🤖 Case Study: Industrial Plant Monitoring and Analytics Using Machine Learning

Sector: Industrial Maintenance & Monitoring
Role: Lead System Developer & ML Architect
Client Impact: From reactive failures to proactive performance

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🎯 Objective

To build a smart, predictive system that proactively identifies equipment issues before they escalate—improving uptime and reducing emergency repairs.

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⚠️ Challenge

With 30+ critical motors and hundreds of real-time sensors, the plant struggled with:

• Information overload for human operators
• Delayed responses to early signs of failure
• Lack of centralized intelligence for predictive insights

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🧩 Solution

I architected and built an end-to-end monitoring and ML system to filter noise and surface only what matters.

Key Components:

• ML Notification Engine: Built in Python, it reduced thousands of data points into a single, actionable alert.
• Modular ML Framework: Designed for easy retraining and plug-and-play expansion.
• IoT Communication Stack: TCP, WebSockets, and Unix sockets for reliable, low-latency data flows.
• Custom Hardware: Designed PCBs and firmware to support robust, long-term equipment monitoring.
• Operator UI: SCADA dashboard using JavaScript + Chart.js for real-time visual feedback.

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📊 Impact

• ✅ Simplified decision-making through a single-source intelligent alerting system
• 🧠 Reduced operator fatigue by eliminating data overload
• ⚙️ Laid the foundation for a future zero-downtime environment

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🧪 Technical Stack

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💡 Key Takeaway

Intelligence isn't about more data—it's about the right data at the right time. This system was built to think for the operator, not just display numbers.