DSL Fault Detection via Near-Far End Error Regression
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Solution Overview
Problem
Existing methods for identifying faults in digital subscriber lines, particularly intermittent faults like unstable joints, are inefficient as they often require specialist equipment, disrupt services, and struggle with intermittent issues that may not exhibit fault characteristics during testing.
Innovation Solution
A method that determines near-end and far-end error instances over time, performs regression analysis to find correlation between these errors, and uses quality of fit parameters to identify faults without disrupting services, using data from DSLAM and customer modem without the need for specialist equipment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If metallic line tests are used to identify faults, then fault detection capability is provided, but service disruption occurs and specialist equipment is required
Solution Approach 1:
The system uses self-generated error instances from the DSL line itself to perform fault detection. The near-end and far-end error instances are automatically collected and analyzed by the regression analysis module without requiring external test equipment or service disruption, enabling the line to essentially test itself continuously
Solution Approach 2:
The patent replaces the mechanical/electrical metallic line tests with a statistical analysis system. Instead of using physical test equipment that connects to the line and requires manual intervention, the system uses software-based regression analysis to process error data and identify faults, substituting mechanical testing with computational analysis
2Reliability
If metallic line tests are used to identify faults, then fault detection is provided, but testing equipment complexity and relay requirements increase
Solution Approach 1:
The system extracts fault detection functionality from complex metallic line test equipment and relay systems. By using error instances already present in the DSL communication data and processing them through regression analysis, the patent removes the need for specialized test equipment, relays, and manual testing procedures
Solution Approach 2:
The regression analysis module serves multiple functions: it processes near-end error instances, processes far-end error instances, performs correlation analysis, identifies faults, and provides continuous monitoring. This multi-functional approach replaces the need for multiple separate test devices and procedures
3Reliability
If metallic line tests are used, then fault identification is provided, but sensitivity to intermittent faults is insufficient
Solution Approach 1:
The system continuously collects near-end and far-end error instances over time without interruption. This continuous monitoring enables the regression analysis to detect intermittent faults that may not be present during single-point testing, as the system analyzes error patterns across multiple time points to identify statistical correlations indicative of unstable joints
Solution Approach 2:
The system uses feedback from the correlation analysis of error instances to identify faults. By continuously analyzing the relationship between near-end and far-end errors and comparing correlation coefficients against thresholds, the system provides feedback that enables detection of intermittent faults based on statistical patterns rather than single-point measurements
Data Source
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AI summary
The invention presents a method of identifying faults on a DSL line, typically intermittent faults arising from unstable joints in the DSL line. The method collects errored seconds data at the DSLAM and at the customer's premises equipment (CPE, typically a home hub or router). The error data collected at the DSLAM are termed near-end errors, and the error data collected at the CPE are termed far-end errors. The near-end and far-end data is then analysed by applyingt regression analysis to determine if there is a correlation or match between the two sets of data. Matching data patterns are indicative of unstable or bad joints in the DSL line, and are typically intermittent and located near the customer's premises.