Lubricant Analysis Classification for Equipment Defect Diagnosis
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Solution Overview
Problem
Current equipment maintenance methods rely heavily on expert analysis of lubricant samples, which can be inconsistent and inefficient in detecting equipment defects and recommending corrective actions, leading to potential exacerbation of issues.
Innovation Solution
A system utilizing machine learning techniques, specifically gradient boosting algorithms, to automatically classify oil sample analysis results as 'good' or 'defective', identify defect types, and recommend corrective actions, improving accuracy and timeliness in equipment maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If expert analysis of oil samples is used to identify equipment defects, then domain expertise and industrial standard thresholds can be applied, but inconsistency and inefficiency in defect detection occur
Solution Approach 1:
The patent replaces the mechanical system of manual expert analysis with an automated machine learning classification system. The ML model processes oil analysis results automatically, eliminating human involvement in the actual classification task while preserving the decision-making logic through training on historical expert data. This substitution directly resolves the contradiction by providing both consistency (automated processing) and efficiency (faster turnaround).
Solution Approach 2:
The patent creates a digital copy of expert knowledge through the machine learning model. By training the classification system on historical oil analysis data and expert decisions, the system replicates expert judgment capabilities in a consistent, automated manner. This copying approach allows the system to maintain the reliability of expert analysis while achieving the productivity gains of automation.
2Reliability
If manual expert analysis is used to classify oil samples and recommend corrective actions, then domain expertise can be applied, but inconsistent operation of equipment results
Solution Approach 1:
The patent replaces manual expert classification with an automated machine learning system that processes oil analysis results consistently. The ML model applies the same learned criteria to every sample, eliminating the variability inherent in human judgment. This substitution ensures consistent operation recommendations while reducing the time required for analysis compared to manual expert review.
3Productivity
If automated machine learning classification is used to detect equipment defects, then accuracy and timeliness in defect identification improve, but system complexity increases
Solution Approach 1:
The patent segments the defect detection task into distinct classification stages handled by specialized ML models. The system divides the overall problem into manageable components (e.g., defect detection, defect classification, corrective action recommendation), with each segment handled by a dedicated model. This segmentation reduces the complexity of individual models while maintaining high overall productivity through coordinated processing.
Solution Approach 2:
The patent introduces a machine learning system as an intermediary between oil analysis results and equipment maintenance decisions. This intermediary layer processes the raw analysis data, applies learned patterns, and generates standardized recommendations. The intermediary manages the complexity by encapsulating the sophisticated classification logic within the ML models, presenting a simplified interface to users while achieving high productivity.
4Measurement precision
If multiple classification models are used to identify defect types and corrective actions, then diagnostic accuracy improves, but computational complexity increases
Solution Approach 1:
The patent segments the classification process into multiple specialized models, each handling a specific aspect of defect analysis. Rather than using one complex model to handle all classification tasks, the system divides the work into separate models for different defect types and corrective actions. This segmentation improves measurement precision for each specific classification task while managing computational complexity through modular architecture.
Solution Approach 2:
The patent applies classification models selectively based on the specific needs of each oil analysis case. Rather than always applying all available classification models to every sample, the system uses partial action by selecting only the necessary models for each situation. This approach maintains high diagnostic accuracy when needed while reducing computational resource consumption for routine cases.
Data Source
AI summary
A system for automatic detection of a defect in equipment may include a binary classification model trained to classify laboratory analysis results of an oil sample according to a gradient boosting algorithm, and to output a classification indicator of “good” or “defective” for the laboratory analysis results of the oil sample. The system may include a first multiclass classification model trained to classify the laboratory analysis results according to the gradient boosting algorithm if the laboratory analysis results are classified as “defective,” and to output a predicted defect type for the defect in equipment. The system may include a second multiclass classification model trained to classify the laboratory analysis results according to the gradient boosting algorithm and the predicted defect type, and to output a predicted corrective action pertaining to the equipment based on the predicted defect type for the defect in equipment.