1. A method for automated segregation of kernels, comprising: collecting sensor data in a time series format; labeling sensor data in a time series format as either normal, or one of one or more possible faults; segmenting sensor data in a time series format into blocks having a substantially uniform slope with respect to time; labeling the blocks as having a rising, falling, or flat slope; joining adjacent blocks with slopes having a same sign; identifying candidate kernels; convoluting the sensor data in a time series format with candidate kernels; applying a feature selection method to determine which kernels have discriminatory power; and training a fault classification system based on kernels having discriminatory power.