions in the gearbox based upon recorded vibration data. Introduction and Business Constraint In industry (e.g., wind power, automotive), gearboxes often operate under random speed variations. A condition monitoring system is expected to detect faults, broken tooth conditions and assess their severity using vibration signals collected under different speed profiles. Modern cars have hundreds of thousands of details and systems where it is necessary to predict breakdowns, control the state of temperature, pressure, etc.As such, in the automotive industry, it is critically important to create and embed TinyML models that can perform right on the sensors and open up a set of technological advantages, such as:
In my experiment I want to show how to easily create such a technology prototype to popularize the TinyML approach and use its incredible capabilities for the automotive industry. Technologies Used
The goal of this tutorial is to demonstrate how you can easily build a compact ML model to solve a multi-class classification task to detect broken tooth conditions in the gearbox. Dataset Description Gearbox Fault Diagnosis Dataset includes the vibration dataset recorded by using SpectraQuest’s Gearbox Fault Diagnostics Simulator. Dataset has been recorded using 4 vibration sensors placed in four different directions and under variation of load from ‘0’ to ’90’ percent. Two different scenarios are included:1) Healthy condition 2) Broken tooth condition There are 20 files in total, 10 for a healthy gearbox and 10 for a broken one. Each file corresponds to a given load from 0% to 90% in steps of 10%. You can find this dataset through the link: https://www.kaggle.com/datasets/brjapon/gearbox-fault-diagnosis The experiment will be conducted on a $4 MCU, with no cloud computing carbon footprints 🙂 Step 1: Model training For model training, I’ll use the free of charge platform, Neuton TinyML. Once the solution is created, proceed to the dataset uploading (keep in mind that the currently supported format is CSV only). Number of coefficients = 397, File Size for Embedding = 2.52 Kb. That’s super cool! It is a really small model!Upon the model training completion, click on the Prediction tab, and then click on the Download button next to Model for Embedding to download the model library file that we are going to use for our device. Step 2: Embedding on Raspberry Pico Once you have downloaded the model files, it’s time to add our custom functions and actions. I am using Arduino IDE to program Raspberry Pico. Setting up Arduino IDE for Raspberry Pico: https://reddit.com/link/tkw3e1/video/qsmo4yepe5p81/player Note: Since we are going to make classification on the test dataset, we will use the CSV utility provided by Neuton to run inference on the data sent to the MCU via USB. Here is our project directory, I tried to build the same model with TensorFlow and TensorFlow Lite as well. My model built with Neuton TinyML turned out to be 4.3% better in terms of Accuracy and 15.3 times smaller in terms of model size than the one built with TF Lite. Speaking of the number of coefficients, TensorFlow’s model has, 9, 330 coefficients, while Neuton’s model has only 397 coefficients (which is 23.5 times smaller than TF!). The resultant model footprint and inference time are as follows: submitted by /u/sumitaiml |
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TinyML Gearbox Fault Prediction on a $4 MCU
