Oil Property Diagnosis Using Sensor Trends for Cause Identification
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Solution Overview
Problem
Existing oil diagnosis systems require oil analysis to accurately identify the cause of abnormalities, leading to delayed maintenance and increased downtime in work machines.
Innovation Solution
An oil diagnosis system that uses a controller to analyze sensor data from oil properties like viscosity, density, and dielectric constant, determining abnormality and identifying the cause without the need for oil analysis by employing amount-of-change determination values and index values to correlate temporal changes in oil properties with known abnormalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If oil analysis is performed to accurately identify the cause of abnormality, then measurement precision is improved, but loss of time increases due to delayed maintenance
Solution Approach 1:
The system performs preliminary analysis by calculating amount-of-change indexes from sensor data trends before oil analysis is conducted. This preliminary assessment identifies likely causes of abnormality, enabling maintenance personnel to prepare appropriate countermeasures in advance and reducing the time needed after oil analysis results are obtained.
Solution Approach 2:
The system introduces an intermediary diagnostic mechanism that uses sensor data and amount-of-change indexes to bridge the gap between abnormality detection and definitive cause identification through oil analysis. This intermediary provides timely diagnostic information without requiring immediate oil extraction, thus reducing maintenance delay while maintaining reasonable accuracy.
2Reliability
If oil analysis is required to identify the cause of abnormality, then reliability of diagnosis is improved, but productivity decreases due to machine downtime
Solution Approach 1:
The system performs preliminary diagnostic assessment using sensor data and amount-of-change indexes before oil analysis is conducted. This preliminary diagnosis provides sufficient information for maintenance personnel to implement appropriate countermeasures, reducing the need for extended machine downtime while maintaining reliable diagnosis through the combination of sensor-based assessment and targeted oil analysis.
3Measurement precision
If multiple sensor data parameters are monitored to identify oil abnormality causes, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system segments the diagnostic process into distinct functional modules: sensor data acquisition, amount-of-change index calculation, abnormality determination, and cause identification. Each module processes specific parameters independently, which improves measurement precision through comprehensive monitoring while managing device complexity through modular architecture that allows independent development and maintenance of each segment.
Data Source
AI summary
A computer for a manufacturer includes: a storage device storing amount-of-change determination values specified for respective amount-of-change indexes indicative of tendencies of temporal changes in pieces of sensor data A, B, and C, about a plurality of oil properties including a viscosity, a density, and a dielectric constant of oil; an abnormality determining section determining abnormality of the oil on the basis of the pieces of sensor data A, B, and C about the plurality of oil properties and abnormality determination values SAh, SAl, SBh, and SCh prescribed for the respective pieces of sensor data A, B, and C about the plurality of oil properties; a cause identifying section identifying, when the abnormality determining section determines the oil to be abnormal, the cause of the abnormality on the basis of the type of the oil property determined to be abnormal and the amount-of-change determination value of the oil property.