Cyclical Behavior Detection Using Fourier Series Analysis
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Solution Overview
Problem
Existing methods for monitoring cyclical behaviors in large datasets are inefficient and prone to inaccuracies, as they rely on complex calculations and human interpretation, making them cumbersome and error-prone.
Innovation Solution
Defining a time-based set of splines in an equation for a dataset to identify periodicity and take responsive actions based on deviations from the cycle, using a computer processor and application that implements these methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If boundary alerting with pre-defined static values is used to monitor cyclical behaviors, then alert rules can be established for each metric, but the method becomes impractical with large amounts of data requiring very complex calculations and introducing inaccuracies
Solution Approach 1:
The patent transforms static boundary values into dynamic parameters by using Fourier series to model cyclical patterns. The system changes from fixed thresholds to time-varying parameters that adapt to the actual cyclical behavior, improving detection accuracy while maintaining computational feasibility through parameter transformation
Solution Approach 2:
The patent replaces complex manual calculation methods with automated Fourier analysis algorithms. By substituting mechanical/mathematical complexity with computational methods, the system achieves accurate cyclical detection without requiring very complex manual calculations, resolving the contradiction between precision and complexity
2Ease of operation
If graphic display with human interpretation is used to analyze large amounts of data, then visual inspection can identify patterns, but the approach becomes cumbersome and error prone
Solution Approach 1:
The patent implements self-service by enabling the system to automatically detect and analyze cyclical patterns without human intervention. The Fourier-based algorithm autonomously identifies periodic behaviors, eliminates the need for cumbersome manual graphic analysis, and provides reliable results free from human error, simultaneously improving ease of operation and reliability
3Productivity
If existing boundary alerting techniques are applied to cyclical data, then simple threshold monitoring can be implemented, but the method cannot effectively capture periodic patterns and trends
Solution Approach 1:
The patent applies periodic action by using Fourier series to explicitly model and detect periodic patterns in the data. Instead of simple continuous threshold monitoring, the system uses periodic functions that match the natural cyclical behavior, improving both monitoring efficiency and preserving cyclical pattern information that would be lost with static thresholds
Solution Approach 2:
The patent achieves universality by creating a monitoring system that can handle multiple types of cyclical patterns simultaneously through Fourier analysis. The same mathematical framework works for various frequencies and amplitudes, maintaining high productivity while capturing diverse cyclical information that simple boundary alerting would lose
Data Source
AI summary
Methods, systems, and computer program products for identifying cyclical behaviors are provided. A method includes defining a time-based set of splines in an equation for a dataset, identifying a periodicity of a cycle derived from implementing the time-based set of splines on the dataset, and taking a responsive action as a result of identifying the periodicity of the cycle.