Method, apparatus and device for predicting fault of optical module

Patent metadata

Field	Value
Publication number	US-11265081-B2
Application number	US-202117166601-A
Country	US
Kind code	B2
Filing date	Feb 3, 2021
Priority date	Jan 30, 2019
Publication date	Mar 1, 2022
Grant date	Mar 1, 2022

Abstract

A method and an apparatus for predicting a fault of an optical circuit includes determining a classification threshold of an operating parameter based on a classification sample set corresponding to the operating parameter of optical circuit and predicting, based on comparison results between the classification threshold and a plurality of measured values in a sequence, whether a fault occurs in the future on the optical circuit corresponding to the sequence.

First claim

What is claimed is:

1. A method for predicting a fault of an optical module and comprising:
obtaining a first sequence of the optical module, wherein the first sequence comprises a plurality of measured values of an operating parameter of the optical module;
obtaining a classification threshold corresponding to the operating parameter;
comparing the measured values and the classification threshold to produce comparison results; and
determining a first prediction result of the optical module based on the comparison results,
wherein the first prediction result indicates whether the optical module has entered a faulty mode,
wherein when the first prediction result indicates that the optical module has entered the faulty mode, the method further comprises:
generating a first feature set based on the first sequence; and
inputting the first feature set into a fault prediction model to obtain a second prediction result, and
wherein the second rediction result indicates an urgency level at which the fault is expected to occur on the optical module.

2. The method of claim 1 , further comprising:
obtaining a sample sequence corresponding to the operating parameter;
determining a tag of the sample sequence based on the classification threshold and the sample sequence, wherein either the tag indicates whether the optical module corresponding to the sample sequence is a non-faulty optical module, or the tag indicates the urgency level at which the fault occurs on the optical module corresponding to the sample sequence;
generating a fault prediction sample corresponding to the sample sequence, wherein the fault prediction sample comprises a second feature set of the sample sequence and the tag of the sample sequence; and
generating the fault prediction model based on the fault prediction sample.

3. The method of claim 2 , further comprising updating the fault prediction model based on the first feature set and the second prediction result.

4. The method of claim 1 , wherein the classification threshold is based on a classification sample set corresponding to the operating parameter, wherein the classification sample set comprises a plurality of classification samples, wherein each of the classification samples comprises one measured value of the operating parameter and one first classification identifier; and wherein before obtaining the classification threshold corresponding to the operating parameter, the method further comprises determining, based on the classification sample set, the classification threshold corresponding to the operating parameter.

5. The method of claim 4 , further comprising:
determining a plurality of loss values based on the classification sample set and a plurality of reference classification thresholds, wherein each loss value corresponds to one reference classification threshold, and wherein the reference classification thresholds are either preset or based on the measured value of each of the classification samples;
determining a reference classification threshold corresponding to a smallest loss value in the loss values as a classification threshold corresponding to the classification sample set; and
determining, based on the classification threshold corresponding to the classification sample set, the classification threshold corresponding to the operating parameter.

6. The method of claim 5 , further comprising:
reclassifying the classification samples in the classification sample set based on each of the reference classification thresholds;
determining a second classification identifier of each classification sample in the classification sample set based on a classification result; and
determining, based on a loss function and the first classification identifier and the second classification identifier of each classification sample, a loss value corresponding to the reference classification threshold, wherein the loss function comprises:

loss
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,

wherein loss represents a mean square error of all the first classification identifiers in the classification sample set and the second classification identifiers, wherein N is a quantity of classification samples in the classification sample set, wherein N is greater than or equal to two, wherein O q is a first classification identifier of a classification sample whose index value is q in the N classification samples, and wherein P q is a second classification identifier of the classification sample whose index value is q.

7. The method of claim 5 , wherein the classification sample set comprises R positive samples and S negative samples, and wherein the method further comprises:
reclassifying the classification samples in the classification sample set based on each of the plurality of reference classification thresholds;
determining a second classification identifier of each classification sample in the classification sample set based on a classification result; and
determining, based on a loss function and the first classification identifier and the second classification identifier of each classification sample, a loss value corresponding to the reference classification threshold, wherein the loss function is calculated according to the equations:

loss
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l
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Assignees

• Huawei Tech Co Ltd

Inventors

• Yu Fangfang
• Dong Feng
• Song Wei
• Yang Qingping

Classifications

• G06F18/2415 Primary

  based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate · CPC title

• H04L43/16 Primary

  Threshold monitoring · CPC title

• H04L41/16

  using machine learning or artificial intelligence · CPC title

• H04B10/0791 Primary

  Fault location on the transmission path · CPC title

• H04B10/25

  Arrangements specific to fibre transmission · CPC title