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 8):
A method for troubleshooting fault component in an equipment, comprising:\n step 1: acquiring data of a component in an abnormal state and data of fault maintenance of the equipment; step 2: building a fault-component-sensor Bayesian belief network model according to the data of the component in the abnormal state and data of fault maintenance of the equipment; wherein the fault-component-sensor Bayesian belief network model comprises a sensor, a component and a set fault for the equipment; in the fault-component-sensor Bayesian belief network model, the sensor is connected with the component; the component is connected with the set fault for the equipment; the connection between the sensor and the component involves whether the component detected the sensor is abnormal; and the connection between the component and the set fault for equipment involves that the set fault for equipment is induced by component abnormality; step 3: calculating a plurality of probabilities of actual component abnormality detected by each of a plurality of sensors connecting with each of a plurality of components based on the fault-component-sensor Bayesian belief network model; and step 4: ranking the plurality of probabilities of actual component abnormality detected by the plurality of sensors connecting with each of the plurality of the components in a descending order, and to obtain ranked probabilities that the plurality of components are actually abnormal; the plurality of the components are ranked correspondingly according to the plurality of the probabilities of actual component abnormality ranked; and a top-ranked component is to be troubleshot first.

Metadata:
- Claim Count in Document: 32.0
- Percentile: 96.0
- Lexical Diversity: 1.90789
- Patent Class: 702.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: True
- Related Applications: ['09758891', '14909788', '15823116', '13336153', '11868245']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.1972583109318546
- 35 USC 102 Novelty (BERT): 0.486306637063095
- Combined Prediction Score: 0.2261631435449786
- Mean Citation Score: 186.220998
- Max Citation Score: 203.27025
- Similarity Product: 141.19718303802608

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