Gearbox fault detection using vibration sensors with over 99.9% accuracy

For complex industrial systems like gearboxes, it is critical to prevent unexpected equipment failures and optimize maintenance schedules. By monitoring vibration patterns through sensors and machine learning, anomalies can be detected before they become costly failures, moving from reactive or scheduled maintenance to a more efficient predictive approach that reduces downtime and extends equipment lifespan.

Edge AI enables real-time anomaly detection by processing vibration data locally at the machine level. This eliminates cloud dependency and latency while continuously learning normal patterns and identifying deviations. It transforms maintenance from fixed schedules to condition-based interventions, improving response times and enabling scalable deployment across multiple industrial machines.

This use case is powered by TI's proprietary anomaly detection model that has been co-optimized for TI's low-power MCU architecutre. The TI's MSP family of microcontrollers is powered by TinyEngine™ NPU and the anomaly detection model are optimized to reach over 99.9% accuracy.

Data collection

The gearbox fault detection dataset includes the vibration dataset recorded by SpectraQuest’s Gearbox Fault Diagnostics Simulator. Dataset has been recorded using 4 vibration sensors placed in four different direction, and under variation of load from '0' to '90' percent.Two different scenario are included: healthy condition and broken tooth condition.

There are 20 files in total, 10 for healthy gearbox and 10 from broken one. Each file corresponds to a given load from 0% to 90% in steps of 10%. This open-source dataset is available on Kaggle.

Data quality assessment

Ensure your AI model is build on data that truly matters. Here is the visualization of testing dataset using LDA (Linear Discriminant Analysis) and PCA (Principal component analysis) conponents. 

Accelerate development with advanced optimization and searching algorithms for design, training, and depolying AI models with superior performance. 

CCStudio™ Edge AI Studio gives a start to finish workflow for deploying trained models to embedded targets.For developers seeking deeper customization and control, the MSPM0-SDK offers a comprehensive framework for building and integrating edge AI functionality into your own embeded applications.

The MSP-series MCUs deliver scable performance for executing and accelerating gearbox fault detection models, along with key SoC features critical to your application.