Advanced Predictive Maintenance Mobile Application for Civil Engineering Machinery

The client faces frequent machinery downtime caused by lengthy manual inspections of moving parts in civil engineering equipment. Current measurement tools lack the necessary accuracy (within 15 mm) to reliably assess wear, leading to delayed replacements and increased operational costs. Manual inspections are also resource-intensive and prone to errors, impacting overall productivity and maintenance efficiency.

A mid-sized construction equipment manufacturer seeking to reduce machinery downtime and manual inspection costs through automated, high-precision monitoring solutions.

• Develop a mobile application that automates the measurement of critical moving parts on civil engineering machinery with a measurement accuracy within 15 mm.
• Implement an object recognition and deep learning model capable of analyzing images of machinery parts to predict wear and remaining service life.
• Reduce manual inspection time and errors, thereby decreasing machinery downtime.
• Enable accessible, user-friendly interface for operators without technical backgrounds.
• Incorporate features for diagnostics, equipment linking, and workflow management to support maintenance planning.

• Image capture and processing capabilities for machinery parts in varying lighting conditions.
• Object recognition model trained on a diverse image dataset to identify wear indicators with high accuracy.
• Size reference measurement using known dimensions of machinery components (e.g., track shoe height).
• Prediction engine to determine whether parts need immediate replacement or schedule maintenance based on wear level.
• Display of diagnostics and remaining service hours for each inspected part.
• User authorization and profile management for operators.
• History logs and recent diagnostics overview.
• Ability to link multiple machinery units to a single user profile via unique identification numbers.
• Mapping feature for locating service centers and machinery deployment sites.
• Settings configuration for user preferences and operational parameters.

• Equipment identification systems for linking machinery units to profiles.
• Mapping and GPS systems for locating equipment and maintenance facilities.
• Existing asset management or diagnostics systems for data synchronization (if applicable).

• Measurement accuracy within 15 mm for all recognized parts.
• Real-time image processing with results delivered in under 5 seconds per inspection.
• High reliability and robustness in poor lighting or adverse conditions.
• Data security compliant with industry standards for sensitive operational data.
• Scalability to support increased user base and additional machinery units.

By deploying this mobile application, the client aims to significantly reduce equipment downtime and manual inspection efforts. Anticipated benefits include more accurate wear assessments, timely maintenance scheduling, and overall operational cost savings. Based on prior similar implementations, expected improvements in maintenance accuracy and efficiency could lead to a decrease in machinery downtime and inspection costs, ultimately enhancing productivity and profitability.