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Predictive Maintenance Mobile App for Civil Engineering Machinery Using Deep Learning and Object Recognition

sigma.software

Construction

Automotive

Manufacturing

Business Challenges in Machinery Maintenance

Lengthy manual inspections of excavator moving parts cause unplanned downtime and revenue loss. Existing measurement tools lack the 15mm precision required to detect wear between new and degraded components. Non-technical operators need accessible, accurate diagnostic capabilities at construction sites.

About the Client

Global leaders in construction equipment and connected solutions, seeking to reduce machinery downtime through automated inspection technologies.

Key Project Goals

Develop a mobile app for automated, high-precision wear analysis of excavator undercarriage components
Implement deep learning models for predictive maintenance using reference object calibration
Enable non-technical operators to perform diagnostics in varied lighting conditions
Integrate user management and equipment tracking features for fleet maintenance

Core System Capabilities

Object recognition system using excavator track shoe plate as reference measurement
Deep learning model for wear prediction and service hour estimation
User authentication and equipment profile linking via PIN
Real-time diagnostics visualization and historical data tracking
Interactive dealer map for replacement part procurement

Technology Stack

Unity 3D for cross-platform mobile development

TensorFlow/PyTorch for deep learning model training

OpenCV for image processing

Cloud-based model retraining infrastructure

System Integrations

Volvo equipment identification database
Cloud storage for diagnostic history
Third-party mapping API for dealer locations

Performance Criteria

Measurement accuracy within ±15mm tolerance
Low-light image processing capability
Sub-500ms inference time on mid-range mobile devices
Role-based access control for sensitive data

Expected Business Outcomes

Reduction of unplanned machinery downtime by 40-60% through proactive maintenance alerts. 70% faster inspection process enabling daily diagnostics. Scalable solution applicable to 200+ heavy equipment types across construction, mining, and logistics industries.