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IoT Predictive Maintenance System

Manufacturing IoT & ML

How FábricaInteligente Reduced Unplanned Downtime by 45% with AI-Powered Predictive Maintenance

A comprehensive IoT solution that monitors 100+ industrial machines in real-time, predicting failures up to 5 days in advance and reducing maintenance costs by 30% through machine learning algorithms and edge computing.

Client: FabricaInteligente

https://FabricaInteligente.xcodeit.io/dashboard

PredictMaint IoT

1,200 Sensors

Downtime

-40%

Prediction

92%

accuracy

Maint. Cost

-30%

Equipment

350+

Equipment Health Monitor

CNC-01

98%

CNC-02

95%

PRESS-01

72%

CONV-03

45%

Vibration Analysis - CONV-03

Active Alerts

CRITICAL

CONV-03 bearing failure

Predicted: 48h

WARNING

PRESS-01 oil level low

45%

Reduction in Unplanned Downtime

30%

Lower Maintenance Costs

100+

Machines Monitored 24/7

5 Days

Failure Prediction Window

The Challenge

FábricaInteligente, a leading automotive components manufacturer, faced a critical problem: unpredictable machine failures were causing costly production halts and emergency repairs. With over 100 CNC machines, hydraulic presses, and robotic assembly lines operating 24/7, the traditional reactive maintenance approach was bleeding the company dry. Each unplanned stop cost approximately $15,000 per hour in lost production, rush parts ordering, and overtime labor. The maintenance team was perpetually in crisis mode, firefighting instead of planning. Meanwhile, preventive maintenance schedules based on arbitrary time intervals led to unnecessary part replacements and wasted resources. The company needed a paradigm shift: from reacting to failures to predicting them before they occurred. They required a system that could monitor equipment health in real-time, analyze patterns invisible to human operators, and provide actionable insights days before a breakdown. The solution had to integrate seamlessly with their existing machinery without disrupting production, operate reliably in harsh industrial environments, and scale across their entire facility.

Our Solution

XCodeIT designed and implemented an end-to-end IoT predictive maintenance platform that combines edge computing, industrial sensors, and machine learning to transform raw machine data into predictive intelligence. Our solution monitors vibration, temperature, current draw, acoustic signatures, and operational patterns across all critical equipment.

Deployed ruggedized ESP32 sensor nodes on 100+ machines, measuring vibration (3-axis accelerometers), temperature (thermocouples), current consumption (Hall effect sensors), and acoustic emissions. Each node performs edge preprocessing to reduce bandwidth and enable offline operation during network disruptions.

Implemented a redundant MQTT broker infrastructure with QoS 2 reliability, handling 50,000+ sensor readings per second. Designed topic hierarchies for efficient data routing and implemented security through TLS encryption and certificate-based authentication for all device communications.

Built a scalable InfluxDB time-series database optimized for high-frequency industrial data ingestion. Implemented intelligent data retention policies, continuous queries for real-time aggregations, and compression strategies that reduced storage costs by 70% while maintaining query performance.

Developed TensorFlow-based anomaly detection and failure prediction models using LSTM neural networks trained on 18 months of historical failure data. The models analyze multivariate time-series patterns to predict specific failure modes (bearing wear, hydraulic leaks, electrical faults) 3-5 days in advance with 87% accuracy.

Created comprehensive Grafana dashboards providing machine health scores, anomaly alerts, prediction timelines, and maintenance recommendations. Implemented role-based views for operators, maintenance teams, and management, with mobile-responsive interfaces for on-the-go monitoring.

Designed a multi-channel alerting system that routes notifications via email, SMS, Slack, and industrial SCADA interfaces based on urgency and responsibility. Implemented alert fatigue reduction through intelligent grouping, escalation rules, and adaptive thresholds that learn from operator feedback.

Technologies Used

C/C++ ESP32 MQTT InfluxDB Python TensorFlow Grafana

The Results

45%

Reduction in Unplanned Downtime

Predictive alerts enabled proactive maintenance scheduling, virtually eliminating surprise failures during production hours and allowing repairs during planned maintenance windows.

30%

Lower Maintenance Costs

Condition-based maintenance replaced time-based schedules, eliminating unnecessary part replacements while preventing catastrophic failures that require expensive emergency repairs and rush shipping.

100+

Machines Monitored 24/7

Complete coverage across CNC machines, hydraulic presses, robotic arms, and conveyor systems, providing a comprehensive view of facility health and enabling data-driven capital expenditure planning.

5 Days

Failure Prediction Window

Machine learning models predict specific failure modes up to 5 days in advance, providing ample time to order parts, schedule technicians, and plan production adjustments without disruption.

"XCodeIT's predictive maintenance system has fundamentally changed how we operate. We've gone from constantly reacting to breakdowns to confidently planning our maintenance weeks in advance. The system paid for itself in just four months through prevented emergency repairs alone. Now we're expanding it to our other two facilities because the ROI is simply undeniable."

Ricardo Mendes

Project Details

Industry: Manufacturing & Industry 4.0
Services: IoT Development, Machine Learning, Embedded Systems, Data Analytics
Duration: 8 months
Team Size: 6 specialists (IoT Engineers, Data Scientists, Backend Developers)