# US 11,030,064 B2 · Facilitating classification of equipment failure data > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,030,064 B2 - Document ID: US11030064B2 - Status: Granted - Kind: B2 - Application number: 15/617,860 - Filed: 2017-06-08 - Published: 2018-12-13 - Granted: 2021-06-08 - Earliest claimed: 2017-06-08 - Assignees: International Business Machines Corporation - Applicants: International Business Machines Corporation - Inventors: Jing Ding; Jing Li; Ji Jiang Song; Jian Wang - Web page: https://patentgrep.com/patent/US11030064B2 ## CPC - G06F 11/2257: {using expert systems} - G06F 11/00: Error detection; Error correction; Monitoring (error detection, correction or monitoring in information storage based on relative movement between record carrier and transducer G11B20/18; monitoring, i.e. supervising the progress of recording or reproducing G11B27/36; in static stores G11C29/00) - G06F 16/22: Indexing; Data structures therefor; Storage structures - G06F 16/2365: {Ensuring data consistency and integrity} - G06N 20/00: Machine learning - G06N 20/10: using kernel methods, e.g. support vector machines [SVM] - G06N 5/047: Pattern matching networks; Rete networks - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract The subject disclosure relates to employing grouping and selection components to facilitate a grouping of failure data associated with oil and gas exploration equipment into one or more equipment failure type groups. In an example, a method comprises grouping, by a system operatively coupled to a processor, training data of a set of equipment failure data into one or more failure type groups based on one or more determined failure criteria, wherein the one or more failure type groups represent equipment failure classifications associated with energy exploration processes; and selecting, by the system, first ungrouped data from the set of equipment failure data based on a level of similarity between the first ungrouped data and the training data. ## Claims ### Claim 1 A system, comprising: a memory that stores computer executable components; a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise: a first grouping component that groups a first equipment failure data of a set of equipment failure data into a first failure type group based on a determined failure criterion; an equipment assembly including one or more capacitors to increase one or more power factors, wherein the determined failure criterion comprises an average reactive power data identifying a fluid traversing from a first position to a second position; and a selection component that selects a second equipment failure data from the set of equipment failure data based on a level of similarity between the first equipment failure data and the second equipment failure data. ### Claim 2 (depends on 1) The system of claim 1, further comprising a feed component that transmits the first equipment failure data selected from the set of equipment failure data to the first grouping component based on the determined failure criterion that represents domain expert information. ### Claim 3 (depends on 1) The system of claim 1, further comprising a second grouping component that groups the second equipment failure data into the first failure type group based on the level of similarity. ### Claim 4 (depends on 1) The system of claim 1, further comprising an evaluation component that evaluates the level of similarity between the first equipment failure data and the second equipment failure data based on similarity criterion comprising equipment performance similarities or similarities in energy extraction performance. ### Claim 5 (depends on 1) The system of claim 1, further comprising an archiving component that stores equipment failure similarity level data comprising the first equipment failure data and the second equipment failure data at a first data store. ### Claim 6 (depends on 2) The system of claim 2, further comprising a review component that identifies miscategorized data within the first failure type group and the first data store based on a pattern matching criterion. ### Claim 7 (depends on 6) The system of claim 6, further comprising a removal component that removes the miscategorized data from the first failure type group and the first data store based on the pattern matching criterion. ### Claim 8 (depends on 2) The system of claim 2, further comprising a delivery component that transmits reviewed and grouped equipment failure data to the feed component. ### Claim 9 (depends on 5) The system of claim 5, further comprising a generation component that generates a classification framework from the first equipment failure data and the second equipment failure data to facilitate a generation of a classification data for storage at a fourth data store. ### Claim 10 (depends on 9) The system of claim 9, further comprising a classification component that classifies the first equipment failure data, the second equipment failure data, or third equipment failure data as classification data for storage at the fourth data store based on the classification framework, wherein the classification framework facilitates efficient access to the classification data from the memory or the fourth data store based on data access patterns associated with the classification framework. ### Claim 11 (depends on 9) The system of claim 9, wherein the classification framework is a k-nearest neighbor pattern recognition model. ### Claim 12 (depends on 7) The system of claim 7, wherein the removal component removes miscategorized first equipment failure data, miscategorized second equipment failure data, or miscategorized third equipment failure data are stored at a second data store. ### Claim 13 A computer program product for facilitating a grouping of energy exploration equipment data into one or more failure type group, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to: group a set of expert consensus data representing energy exploration equipment failures into one or more failure type groups, wherein a determined failure criterion comprises an average reactive power data identifying a fluid traversing from a first position to a second position, wherein one or more capacitors increase one or more power factors; and select a first set of uncategorized failure data within the one or more failure type groups based on a similarity level between the set of expert consensus data and the first set of uncategorized failure data. ### Claim 14 (depends on 13) The computer program product of claim 13, wherein the program instructions are further executable by the processor to cause the processor to: group the first set of uncategorized failure data within the one or more failure type groups based on the similarity level with the set of expert consensus data. ### Claim 15 (depends on 13) The computer program product of claim 13, wherein the program instructions are further executable by the processor to cause the processor to: classify a second set of uncategorized failure data within the one or more failure type group based on a k-nearest neighbor algorithm. ### Claim 16 (depends on 15) The computer program product of claim 15, wherein the program instructions are further executable by the processor to cause the processor to: store at an archive database a first set of categorized failure data categorized based on a level of similarity to the set of expert consensus data or a second set of categorized failure data categorized based on the k-nearest neighbor algorithm.