Work Machine Failure Diagnostic System Using Snapshot Data
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Solution Overview
Problem
Identifying the true cause of failures in work machines is challenging even with acquired time-series data, as it requires complex analysis to determine deviations from normal ranges and associate them with component failures.
Innovation Solution
A failure diagnostic system for work machines that includes a detection unit to monitor physical quantities, a controller to acquire and analyze time-series data, and a storage unit to associate deviations with potential failure causes, allowing for the identification of component failures based on snapshot data from different periods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If time-series data is acquired and analyzed to identify failure causes, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the continuous time-series data into discrete snapshot data points at specific time intervals. This segmentation simplifies the analysis by breaking down complex continuous data into manageable discrete units that can be easily compared and analyzed for failure patterns.
Solution Approach 2:
The system performs preliminary actions by pre-acquiring snapshot data before failure occurs and storing it in advance. This preliminary data collection enables rapid failure analysis when needed, as the data infrastructure is already in place rather than requiring complex real-time analysis during failure events.
2Reliability
If complex analysis is performed on time-series data to determine failure causes, then reliability is improved, but loss of time increases
Solution Approach 1:
The patent extracts only the essential snapshot data points at predetermined time intervals from the continuous time-series data, removing unnecessary intermediate data. This extraction approach maintains reliability for failure identification while significantly reducing the time required for data analysis compared to processing complete continuous data sets.
Solution Approach 2:
The system creates simplified copies of the time-series data in the form of snapshot data that captures the essential information at key time points. These snapshot copies enable rapid analysis without requiring access to the full complex time-series data, thus reducing diagnosis time while maintaining identification accuracy.
3Measurement precision
If snapshot data from multiple periods is acquired and analyzed, then failure identification accuracy is improved, but device complexity increases
Solution Approach 1:
The patent merges snapshot data from multiple different time periods into a unified analysis framework. By combining these discrete data points from various periods, the system achieves comprehensive failure identification without requiring complex continuous monitoring systems, thus improving accuracy while maintaining relatively simple device architecture.
Data Source
AI summary
A failure of a component mounted on a work machine is easily identified. A controller acquires time-series data detected in a first period as first snapshot data, and acquires the time-series data detected in a second period after the first period as second snapshot data. A storage unit stores information in which a deviation of a physical quantity from a normal range in order to monitor an operation status of the component, and a failure of the component that is a cause of the deviation are associated with each other. The controller identifies the failure of the component based on the first snapshot data, the second snapshot data, and the information stored in the storage unit.