
Developed and delivered an Industrial Machinery Diagnostics MCP Server for the modelcontextprotocol/servers repository, focusing on predictive maintenance for industrial automation. The server integrated over 25 diagnostic tools, including FFT and envelope analysis, and implemented machine learning-based anomaly detection using Python, leveraging models such as OneClassSVM and LocalOutlierFactor. Interactive HTML reports were generated with Plotly to visualize diagnostics and anomalies, supporting actionable maintenance decisions and ISO 20816 compliance. The work emphasized robust documentation, MCP protocol alignment, and repository onboarding improvements, establishing a scalable foundation for future data-driven maintenance analytics and reducing equipment downtime through proactive diagnostics and clear reporting.
November 2025 Monthly Summary for modelcontextprotocol/servers: Key features delivered: - Industrial Machinery Diagnostics MCP Server (Predictive Maintenance MCP Server) added, featuring 25+ diagnostic tools (FFT, envelope analysis, ISO 20816 compliance) and ML-based anomaly detection (OneClassSVM, LocalOutlierFactor). - Interactive HTML reports generated with Plotly to visualize diagnostics and anomalies for actionable maintenance decisions. Major bugs fixed: - No major defects reported in this repository this month; primary focus was feature delivery, documentation, and quality improvements to support MCP standards. Overall impact and accomplishments: - Enables proactive maintenance and reduced downtime for industrial equipment by providing a scalable diagnostics and anomaly-detection platform with clear reporting and compliance with MCP protocol. - Establishes a solid foundation for future tooling expansion and data-driven maintenance analytics. Technologies/skills demonstrated: - Python-based server development and integration with ML models for anomaly detection. - Data analysis with FFT and envelope analysis techniques; ISO 20816 compliance awareness. - Visualization with Plotly; thorough documentation, README governance, MIT license adherence, and MCP protocol alignment.
November 2025 Monthly Summary for modelcontextprotocol/servers: Key features delivered: - Industrial Machinery Diagnostics MCP Server (Predictive Maintenance MCP Server) added, featuring 25+ diagnostic tools (FFT, envelope analysis, ISO 20816 compliance) and ML-based anomaly detection (OneClassSVM, LocalOutlierFactor). - Interactive HTML reports generated with Plotly to visualize diagnostics and anomalies for actionable maintenance decisions. Major bugs fixed: - No major defects reported in this repository this month; primary focus was feature delivery, documentation, and quality improvements to support MCP standards. Overall impact and accomplishments: - Enables proactive maintenance and reduced downtime for industrial equipment by providing a scalable diagnostics and anomaly-detection platform with clear reporting and compliance with MCP protocol. - Establishes a solid foundation for future tooling expansion and data-driven maintenance analytics. Technologies/skills demonstrated: - Python-based server development and integration with ML models for anomaly detection. - Data analysis with FFT and envelope analysis techniques; ISO 20816 compliance awareness. - Visualization with Plotly; thorough documentation, README governance, MIT license adherence, and MCP protocol alignment.

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