Industrial Edge AI: Deploying Micro-LLMs and Anomaly Detection on ESP32 & Raspberry Pi Networks

In industrial manufacturing, oil refineries, and smart agriculture, internet connectivity to cloud servers can be intermittent or bandwidth-restricted.

Industrial Edge AI runs lightweight neural network models directly on microcontrollers (ESP32, STM32) and edge gateways (Raspberry Pi 5, Jetson Orin) to analyze vibration, temperature, and acoustic signals locally.

1. Predictive Maintenance Architecture

[ Industrial Vibration Sensor ]
               |
               v
[ ESP32 Microcontroller (TinyML FFT Model) ]
               |
               |-- Anomaly Score > Threshold
               v
[ Raspberry Pi Edge Gateway (SLM Reasoning Agent) ]
               |
               v
[ Automated Machinery Shutdown & Maintenance Alert ]

2. C++ TinyML Execution snippet

#include <TensorFlowLite_ESP32.h>

void loop() {
    float rawSensorData[64];
    readVibrationSensor(rawSensorData);
    
    // Run TinyML Inference in SRAM (< 100 KB memory)
    TfLiteStatus status = interpreter->Invoke();
    float anomalyScore = outputTensor->data.f[0];
    
    if (anomalyScore > 0.85) {
        digitalWrite(ALARM_RELAY_PIN, HIGH);
    }
}

Deploying TinyML on microcontrollers enables real-time physical safety controls without cloud dependence.