Electronic Component Failure Prediction Using Time-Windowed Data Clustering
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Solution Overview
Problem
Predicting the failure of electrical or electro-mechanical components within larger systems is challenging, leading to significant costs and downtime due to unexpected failures during operation.
Innovation Solution
A system and method for analyzing data streams from components, using extraction protocols to identify new data segments, transforming them into clustering signature vectors, and comparing them to reference vectors to detect potential failures by identifying predictor clusters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If continuous monitoring of component output is performed to detect anomalies, then failure prediction capability is improved, but system complexity and computational resources required increase
Solution Approach 1:
The patent divides the continuous data stream into discrete time windows (e.g., 1-second intervals) and extracts features from each segment independently. This segmentation allows the system to process data in manageable chunks, reducing computational complexity while maintaining continuous monitoring capability. The segmentation principle is applied when creating time-windowed feature vectors for analysis.
Solution Approach 2:
The patent extracts specific features from the raw component output data, such as mean value, standard deviation, skewness, and kurtosis statistics. By taking out only the most relevant statistical characteristics rather than processing the entire raw data stream, the system reduces computational requirements while preserving the essential information needed for failure detection.
2Loss of time
If real-time data processing is implemented to detect anomalies immediately, then response time to failures is improved, but computational resources and processing time increase
Solution Approach 1:
The patent applies partial action by processing only the necessary statistical features (mean, standard deviation, skewness, kurtosis) from each time window rather than performing complete data analysis on every data point. This selective processing achieves real-time anomaly detection while minimizing computational resource consumption.
Solution Approach 2:
The system performs preliminary feature extraction and statistical computation on each time window before feeding the data to the anomaly detection algorithm. This preliminary processing organizes the data in advance, enabling faster real-time decision-making when anomalies are detected without requiring extensive computation at the moment of detection.
3Measurement precision
If comprehensive feature extraction is performed to improve detection accuracy, then anomaly detection precision is improved, but data processing time and complexity increase
Solution Approach 1:
The patent extracts only the most informative statistical features (mean, standard deviation, skewness, kurtosis) from the time-windowed data. By selecting and processing only these key features rather than all possible characteristics, the system achieves high detection precision while maintaining efficient processing throughput.
Solution Approach 2:
The patent transforms the raw time-series data into statistical parameters (mean, standard deviation, skewness, kurtosis) that capture the essential characteristics of the signal. This parameter transformation consolidates multiple data points into a few meaningful statistics, improving detection precision without proportionally increasing processing time.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables real-time prediction of component failures, reducing downtime and maintenance costs by detecting anomalies and allowing proactive replacement.
Implementation Method 1
The controller transforms the EDS to a clustering signature vector (CSV)... The controller transforms the CSV corresponding to the EDS... The controller transforms the EDS by applying a Fourier Transform to the EDS
Data Source
AI summary
A method is provided for finding events that predict failure of an electronic component of a data processing system. The method includes extracting EDSs from a data stream including an output of the electronic component as a function of time, each EDS satisfying an extraction protocol and being characterized by a time stamp indicating a time at which the EDS was present; transforming the EDSs to corresponding CSVs; clustering the CSVs into clusters, each cluster being characterized by a number of CSVs in the cluster and a range of time stamps for the CSVs in the cluster, where the clustering is determined by a similarity protocol; and identifying a predictor cluster from the clusters having CSVs with time stamps during a period that is nearer a point of failure of the electronic component than other clusters having CSVs, where the predictor cluster has more than a predetermined number of CSVs.