Hardware Guide
The STM32F4 is a widely used Cortex-M4 for vibration-based predictive maintenance. With 192 KB SRAM, 168 MHz clock, and DSP instructions, it runs TFLite Micro vibration classifiers with fast inference — reliable, affordable, and backed by the largest STM32 ecosystem.
| Spec | STM32F4 |
|---|---|
| Processor | ARM Cortex-M4F @ 168 MHz |
| SRAM | 192 KB |
| Flash | 1 MB |
| Key Features | Single-precision FPU, DSP instructions, Widely available ecosystem |
| Connectivity | USB OTG FS |
| Price Range | $3 - $10 (chip), $10 - $30 (dev board) |
The STM32F4's 192 KB SRAM provides 3x the 64 KB minimum for predictive maintenance models. A typical vibration classifier (30 KB model, 15 KB tensor arena) uses under 50 KB total — leaving 140+ KB for the application stack, sensor buffers, and communication protocols. The Cortex-M4F's DSP instructions and single-precision FPU accelerate FFT computation for vibration feature extraction. TFLite Micro uses CMSIS-NN optimized kernels on the Cortex-M4, delivering significant speedup over generic C implementations. The STM32F4 is one of the most widely deployed STM32 families — extensive documentation, community examples, and long-term availability (10+ year production guarantee from ST). The 168 MHz clock handles vibration analysis up to ~1 kHz comfortably. For higher-frequency analysis, step up to the STM32H7. Connectivity requires external modules, but industrial environments typically use Ethernet, RS-485, or CAN bus interfaces — all well-supported on the STM32F4.
Set up STM32CubeIDE for STM32F4
Install STM32CubeIDE and create a project for your STM32F407-Discovery board. Use STM32CubeMX to configure I2C for the accelerometer and UART/SPI for communication.
Integrate TFLite Micro with CMSIS-NN
Add TFLite Micro to your project. Enable CMSIS-NN and CMSIS-DSP libraries in the project settings. These provide optimized kernels for Cortex-M4 that TFLite Micro uses automatically during inference.
Build the vibration analysis pipeline
Sample the accelerometer at 200-500 Hz via I2C with DMA. Compute FFT on 256-512 sample windows using CMSIS-DSP's arm_rfft_fast_f32. Extract spectral features (peak frequency, RMS, crest factor) as model input.
Train and deploy the classifier
Train a 1D CNN or dense network on FFT features from labeled vibration data (normal, bearing-wear, misalignment, etc.). Quantize to int8, target model size under 30 KB. Embed in firmware and run inference per FFT window.
3x the clock speed and 4x the RAM for high-frequency vibration analysis (up to 10 kHz). Edge Impulse simplifies DSP. Higher cost, justified for critical machinery monitoring.
Built-in Wi-Fi for wireless monitoring at similar RAM (520 KB). Edge Impulse's pipeline handles DSP automatically. Better for retrofitting existing machinery without wired connections.
Deploy vibration monitoring to STM32F4 visually — from sensor input to maintenance alert in one workflow.
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