Histogram Alarm Bucket Tuning for Accurate Production Monitoring
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Solution Overview
Problem
Histogram-based data classification is sensitive to the number of intervals or buckets used, which can lead to incorrect data classification and false alarms due to either low or high granularity, impacting the accuracy of monitoring and remediation in production systems.
Innovation Solution
A system and method for optimizing the number of buckets in histogram-based alarms by determining an optimal number based on maximum and empty buckets trends, using a data evaluation agent that processes sampled datasets to generate regression functions and identify the ideal number of buckets for accurate data distribution analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of buckets is increased to improve data classification granularity, then measurement precision is improved, but device complexity increases and false alarms increase due to empty buckets
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the number of buckets in the histogram based on the characteristics of the incoming data stream. Instead of using a fixed number of buckets, the system modifies this parameter adaptively to optimize both classification precision and avoid empty bucket issues, thereby resolving the contradiction between measurement precision and device complexity.
2Measurement precision
If the number of buckets is increased to improve data distribution resolution, then measurement precision is improved, but reliability deteriorates due to increased false alarms from empty buckets
Solution Approach 1:
The system employs feedback mechanisms by continuously monitoring the histogram data distribution and using this information to adjust the number of buckets. The feedback loop ensures that the bucket configuration adapts to maintain reliable alarm generation by preventing empty buckets while preserving adequate resolution for accurate data distribution analysis.
3Device complexity
If the number of buckets is decreased to simplify histogram configuration, then device complexity is reduced, but measurement precision deteriorates due to low granularity
Solution Approach 1:
The patent implements dynamics by transitioning from a static, fixed number of buckets to a dynamic configuration that adapts based on data characteristics. This allows the system to maintain simplicity in basic configuration while achieving high measurement precision through automatic adaptation, effectively resolving the contradiction between device complexity and measurement precision.
4Reliability
If the number of buckets is decreased to reduce empty buckets, then reliability is improved, but measurement precision deteriorates due to insufficient granularity
Solution Approach 1:
The system resolves this contradiction through parameter changes by dynamically adjusting the number of buckets based on real-time data distribution analysis. This adaptive approach ensures that the bucket configuration maintains sufficient granularity for precise measurement while simultaneously minimizing empty buckets to preserve reliability, achieving both goals simultaneously.
Data Source
AI summary
A method and system for implementing histogram-based alarms in a production system. Specifically, the method and system disclosed herein entail generating histograms overlaid with frequency (i.e., number of data samples) based class policies to serve as data classifiers for measurements, metrics, or information produced by physical and/or logical sensors. The accurateness of histograms to represent distributions of data, however, may depend on certain constraints—one of which may be the number of intervals or buckets employed. Therefore, disclosed herein is also a methodology for identifying an optimal number of buckets, for particular sensor specific datasets, based on a maximum samples trend and an empty buckets trend associated with the datasets.