Riyan

The first time I mounted an MPU6050 on a robot arm and expected clean, trustworthy angle data, I got garbage. Not "slightly off" garbage — drift -into-oblivion garbage, where the reported orientation would wander several degrees off true within a couple of minutes even sitting still on a table. My first instinct was to blame the sensor. It wasn't the sensor's fault. It's just what accelerometers and gyroscopes do on their own, and almost nobody tells you that up front — most tutorials show you the happy path and skip the part where your robot slowly forgets which way is up. This post is the fix I wish someone had handed me: sensor fusion . Specifically, how to combine two noisy, individually-flawed sensors into one signal good enough to actually control a robot with — and how to do it on hardware too small to run a "real" Kalman filter library off the shelf. Why one sensor is never enough Every sensor lies to you in a specific, predictable way:...

Predicting Mechanical Failure: Vibration Anomaly Detection with TinyML In industrial manufacturing, rotating machinery—pumps, compressors, CNC spindles, and gearboxes—gives clear warning signs long before catastrophic failure. These warnings manifest as subtle shifts in acoustic emissions, harmonic resonance, and multi-axis vibration patterns. Traditionally, identifying these defects required expensive external vibration analyzers or periodic manual technician inspections. Today, TinyML-powered condition monitoring enables continuous, low-cost predictive maintenance. By embedding unsupervised anomaly detection models directly onto microcontrollers connected to industrial accelerometers, machines can diagnose their own wear patterns in real time while drawing milliwatts of power. The Engineering Bottleneck: Raw Time-Series vs. Spectral Features Industrial accelerometers (such as the ADXL345 or high-bandwidth piezoelectric sensors) sample vibration across 3 axes at frequencies...