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:

  • Internet independence
  • No waste of energy and money on data transfer
  • Advanced privacy and security

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

  • Neuton TinyML: Neuton, I selected this solution since it is free to use and automatically creates tiny machine learning models deployable even on 8-bit MCUs. According to Neuton developers, you can create a compact model in one iteration without compression.
  • Raspberry Pi Pico: The chip employs two ARM Cortex-M0 + cores, 133 megahertz, which are also paired with 256 kilobytes of RAM when mounted on the chip. The device supports up to 16 megabytes of off-chip flash storage, has a DMA controller, and includes two UARTs and two SPIs, as well as two I2C and one USB 1.1 controller. The device received 16 PWM channels and 30 GPIO needles, four of which are suitable for analog data input. And with a net $4 price tag.

Let's Build It

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' per cent. 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:

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).

It's time to select the target variable or the output you want for each prediction. In this case, we have class as Output Variable: 'target'

Since the dataset is a vibration, we need to prepare the data before training the model. To do this, I select the setting Digital Signal Processing (DSP).

Digital Signal Processing (DSP) option enables automatic preprocessing and feature extraction for data from gyroscopes, accelerometers, magnetometers, electromyography (EMG), etc. Neuton will automatically transform raw data and extract additional features to create precise models for signal classification.

For this model, we use Accuracy as a metric (but you can experiment with all available metrics).

While the model is being trained, you can check out Exploratory Data Analysis generated once the data processing is complete, check the below video:

The target metric for me was: Accuracy 0.921372 and the trained model had the following characteristics:

Number of coefficients = 397, File Size for Embedding = 2.52 Kb. That's...

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