Facility Anomaly Analysis Using Multi-Parameter Causal Models
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Solution Overview
Problem
Existing methods for analyzing anomalies in electronic appliances, such as those in data centers or server farms, are inefficient and fail to quickly identify and rectify deviations from operational states, affecting reliability and availability.
Innovation Solution
A method and system using processor and memory circuitry to detect deviations by building models that link relevant parameters, allowing for the identification of mutable parameters whose changes can restore the system to an operational state, with periodic retraining of models based on collected data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional anomaly detection methods are used in facilities with electronic appliances, then the system can detect anomalies, but the analysis is inefficient and fails to quickly identify and rectify deviations from operational states
Solution Approach 1:
The system performs preliminary actions by continuously collecting and storing operational data from multiple parameters before anomalies occur. Models are pre-trained on this historical data to establish normal operational patterns, enabling rapid anomaly detection and root cause identification when deviations occur, thus improving efficiency and reducing response time.
Solution Approach 2:
The system transitions from traditional single-parameter anomaly detection to multi-dimensional analysis by collecting data from numerous parameters simultaneously and using machine learning models to analyze relationships across these dimensions. This holistic approach enables faster and more accurate identification of anomaly root causes by examining the system from multiple parameter perspectives concurrently.
2Measurement precision
If models link multiple parameters to identify root causes of anomalies, then the identification accuracy improves, but the system complexity increases
Solution Approach 1:
The system introduces machine learning models as intermediaries that automatically process and analyze relationships between multiple parameters. These models serve as mediators between raw parameter data and anomaly root cause identification, handling the complexity of multi-parameter relationships while providing accurate results without requiring manual analysis of complex parameter interactions.
Solution Approach 2:
The system implements self-service through automated model training and anomaly analysis. The models automatically learn from historical data, identify patterns, and pinpoint root causes of anomalies without human intervention. This automation handles the computational complexity of analyzing multiple parameters simultaneously, delivering precise root cause identification while reducing the operational burden on users.
Data Source
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AI summary
There is provided a system and method of analysing anomalies in one or more electronic appliances including at least one computer. The method comprises, by a processor and memory circuitry, upon detection of a deviation of a given parameter representative of the one or more electronic appliances from an operational state, providing a model associated with the given parameter, wherein the model links one or more other parameters to the given parameter, wherein the one or more other parameters affect the given parameter, and based at least on the model, identifying, among the one or more other parameters, at least one parameter Pj for which a change in its value allows bringing back the given parameter to the operational state.