readme update: version

Author: LGDiMaggio

CITATION.cff

@@ -1,8 +1,8 @@
 cff-version: 1.2.0
 message: "If you use this software, please cite it as below."
-title: "Predictive Maintenance MCP Server"
-version: 0.2.0
-date-released: 2025-11-12
+title: "Predictive Maintenance MCP Server: A Proof of Concept"
+version: 0.3.0
+date-released: 2025-01-16
 authors:
   - family-names: Di Maggio
     given-names: Luigi Gianpio
@@ -12,24 +12,34 @@ authors:
 repository-code: "https://github.com/LGDiMaggio/predictive-maintenance-mcp"
 url: "https://github.com/LGDiMaggio/predictive-maintenance-mcp"
 abstract: >
-  A Model Context Protocol server that brings industrial machinery
-  diagnostics directly to Large Language Models like Claude, enabling
-  AI-powered vibration analysis, bearing fault detection, and predictive
-  maintenance through natural conversation. Features include FFT spectrum
-  analysis, envelope analysis for bearing faults, ISO 20816-3 compliance
-  checks, and ML anomaly detection with interactive HTML reports.
+  A Proof of Concept Model Context Protocol server demonstrating how Large
+  Language Models like Claude can be empowered with industrial machinery
+  diagnostics capabilities. Features real vibration data (15 bearing fault
+  signals), FFT spectrum analysis, envelope analysis for bearing faults,
+  ISO 20816-3 compliance checks, ML anomaly detection with semi-supervised
+  learning, and interactive HTML reports. Demonstrates the feasibility of
+  natural language interfaces for complex industrial diagnostics workflows.
 keywords:
+  - signal-processing
+  - iiot
+  - predictive-analytics
+  - anomaly-detection
+  - asset-management
+  - envelope-analysis
   - predictive-maintenance
-  - vibration-analysis
   - condition-monitoring
-  - bearing-fault-detection
+  - machine-failure
+  - bearing-fault-diagnosis
+  - industrial-iot
+  - fault-diagnosis
+  - vibration-analysis
+  - claude-ai
+  - industry-4-0
   - model-context-protocol
-  - mcp
-  - fft-analysis
-  - envelope-analysis
-  - iso-20816
-  - machine-learning
-  - anomaly-detection
+  - mcp-server
+  - fastmcp
+  - iso10816
+  - claude-agents
 license: MIT
 # Zenodo DOI
 # identifiers:
@@ -38,11 +48,11 @@ license: MIT
 #     description: "Zenodo DOI for this version"
 preferred-citation:
   type: software
-  title: "Predictive Maintenance MCP Server"
+  title: "Predictive Maintenance MCP Server: A Proof of Concept"
   authors:
     - family-names: Di Maggio
       given-names: Luigi Gianpio
   year: 2025
-  version: 0.2.0
+  version: 0.3.0
   repository-code: "https://github.com/LGDiMaggio/predictive-maintenance-mcp"
   license: MIT

README.md

@@ -15,6 +15,7 @@ A Model Context Protocol server that brings **industrial machinery diagnostics**
 ## 📑 Table of Contents
 
 - [✨ What Makes This Special](#-what-makes-this-special)
+- [🔬 Proof of Concept & Community Invitation](#-proof-of-concept--community-invitation)
 - [🎬 Quick Examples](#-quick-examples)
 - [🚀 Installation](#-installation)
 - [Configuration](#configuration)
@@ -42,6 +43,44 @@ A Model Context Protocol server that brings **industrial machinery diagnostics**
 - **🔍 Advanced Diagnostics** - FFT spectrum analysis, envelope analysis for bearing faults, time-domain feature extraction
 - **🚀 Zero Configuration** - Works out of the box with sample data, auto-detects sampling rates from metadata
 
+## 🔬 Proof of Concept & Community Invitation
+
+**This is a Proof of Concept (PoC)** - a demonstration of feasibility showing how LLMs can be empowered with industrial diagnostics capabilities through the Model Context Protocol.
+
+### PoC Goals Achieved ✅
+
+- ✅ **Real vibration data integration** - 15 bearing fault signals with metadata
+- ✅ **Professional analysis workflows** - FFT spectrum, envelope analysis, ISO 20816-3 compliance
+- ✅ **ML anomaly detection** - Semi-supervised learning with hyperparameter tuning
+- ✅ **Metadata-driven auto-detection** - Sampling rates and signal units from JSON files
+- ✅ **Interactive HTML reports** - Plotly visualizations with automatic peak detection
+- ✅ **Natural language interface** - Complex diagnostics through conversational AI
+
+### 🤝 We Invite the Community to Contribute!
+
+This PoC demonstrates the potential - now we need **your expertise** to make it production-ready.
+
+**How You Can Help:**
+
+- **📊 Add More Data** - Real-world vibration datasets from different machines/sensors/industries
+- **🔧 Expand Diagnostics** - Gear fault detection, pump cavitation, motor electrical faults, misalignment
+- **🤖 Improve ML** - Deep learning approaches, advanced anomaly models, real-time streaming analysis
+- **📏 Standards Compliance** - Additional ISO standards (10816, 14839), NIST guidelines, API 670
+- **🌍 Internationalization** - Multi-language support, regional standards (VDI, GB)
+- **📖 Documentation** - Tutorials, case studies, industrial validation, best practices
+- **🐛 Testing** - Edge cases, validation with ground truth data, cross-validation studies
+
+**Get Involved:**
+
+- 🐛 **Report issues**: [GitHub Issues](https://github.com/LGDiMaggio/predictive-maintenance-mcp/issues)
+- 💡 **Suggest features**: [Discussions](https://github.com/LGDiMaggio/predictive-maintenance-mcp/discussions)
+- 🔀 **Submit PRs**: See [CONTRIBUTING.md](CONTRIBUTING.md)
+- 💬 **Join the conversation**: [LinkedIn](https://www.linkedin.com/in/luigi-gianpio-di-maggio)
+
+**Why This Matters:**
+
+Predictive maintenance is critical for Industry 4.0, but expertise is scarce and analysis tools are fragmented. By combining LLM reasoning with diagnostic tools through MCP, we can **democratize access to expert-level machinery diagnostics** and make industrial AI more accessible to engineers worldwide.
+
 ## 🎬 Quick Examples
 
 ### Example 1: Bearing Fault Detection
@@ -496,13 +535,13 @@ The system now includes a hybrid documentation reader that combines:
 **Known Limitations:**
 - PDF reading requires PyPDF2 (optional dependency)
 - Limited bearing catalog (extensible)
-- No OCR for scanned PDFs (planned for v0.3.0)
+- No OCR for scanned PDFs (planned for v0.4.0)
 
 See [resources/machine_manuals/README.md](resources/machine_manuals/README.md) for usage guide.
 
 ---
 
-### 🔮 Planned for v0.3.0
+### 🔮 Planned for v0.4.0
 
 #### 🔍 Vector Search for Large Documents
 For manuals >100 pages, semantic search will be more efficient:

pyproject.toml

@@ -4,28 +4,34 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "predictive-maintenance-mcp"
-version = "0.2.0"
-description = "AI-Powered Predictive Maintenance & Fault Diagnosis MCP Server - Industrial machinery condition monitoring, vibration analysis, bearing diagnostics, and ML-based anomaly detection through Model Context Protocol"
+version = "0.3.0"
+description = "Proof of Concept: AI-Powered Predictive Maintenance & Fault Diagnosis MCP Server - Industrial machinery condition monitoring, vibration analysis, bearing diagnostics, and ML-based anomaly detection through Model Context Protocol"
 readme = "README.md"
 authors = [
     { name = "Luigi Di Maggio", email = "luigi.dimaggio@example.com" }
 ]
 license = { text = "MIT" }
 keywords = [
+    "signal-processing",
+    "iiot",
+    "predictive-analytics",
+    "anomaly-detection",
+    "asset-management",
+    "envelope-analysis",
     "predictive-maintenance",
     "condition-monitoring",
+    "machine-failure",
+    "bearing-fault-diagnosis",
+    "industrial-iot",
     "fault-diagnosis",
     "vibration-analysis",
-    "mcp-server",
     "claude-ai",
-    "machine-learning",
-    "anomaly-detection",
-    "bearing-diagnostics",
-    "fft",
-    "envelope-analysis",
-    "iso-20816",
-    "industrial-iot",
-    "industry-4-0"
+    "industry-4-0",
+    "model-context-protocol",
+    "mcp-server",
+    "fastmcp",
+    "iso10816",
+    "claude-agents"
 ]
 classifiers = [
     "Development Status :: 4 - Beta",