This article details a predictive maintenance system built on the iNeuOS IoT platform. It collects time series data from vibration sensors and uses the DeepSeek V4 Pro LLM to allow users to query equipment status in natural language. The system performs independent analysis of vibration channels and provides a comprehensive assessment, eliminating the need for SQL or specialized tools. This approach represents a significant advancement in making industrial IoT data accessible to non-technical stakeholders, reducing downtime and maintenance costs. The integration of LLMs with time series models offers a scalable pattern for predictive maintenance across various industries.
A case study on combining IoT data, time series analysis, and LLMs for predictive maintenance, enabling natural language queries for equipment status.