Kernel-based fault detection system and method

An apparatus comprising:

• a processor;
• a memory coupled to the processor;
• a fault detection program residing in the memory and being executed by the processor, the fault detection program including:
• a kernel-based Maximum Representation Discrimination Features (MRDF) feature extractor, the kernel-based MRDF feature extractor receiving measured turbine sensor data from a turbine engine, the kernel-based MRDF feature extractor using a nonlinear kernel function, the nonlinear kernel function comprising a nonlinear mapping of the measured turbine sensor data to a higher dimensional space, the kernel-based MRDF feature extractor using the nonlinear kernel function to extract features indicative of nonlinear correlations in the sensor data;
• and
• a classifier, the classifier receiving the extracted features and classifying the extracted features to determine if a fault occurred in the turbine engine,
• wherein the kernel-based MRDF feature extractor is developed using historical sensor data from known good and known bad turbine engines.

A program product comprising:

• a) a fault detection program, the fault detection program including:
• a kernel-based Maximum Representation Discrimination Features (MRDF) feature extractor, the kernel-based MRDF feature extractor receiving measured turbine sensor data from a turbine engine, the kernel-based MRDF feature extractor, the nonlinear kernel function comprising a nonlinear mapping of the measured turbine sensor data to a higher dimensional space, the kernel-based MRDF feature extractor using the nonlinear kernel function using a nonlinear kernel function to extract features indicative of nonlinear correlations in the sensor data;
• and
• a classifier, the classifier receiving the extracted features and classifying the extracted features to determine if a fault occurred in the turbine engine;
• and
• b) computer-readable signal bearing media bearing said program,
• wherein the kernel-based MRDF feature extractor is developed using historical sensor data from known good and known bad turbine engines.

A method of detecting faults in a turbine engine, the method comprising the steps of:

• receiving measured turbine sensor data from the turbine engine;
• extracting features indicative of nonlinear correlations in the sensor data from the sensor data using a kernel-based Maximum Representation Discrimination Features (MRDF) extraction technique using a nonlinear kernel function, the nonlinear kernel function comprising a nonlinear mapping of the measured turbine sensor data to a higher dimensional space;
• and
• classifying the extracted features to determine if a fault occurred in the turbine engine,
• wherein the kernel-based MRDF extraction technique is developed using historical sensor data from known good and known bad turbine engines.