Providing early warning and assessment of vehicle design problems with potential operational impact
First Claim
1. An unsupervised machine learning aircraft design method, comprising:
- receiving, by operation of at least one processor coupled to an electronic data repository and a communication network, a plurality of design problem data and in-service event data for an aircraft from the electronic data repository, wherein the plurality of design problem data and in-service event data includes heterogeneous natural language data, free text data, numeric data, and scalar and vector sensor data;
generating a high order vector for each of the plurality of design problem data and in-service event data, wherein a first high order vector is generated for a first design problem data and in-service event data of the plurality of design problem data and in-service event data, based on natural language text contained within the first design problem data and in-service event data;
concatenating each high order vector, including the first high order vector, into a high order vector matrix;
generating a reduced order symptom-normalized matrix by dimensionality reduction of the high order vector matrix;
generating a similarity matrix from the reduced order symptom-normalized matrix by computing a similarity metric between the reduced order symptom-normalized matrix and each of the plurality of problem data and in-service event data;
computing an impact score for each design problem represented in the similarity matrix as a function of a corresponding portion of the plurality of design problem data and in-service event data;
generating a priority matrix configured to identify design problems having high impact scores;
communicating an alert of the high impact scored design problems; and
prioritizing corresponding maintenance schedule and design updates for at least one aircraft during development, based on the generated similarity matrix.
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Abstract
Method and apparatus for unsupervised aircraft design. A plurality of design problem data and service event data for an aircraft is received from an electronic data repository. Embodiments communicate with sensors on the aircraft during flight operations and capturing service data and sensor data. A high order vector is generated for each received problem report and service event data and each high order vector is concatenated into a high order vector matrix. Embodiments generate a reduced order symptom-normalized matrix by factorization of the concatenated high order vector matrix and generate a similarity matrix from the symptom-normalized matrix. An impact score is computed for each in-service event data as a function of similar problem reports using the similarity matrix. Embodiments generate a priority matrix configured to identify service event data having high impact scores and communicate a real-time alert of the high impact scored service event.
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Citations
19 Claims
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1. An unsupervised machine learning aircraft design method, comprising:
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receiving, by operation of at least one processor coupled to an electronic data repository and a communication network, a plurality of design problem data and in-service event data for an aircraft from the electronic data repository, wherein the plurality of design problem data and in-service event data includes heterogeneous natural language data, free text data, numeric data, and scalar and vector sensor data; generating a high order vector for each of the plurality of design problem data and in-service event data, wherein a first high order vector is generated for a first design problem data and in-service event data of the plurality of design problem data and in-service event data, based on natural language text contained within the first design problem data and in-service event data; concatenating each high order vector, including the first high order vector, into a high order vector matrix; generating a reduced order symptom-normalized matrix by dimensionality reduction of the high order vector matrix; generating a similarity matrix from the reduced order symptom-normalized matrix by computing a similarity metric between the reduced order symptom-normalized matrix and each of the plurality of problem data and in-service event data; computing an impact score for each design problem represented in the similarity matrix as a function of a corresponding portion of the plurality of design problem data and in-service event data; generating a priority matrix configured to identify design problems having high impact scores; communicating an alert of the high impact scored design problems; and prioritizing corresponding maintenance schedule and design updates for at least one aircraft during development, based on the generated similarity matrix. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
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14. A system, comprising:
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one or more computer processors; and a memory containing computer program code that, when executed by operation of the one or more computer processors, performs an operation comprising; retrieving problem report data for at least a first class of vehicle, comprising; retrieving a plurality of user-submitted problem reports, each comprising a natural language description of a respective problem occurrence for a respective in-service vehicle of the first class of vehicle; retrieving a plurality of service events, each specifying a fault code; and retrieving a plurality of sensor events, each corresponding to a respective occurrence of a pattern of data being received from one or more sensor devices; calculating a vector representation for each of the plurality of user-submitted problem reports, the plurality of service events and the plurality of sensor events, wherein a first vector representation is generated for a first user-submitted problem report containing natural language text describing the respective problem occurrence for the respective in-service vehicle of the first class of vehicle; reducing a dimensionality of the vector representations; calculating similarity values between the vector representations; categorizing each of the vector representations into one of a plurality of problem categories, based on the similarity values; calculating a respective measure of impact for each of the plurality of problem categories; and prioritizing corresponding maintenance schedule and design updates for at least one aircraft during development, based on the calculated measures of impact. - View Dependent Claims (15, 16, 17, 18)
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19. A non-transitory computer-readable medium containing computer program code that, when executed by operation of one or more computer processors, performs an operation comprising:
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retrieving problem report data for at least a first class of vehicle, comprising; retrieving a plurality of user-submitted problem reports, each comprising a natural language description of a respective problem occurrence for a respective in-service vehicle of the first class of vehicle; retrieving a plurality of service events, each specifying a fault code; and retrieving a plurality of sensor events, each corresponding to a respective occurrence of a pattern of data being received from one or more sensor devices; calculating a vector representation for each of the plurality of user-submitted problem reports, the plurality of service events and the plurality of sensor events, wherein a first vector representation is generated for a first user-submitted problem report containing natural language text describing the respective problem occurrence for the respective in-service vehicle of the first class of vehicle; reducing a dimensionality of the vector representations; calculating similarity values between the vector representations; categorizing each of the vector representations into one of a plurality of problem categories, based on the similarity values; calculating a respective measure of impact for each of the plurality of problem categories; and prioritizing corresponding maintenance schedule and design updates for at least one aircraft during development, based on the calculated measures of impact.
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Specification