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 2):
The apparatus of  claim 1 , wherein the building portion further comprises:\n a constructing unit, configured to construct a table of sensor abnormality-component based on the data of the component in the abnormal state; wherein the table of sensor abnormality-component comprises a probability that a component is detected abnormal by the plurality of sensors and the component is actually abnormal; the data of the component in the abnormal state comprises a number of times that a component is detected abnormal by each of the plurality of sensors and the component is actually abnormal, a number of times that a component is detected normal by each of the plurality of sensors but the component is actually abnormal, and a number of times a component is detected normal by each of the plurality of sensors but the component is actually abnormal; wherein the probability that a component is detected abnormal by each of the plurality of sensors and the component is actually abnormal is represented by a ratio of a number of times a component is detected abnormal by each of the plurality of sensors and the component is actually abnormal, to a number of times of component abnormality; wherein the number of times of component abnormality is represented by a sum of the number of times that the component is detected abnormal by each of the plurality of sensors and the component is actually abnormal, the number of times that the component is detected normal by each of the plurality of sensors but the component is actually abnormal, and the number of times that the component is detected normal by each of the plurality of sensors but the component is actually abnormal; a generating unit, configured to generate a fault dictionary based on the data of fault maintenance of the equipment; wherein the fault dictionary comprises a first probability of each of the plurality of components; the first probability is a probability of the set fault of the equipment when each of the plurality of components is abnormal; the data of fault maintenance of the equipment comprises a number of times of the set fault of the equipment when each of the plurality of the components is abnormal; wherein the first probability is further represented by a ratio of the number of times of set fault of the equipment when each of the plurality of the components is abnormal to the sum of the number of times of the set fault of the equipment when each of the plurality of the components is abnormal; and a building unit, configured to build a fault-component-sensor Bayesian belief network model with a Bayesian belief network based on the fault dictionary and the table of sensor abnormality-component.

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): 0.2143370552092008
- 35 USC 102 Novelty (BERT): 0.5029685045846513
- Combined Prediction Score: 0.2432002001467458
- Mean Citation Score: 186.220998
- Max Citation Score: 203.27025
- Similarity Product: 118.90647663362324

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