Fault Detection via Data Distribution Analysis
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Solution Overview
Problem
Complex systems in various industries face challenges in detecting faults due to unreliable components, harsh environments, and noise in sensor readings, leading to delayed or missed fault detection, which can result in system downtime and failures.
Innovation Solution
A fault detection system that analyzes data distribution characteristics, classifying residual Delta signals based on standard deviation bands to identify subtle changes in sensor signals, allowing for early detection of faults without the need for signal filtering, thereby reducing noise interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If signal filtering is applied to reduce noise in sensor readings, then noise interference is reduced, but fault detection timing is delayed and subtle fault signals are lost
Solution Approach 1:
The patent extracts only the necessary information from raw sensor signals by analyzing data distribution characteristics directly, without applying signal filtering. This extraction approach removes noise interference while preserving early fault signals by focusing on statistical properties rather than raw signal values.
Solution Approach 2:
The patent transforms the analysis from raw signal domain to statistical parameter domain by computing data distribution characteristics. This parameter transformation allows fault detection based on statistical changes in data distribution rather than absolute signal values, enabling early fault detection without noise filtering.
2Device complexity
If traditional fault detection methods are used, then simple implementation is maintained, but fault detection accuracy is reduced and subtle changes are missed
Solution Approach 1:
The patent adds a new dimension to fault detection by analyzing data distribution characteristics across multiple standard deviation bands. This dimensional transformation from single-value analysis to distribution-based analysis significantly improves fault detection accuracy while maintaining computational efficiency through standardized statistical measures.
3Measurement precision
If data distribution analysis is applied to detect subtle fault signals, then fault detection accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent segments the data distribution analysis into discrete standard deviation bands, allowing fault detection through counting and comparing data points in each band. This segmentation approach maintains computational simplicity by using basic statistical operations rather than complex algorithms, achieving high accuracy with low computational overhead.
Data Source
AI summary
Certain embodiments may include a method, system, apparatus, and/or machine accessible storage medium to: obtain baseline data associated with a device, wherein the baseline data comprises an indication of an expected performance of the device during healthy operation; obtain status data associated with the device, wherein the status data is obtained based on operational information monitored by a sensor; compute delta data based on a delta between the status data and the baseline data; compute a standard deviation of the delta data; compute a plurality of standard deviation bands based on the standard deviation of the delta data; compute a statistical distribution of the delta data based on the plurality of standard deviation bands; and detect a fault in the device based on the statistical distribution of the delta data.