PATENT CLAIM ANALYSIS

Application Number: 16053994
Application Type: Utility
Filing Date: 2018-08
Publication Date: 2019-02
Patent Classification: ["702", "183000"]

Abstract:
Disclosed is an apparatus for troubleshooting fault component in equipment and a method thereof. The device includes portions for building a fault-component-sensor Bayesian belief network model by acquiring component state-abnormal and data of fault maintenance of the equipment, and for calculating probabilities of actual component abnormality when a sensor connected with an component detects component abnormality based on the model; and arranging the probabilities in a descending order to obtain the arranged probabilities of actual component abnormality, and the top-arranged component being the one to be troubleshot first. Namely, a relational expression among the fault, the component and the sensor may be systematically built by adopting the method or the apparatus provided by the present disclosure, and the most-likely-failing component in an equipment may be quickly detected according to the expression, thereby improving troubleshooting efficiency.

Claim (Index 13):
The method of  claim 12 , wherein the step of updating the fault-component-sensor Bayesian belief network model according to the first judgment result further comprises:\n judging whether the probability that a component is detected abnormal by each of the plurality of sensors and the component is actually abnormal is no smaller than a second threshold value, obtaining a second judgment result; reserving the sensor if the second judgment result shows that the probability that a component is detected abnormal by each of the plurality of sensors connecting to the component and the component is actually abnormal is no smaller than the second threshold value; judging whether the probability that a component is detected abnormal by each of the plurality of sensors connecting to the component and the component is actually abnormal is no smaller than a third threshold value, if the second judgment result shows the probability is less than the second threshold value when the component is detected abnormal, and obtaining a third judgment result; wherein the third threshold value is represented by a setting number of times of the probability when the plurality of sensors connected with the component except for the sensor detected that the component is abnormal and the component is actually abnormal; reserving the sensor if the third judgment result shows that the probability that the component is actually abnormal when the sensor connected with the component detects that the component is in the abnormal state is no smaller than the third threshold value; removing the sensor if the third judgment result shows that the actual probability of component abnormality is less than the third threshold value when the component is detected abnormal by the sensor; and updating the fault-component-sensor Bayesian belief network model according to the first judgment result, the second judgment result and the third judgment result.

Metadata:
- Claim Count in Document: 32.0
- Percentile: 96.0
- Lexical Diversity: 1.90789
- Patent Class: 702.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: True
- Related Applications: ['09758891', '14909788', '15823116', '13336153', '11868245']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2140594195026918
- 35 USC 102 Novelty (BERT): 0.504620851499861
- Combined Prediction Score: 0.2431155627024087
- Mean Citation Score: 186.220998
- Max Citation Score: 203.27025
- Similarity Product: 116.34276255257429

Labels:
- Claim Label 101: 1
- Claim Label 102: 1
- Claim Label 103: 1
- Claim Label 112: 1
- Combined Label: 1
- Label 101 Adjusted: 1

Dataset: test