Monitoring and control of refrigeration equipment
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Solution Overview
Problem
Refrigeration equipment generates a high volume of alerts, leading to bandwidth and resource wastage and potentially ignoring critical alerts, resulting in serious consequences due to operator fatigue from managing excessive notifications.
Innovation Solution
A system that monitors alert rates from refrigeration equipment, adjusts alert thresholds using machine learning models, and tracks modifications to determine equipment faultiness, reducing unnecessary alerts and focusing on critical issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the alert threshold value is set low to detect all potential issues, then the detection coverage is improved, but the alert generation rate becomes excessively high causing operator fatigue and resource wastage
Solution Approach 1:
The alert threshold is transformed from a static value to a dynamic value that adapts based on equipment history and operational context. The system adjusts thresholds automatically, making them flexible rather than fixed, thereby optimizing the balance between detection sensitivity and alert volume without requiring manual intervention.
Solution Approach 2:
The system changes the parameter of alert threshold values based on equipment-specific patterns and historical data. By modifying threshold parameters dynamically for different equipment instances, the system achieves high detection coverage while maintaining manageable alert rates tailored to each equipment's behavior.
2Loss of energy
If the alert threshold value is set high to reduce alert volume, then the resource consumption is reduced, but the detection precision decreases causing critical alerts to be missed
Solution Approach 1:
Instead of applying a uniform high threshold across all equipment, the system applies locally optimized thresholds tailored to each equipment instance's characteristics and historical behavior. This localized approach maintains high detection precision for each specific equipment while collectively reducing overall alert volume and resource consumption.
Solution Approach 2:
The system dynamically adjusts threshold parameters based on equipment-specific patterns, transforming fixed high thresholds into adaptive values that maintain detection precision while optimizing resource usage for each individual equipment instance.
3Reliability
If the system monitors all equipment with fixed thresholds, then the monitoring coverage is comprehensive, but the system complexity increases due to managing high volumes of alerts
Solution Approach 1:
The system performs self-adjustment of alert thresholds without requiring complex external management. Each equipment instance's threshold adapts automatically based on its own historical data and patterns, enabling comprehensive monitoring while reducing the complexity of centralized alert management through autonomous equipment-level optimization.
Solution Approach 2:
The monitoring system transitions from static fixed thresholds to dynamic adaptive thresholds that automatically adjust to equipment behavior. This dynamic approach maintains comprehensive monitoring coverage while simplifying system complexity by eliminating the need for manual threshold management of high-volume alerts.
Data Source
AI summary
A system generates alerts based on sensor data obtained from equipment, for example, refrigeration equipment. Examples of equipment instances include refrigeration equipment, heating equipment, air conditioning equipment, and so on. The system accesses a model, for example, a machine learning model trained to predict an alert threshold for generation of alerts. The system modifies the alert threshold value for an equipment instance based on the output of the machine learning model. The system uses the modified alert threshold value for generating alerts. The system may track the number of times the alert threshold was adjusted. If the number of times the alert threshold value is modified exceeds a predetermined fault threshold value, the system determines that the equipment is faulty.