Predictive Maintenance Platform
Predictive Maintenance for Automotive Manufacturing.
A leading automotive parts manufacturer was experiencing significant losses due to unplanned equipment downtime on their CNC machining and stamping press lines. Breakdowns during peak production shifts were costing approximately $45,000 per hour in lost output, overtime labor, and expedited shipping for late orders.
Artes Solution designed and deployed a comprehensive predictive maintenance platform that monitors 120+ machines across 3 production halls using vibration sensors, temperature probes, current transformers, and acoustic emission detectors. The system captures over 2 million data points per day, processes them through custom machine learning models at the edge, and predicts equipment failures 2-4 weeks before they occur.
Within the first 6 months, unplanned downtime was reduced by 42%, spare parts inventory costs decreased by 18% through just-in-time ordering, and the overall OEE improved from 68% to 84% across all monitored production lines.
Project Results
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Downtime Reduction
42% -
OEE Improvement
84%
How It Works.
Sensor Network
480+ industrial-grade sensors deployed across CNC machines, hydraulic presses, and conveyor systems. Vibration (tri-axial accelerometers), temperature (RTD PT100), current (split-core CTs), and acoustic emission sensors capture high-frequency data at 10kHz sampling rate for early fault signature detection.
Edge Processing
Custom ESP32 and STM32 based data acquisition units perform FFT analysis, RMS calculation, and feature extraction at the edge. Processed features are transmitted via MQTT over the factory Wi-Fi network to local Raspberry Pi gateways that run lightweight ML inference models for real-time anomaly scoring.
ML Prediction Engine
Isolation Forest and LSTM-based models trained on 18 months of historical failure data detect bearing wear, spindle imbalance, hydraulic pressure decay, and belt degradation patterns. The system generates maintenance recommendations with confidence scores and estimated remaining useful life (RUL) for each monitored component.
Web Dashboard
Real-time .NET Core web application with SignalR live updates showing machine health scores, vibration spectrum analysis, trend charts, and alarm management. Role-based access for operators, maintenance engineers, and plant managers with mobile-responsive design for shop floor tablets.
CMMS Integration
Automated work order generation in the customer's SAP PM module when failure probability exceeds configured thresholds. Spare part recommendations, estimated labor hours, and priority ranking ensure maintenance teams can plan interventions during scheduled downtime windows.
OEE Analytics
Automatic OEE calculation with availability, performance, and quality loss categorization. Pareto analysis of downtime causes, shift-by-shift comparison, and monthly improvement tracking reports that helped the plant achieve a 16-point OEE increase within the first operational year.
Want a Similar Solution for Your Factory?
We can design a predictive maintenance system tailored to your equipment, production process, and maintenance workflow. Let's discuss how we can reduce your unplanned downtime.
