Show HN: MCP server for vibration-based predictive maintenance (github.com)

A new Model Context Protocol (MCP) server has been introduced for vibration-based predictive maintenance, allowing industrial machinery diagnostics to be conducted through large language models (LLMs) like Claude. This innovative system empowers users to perform AI-driven vibration analysis, detect bearing faults, and manage maintenance workflows using natural language interactions. Key technical features include the ability to transform raw vibration data into actionable ISO-compliant reports, conduct FFT spectrum and envelope analysis, and implement machine learning models for anomaly detection with semisupervised learning. This development is significant for the AI/ML community as it bridges the gap between sophisticated diagnostic tools and conversational AI, enhancing the accessibility of expert-level machinery diagnostics for engineers. By integrating real vibration data from 20 production-quality signals—including both healthy and faulty machinery—the MCP server showcases advanced capabilities like automatic sampling rate detection and the generation of interactive HTML reports. This proof of concept demonstrates the feasibility of utilizing LLMs in predictive maintenance, inviting community collaboration to further refine and expand the system's diagnostic functions and datasets, ultimately supporting the vision of smarter, more efficient manufacturing processes in the era of Industry 4.0.