Optical Module Fault Prediction Using Classification Thresholds
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing optical module fault prediction methods are inadequate in accurately predicting faults before they occur, leading to delayed maintenance and service disruptions in large data centers.
Innovation Solution
A method that determines a classification threshold based on a classification sample set for operating parameters like bias current and receive power, using a fault prediction model to predict the likelihood and urgency of faults, allowing for timely maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional fault monitoring methods are used to detect optical module faults, then faults can be detected after they occur, but the prediction accuracy is insufficient and maintenance cannot be performed proactively
Solution Approach 1:
The patent applies preliminary action by establishing classification thresholds and fault prediction models before faults occur. The system proactively analyzes operating parameters (bias current, receive power) against pre-defined thresholds and trained models to predict potential faults before they manifest, enabling preventive maintenance rather than reactive response. This is evident in the steps of determining classification thresholds based on historical data and using these thresholds to predict faults before service disruption occurs.
2Loss of time
If classification thresholds are established based on classification sample sets to predict faults, then early fault identification is enabled, but the system complexity increases due to threshold determination and model training requirements
Solution Approach 1:
The system applies self-service by automatically determining classification thresholds and training fault prediction models using historical operating data without requiring manual expert intervention. The system autonomously collects bias current and receive power data, processes this data to establish thresholds, and continuously refines prediction models. This automation reduces the complexity burden on operators while maintaining high prediction accuracy, enabling the system to serve itself in the fault prediction process.
3Measurement precision
If multiple operating parameters are monitored continuously to improve prediction accuracy, then fault prediction capability is enhanced, but the energy consumption and measurement resources increase
Solution Approach 1:
The patent applies the extraction principle by selectively focusing monitoring resources on the two most critical operating parameters: bias current and receive power. Rather than continuously monitoring all possible optical module parameters, the system extracts and analyzes only these key parameters that have the strongest correlation with fault occurrence. This selective approach maintains high prediction accuracy while minimizing energy consumption and measurement resource requirements by concentrating on the most informative parameters.
Data Source
Figure 1
Figure 2
Figure 3~4
AI summary
This application provides a method and an apparatus for predicting a fault of an optical module. In this application, a classification threshold of an operating parameter is determined based on a classification sample set corresponding to the operating parameter of optical modules; and whether a fault occurs in the future on an optical module corresponding to a sequence to be detected is predicted based on comparison results between the classification threshold and a plurality of measured values in the sequence to be detected, to help a maintenance engineer focus on an optical module on which a fault may occur, and reduce impact caused by the fault of the optical module on a service.