Machine State Sensing with Single GNSS and Sensor Fusion
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Solution Overview
Problem
Existing systems for determining the position and orientation of machines, particularly in earth-working activities, are costly due to the need for multiple GNSS sensors, and often require complex calibration procedures.
Innovation Solution
A method and system utilizing a single GNSS sensor combined with rotational and other sensors to determine machine orientation and position, leveraging data fusion techniques to enhance accuracy without extensive calibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional monitoring systems with multiple separate sensors are used, then comprehensive machine state data can be collected, but system complexity and cost increase
Solution Approach 1:
The patent combines multiple sensing functions (acoustic, vibration, thermal, etc.) into a single wearable device that attaches to the machine. This single device integrates multiple sensors and processing capabilities, eliminating the need for multiple separate monitoring systems while maintaining comprehensive machine state detection capabilities.
Solution Approach 2:
The wearable monitoring device is designed to perform multiple functions: detecting vibrations, acoustic emissions, temperature, and other machine state parameters simultaneously. This multi-functional approach allows one device to replace multiple specialized sensors, reducing overall system complexity while improving monitoring reliability.
2Reliability
If traditional wired monitoring systems are used, then stable data transmission can be achieved, but machine mobility and ease of installation are reduced
Solution Approach 1:
The patent replaces wired mechanical connections with wireless communication technology. The wearable device transmits machine state data wirelessly to remote monitoring systems, eliminating the need for physical cable connections while maintaining data transmission reliability through modern wireless protocols.
Solution Approach 2:
The monitoring system transitions from a static wired configuration to a dynamic wireless system. The wearable device can be easily attached and detached, moved between different machine locations, and adjusted as needed, providing flexibility and ease of installation while maintaining stable data transmission through adaptive wireless communication.
3Loss of time
If comprehensive machine monitoring is implemented, then predictive maintenance capability is improved, but energy consumption and device complexity increase
Solution Approach 1:
The wearable device continuously monitors machine state parameters in real-time, enabling ongoing predictive maintenance capabilities. This continuous monitoring detects early signs of machine degradation, allowing maintenance to be performed before failures occur, thus reducing unplanned downtime while managing energy consumption through efficient sensing and processing.
Solution Approach 2:
The device monitors changes in multiple machine state parameters (vibration frequency, amplitude, temperature, acoustic emissions) simultaneously. By tracking parameter changes over time and analyzing patterns, the system can predict failures and schedule maintenance optimally, reducing downtime while energy consumption is managed through intelligent sensor activation and data processing strategies.
Data Source
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AI summary
A machine (100) includes a rotational sensor (124) configured to sense rotation of an upper frame (102) of the machine relative to a lower frame (104) of the machine. The machine (100) also includes a three-dimensional position sensor (126) spaced from an axis of rotation (106) of the upper frame (102) relative to the lower frame (104). The machine (100) can also include a number of additional sensors including sensors to detect track movement (128), imaging sensors (130), ranging sensors, IMUs, linear displacement sensors and/or the like. A computing system receives the various inputs from the sensors and fuses the data to determine state information for the machine (100).