Fault detection system and method using multiway principal component analysis
First Claim
1. A fault detection system for detecting faults in a plurality of turbine engines, the fault detection system comprising:
- a multiway principal component analysis (MPCA) mechanism, the MPCA mechanism adapted to receive a batch of turbine sensor data from the plurality of turbine engines, the batch of turbine sensor data including sensor data from the plurality of turbine engines operating over a plurality of different operational phases, the MPCA mechanism including a plurality of load vectors, each of the plurality of load vectors representing a principal component in the turbine sensor data, the MPCA mechanism further adapted to project the batch of turbine sensor data onto the plurality of load vectors to generate a plurality of statistical measures; and
a classifier, the classifier adapted to receive the plurality of statistical measures and analyze the plurality of statistical measures to determine if a fault occurred in the plurality of turbine engines during the plurality of different operational phases, the classifier further generating an output indicating the fault responsive to the classifier determining that the fault occurred in the plurality of turbine engines during the plurality of different operational phases.
1 Assignment
0 Petitions
Accused Products
Abstract
A fault detection system and method is provided that facilitates detection of faults that are manifest over a plurality of different operational phases. The fault detection system and method use multiway principal component analysis (MPCA) to detect fault from turbine engine sensor data. Specifically, the fault detection system uses a plurality of load vectors, each of the plurality of load vectors representing a principal component in the turbine engine sensor data from the multiple operational phases. The load vectors are preferably developed using sets of historical sensor data. When developed using historical data covering multiple operational phases, the load vectors can be used to detect likely faults in turbine engines. Specifically, new sensor data from the multiple operational phases is projected on to the load vectors, generating a plurality of statistical measures that can be classified to determine if a fault is manifest in the new sensor data.
-
Citations
22 Claims
-
1. A fault detection system for detecting faults in a plurality of turbine engines, the fault detection system comprising:
-
a multiway principal component analysis (MPCA) mechanism, the MPCA mechanism adapted to receive a batch of turbine sensor data from the plurality of turbine engines, the batch of turbine sensor data including sensor data from the plurality of turbine engines operating over a plurality of different operational phases, the MPCA mechanism including a plurality of load vectors, each of the plurality of load vectors representing a principal component in the turbine sensor data, the MPCA mechanism further adapted to project the batch of turbine sensor data onto the plurality of load vectors to generate a plurality of statistical measures; and a classifier, the classifier adapted to receive the plurality of statistical measures and analyze the plurality of statistical measures to determine if a fault occurred in the plurality of turbine engines during the plurality of different operational phases, the classifier further generating an output indicating the fault responsive to the classifier determining that the fault occurred in the plurality of turbine engines during the plurality of different operational phases. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
-
-
12. A method of detecting faults in a turbine engine, the method comprising the steps of:
-
receiving a batch of turbine engine sensor data from a plurality of turbine engines, the batch of turbine engine sensor data including sensor data from the plurality of turbine engines operating over a plurality of different operational phases; projecting the batch of turbine engine sensor data onto a plurality of multiway principal component analysis (MPCA) load vectors to generate a plurality of statistical measures; classifying the plurality of statistical measures to determine if a fault occurred in the plurality of turbine engines during the plurality of different operational phases; and generating an output indicating the fault responsive to determining the fault occurred in the plurality of turbine engines during the plurality of different operational phases. - View Dependent Claims (13, 14, 15, 16, 17, 18, 19, 20, 21)
-
-
22. A program product comprising:
-
a) a fault detection program for detecting faults in a plurality of turbine engines, the fault detection program including; a multiway principal component analysis (MPCA) mechanism, the MPCA mechanism adapted to receive a batch of turbine sensor data from the plurality of turbine engines, the batch of turbine sensor data including sensor data from the plurality of turbine engines operating over a plurality of different operational phases, the MPCA mechanism including a plurality of load vectors, each of the plurality of load vectors representing a principal component in the turbine sensor data, the MPCA mechanism adapted to project the batch of turbine sensor data onto the plurality of load vectors to generate a plurality of statistical measures; and a classifier, the classifier adapted to receive the plurality of statistical measures and analyze the plurality of statistical measures to determine if a fault occurred in the plurality of turbine engines during the plurality of different operational phases, the classifier further generating an output indicating the fault responsive to the classifier determining that the fault occurred in the plurality of turbine engines during the plurality of different operational phases; and b) computer-readable signal-bearing recordable media bearing said fault detection program.
-
Specification