Construction Equipment Process Failure Detection via Sensor Monitoring
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Solution Overview
Problem
Current asset management systems in industries like construction often rely on outdated methods, leading to inefficiencies and inaccuracies in tracking and maintaining equipment, which can result in increased costs and reduced operational efficiency due to inadequate monitoring and maintenance scheduling.
Innovation Solution
A system and method for detecting construction equipment process failures by receiving information from reporting sources, populating a database, and providing reports if the equipment operates outside of assigned norms, enabling real-time monitoring and maintenance scheduling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If outdated asset management methods are used, then device complexity is reduced, but productivity and reliability deteriorate
Solution Approach 1:
The patent replaces manual, mechanical asset management methods with an automated electronic monitoring system. Sensors detect equipment parameters (vibration, temperature, pressure) and automatically transmit data to a central database, eliminating the need for manual inspection and paper-based tracking. This substitution of mechanical processes with electronic automation directly resolves the contradiction by significantly improving productivity while the modular sensor-based architecture keeps system complexity manageable.
Solution Approach 2:
The equipment monitoring system performs self-diagnosis and self-reporting functions. Sensors continuously monitor equipment status and automatically generate maintenance alerts when parameters exceed thresholds, eliminating the need for constant human intervention. The system serves itself by detecting its own operational state and initiating maintenance protocols, thereby improving operational efficiency without requiring complex human-operated management structures.
2Reliability
If real-time monitoring is implemented, then reliability is improved, but use of energy increases
Solution Approach 1:
The monitoring system employs periodic sampling of equipment parameters rather than continuous monitoring. Sensors take measurements at predetermined time intervals or when specific trigger conditions are met, allowing the system to maintain reliable detection capability while reducing overall energy consumption. This periodic action approach enables the system to balance reliability requirements with energy efficiency by being active only when necessary.
Solution Approach 2:
The system dynamically adjusts monitoring parameters based on equipment operational state. When equipment operates within normal parameters, monitoring frequency is reduced to conserve energy. When parameters approach critical thresholds or abnormal patterns are detected, the system automatically increases monitoring frequency and detail, thereby maintaining high reliability during critical periods while minimizing energy consumption during stable operation.
3Measurement precision
If comprehensive data collection is performed, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system extracts and collects only the most critical equipment parameters and deviation thresholds from comprehensive data sets. Rather than analyzing all possible equipment data points, the system identifies and monitors key indicators of equipment health and performance. This selective extraction maintains measurement precision for critical parameters while significantly reducing data processing time by eliminating unnecessary data collection and analysis.
Solution Approach 2:
The monitoring system implements real-time feedback loops that immediately process and act on detected parameter deviations. When sensors detect parameters exceeding predetermined thresholds, the system instantly generates alerts and initiates maintenance protocols without requiring comprehensive analysis of all equipment data. This feedback mechanism ensures high measurement precision for critical parameters while minimizing time loss through immediate automated response rather than delayed comprehensive analysis.
Data Source
AI summary
A method and system for detecting construction equipment process failure are disclosed. According to one embodiment, information about a construction equipment asset from a reporting source is received. A database is then populated with the information. A process failure report is provided if the construction equipment asset is operated in a manner which violates a process norm assigned to the construction equipment asset.