Adaptive Equipment Anomaly Warning Using Stage-Specific Thresholds
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Solution Overview
Problem
Existing equipment anomaly detection technologies fail to accurately identify anomalies due to fixed thresholds that do not consider the equipment's operating condition and lifecycle stage, leading to false alarms or missed detections.
Innovation Solution
An equipment anomaly warning system that dynamically adjusts sensitivity based on stage-specific anomaly count thresholds, using a sampling module, evaluation module, and determination module to analyze sensor data and send appropriate warnings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fixed anomaly thresholds are used for equipment detection, then the detection system is simple to implement, but misjudgments and omissions increase due to inability to adapt to equipment lifecycle stages
Solution Approach 1:
The patent implements dynamic anomaly thresholds that automatically adjust based on equipment lifecycle stage. The system transitions from static fixed thresholds to dynamic adaptive thresholds by introducing a lifecycle stage recognition mechanism that modifies detection parameters in real-time according to equipment age and operational phase, thereby improving detection accuracy without requiring manual intervention.
Solution Approach 2:
The patent changes the detection parameter (anomaly threshold) based on equipment lifecycle stage. By introducing stage-specific thresholds that vary with equipment age and operational phase, the system adapts its sensitivity to match equipment behavior at different lifecycle stages, reducing both false positives and false negatives while maintaining automated operation.
2Reliability
If stage-specific anomaly count thresholds are implemented, then anomaly detection accuracy improves, but the system complexity increases
Solution Approach 1:
The patent segments the equipment lifecycle into distinct stages (e.g., early stage, mature stage, end-of-life stage) and assigns specific anomaly thresholds to each segment. This segmentation allows the system to apply appropriate detection sensitivity for each lifecycle phase, improving reliability by matching threshold strictness to equipment maturity while maintaining manageable system complexity through structured categorization.
Solution Approach 2:
The patent performs preliminary classification of equipment lifecycle stage before applying anomaly detection. By pre-determining the equipment's current stage and selecting appropriate thresholds in advance, the system avoids complex real-time calculations during anomaly evaluation, thereby improving reliability through stage-appropriate thresholds while keeping the overall system complexity acceptable.
3Measurement precision
If dynamic anomaly detection is performed based on equipment health status, then misjudgments are reduced, but computational requirements and system complexity increase
Solution Approach 1:
The patent applies different detection precision levels to different equipment lifecycle stages. Rather than using uniformly high computational precision across all stages, the system applies locally optimized detection strategies tailored to each stage's characteristics, achieving high detection precision where needed while reducing computational overhead in stages where simpler detection suffices.
Data Source
AI summary
Equipment anomaly warning systems and methods are disclosed and used for acquiring a set of sensing parameters of a piece of equipment, evaluating a stage-specific anomaly count threshold for the equipment based on the set of sensing parameters, and detecting a cumulative anomaly count based on the set of sensing parameters to send a warning signal based on a comparison between the cumulative anomaly count and the stage-specific anomaly count threshold. In this way, it can more accurately identify equipment anomalies needed to warn and reduce misjudgments or omissions of anomalies compared with technologies that use a fixed mechanism to detect anomalies.