Heavy-Duty Vehicle State Monitoring for Workflow Bottlenecks
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Solution Overview
Problem
Existing management systems for heavy-duty vehicles in infrastructure projects lack efficiency monitoring and require extensive asset upgrades, are not agnostic to equipment type and brand, and fail to detect complex interdependencies between assets.
Innovation Solution
A production asset monitoring system using state machines on portable devices or in-vehicle units to automatically detect operating states, aggregate data, and analyze transitions between states, leveraging sensors and V2X communication to identify interdependencies and optimize workflows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If management systems monitor efficiency of heavy-duty vehicles in infrastructure projects, then productivity and workflow optimization improve, but device complexity and implementation cost increase
Solution Approach 1:
The state machine framework is designed to be universally applicable across multiple heavy-duty vehicle types (excavators, dump trucks, wheel loaders, conveyors) and infrastructure project contexts. A single monitoring system can track diverse assets using common state categories (productive, unproductive, idle), eliminating the need for separate monitoring systems for each vehicle type and reducing overall system complexity.
Solution Approach 2:
The system changes parameters by categorizing continuous operational data into discrete state parameters (productive/unproductive/idle) that can be easily aggregated and analyzed. This parameter transformation simplifies complex operational monitoring into manageable state transitions that can be processed by the monitoring system without requiring complex analytical algorithms.
2Measurement precision
If management systems require extensive production asset upgrades to monitor operating states, then measurement precision improves, but ease of manufacture and implementation worsen
Solution Approach 1:
The state machine acts as an intermediary layer between raw sensor data from production assets and the monitoring system. Instead of requiring direct integration with complex asset control systems, the state machine processes sensor inputs (GPS location, operational sensors) and outputs standardized state information, simplifying the integration process while maintaining measurement precision.
Solution Approach 2:
The system creates a virtual model (state machine) that copies and represents the operational states of physical assets without requiring physical modifications to the assets themselves. This virtual representation allows precise monitoring while avoiding the need for extensive hardware upgrades or modifications to the production assets.
3Measurement precision
If management systems are specific to certain equipment types and brands, then measurement precision for that equipment improves, but adaptability to different equipment types worsens
Solution Approach 1:
The monitoring system is designed with universal state machine templates that can be applied to any heavy-duty vehicle or construction equipment regardless of manufacturer or model. The system recognizes equipment-agnostic parameters (GPS coordinates, operational status sensors) and applies the same state categorization logic across all asset types, enabling precise monitoring while maintaining full adaptability.
Solution Approach 2:
The system segments monitoring into independent state machine modules that can be individually configured for different equipment types while sharing a common framework. Each asset type can have customized state transition rules while maintaining compatibility with the overall monitoring system, allowing precision for specific equipment while preserving adaptability across diverse fleets.
4Device complexity
If management systems manually track asset states instead of automated detection, then device complexity decreases, but loss of time and productivity worsen
Solution Approach 1:
The state machine system is self-service in that it automatically detects and tracks asset states using onboard sensors and GPS data without requiring manual intervention. The system autonomously processes sensor inputs, determines current states, and logs transitions, eliminating the need for manual tracking while keeping the monitoring system relatively simple and automated.
Solution Approach 2:
The system implements continuous feedback loops where sensor data from assets is automatically processed by state machines that adjust state classifications in real-time. This automated feedback mechanism eliminates manual tracking time while maintaining system simplicity through rule-based state transition logic rather than complex algorithms.
Data Source
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AI summary
A production asset monitoring system, for monitoring current operating states of one or more production assets, where the production asset monitoring system is arranged to associate a respective state machine which each asset in the one or more production assets, where each state machine implements a plurality of operating states, out of which operating states at least one state is categorized as a productive state and at least one state is categorized as an unproductive state, where the production asset monitoring system further comprises a sensor system configured to detect a current operating state for each state machine, and where the production asset monitoring system is configured to aggregate the states of a plurality of assets into a report indicative of production asset utilization.