1. A method of machine condition monitoring, comprising: receiving, by a computer, historic operating data including data from O signals over time; extracting I patterns x from data from individual signals in the operating data; clustering the I patterns into K pattern clusters c k based on similarities; clustering the O signals into R signal clusters based on correlations among the O signals; receiving an annotated training data sample containing data from N signals selected from the O signals and having at least one marked failure time period; creating a K×N confidence vector containing K confidence values for each of the N signals, each confidence value representing a confidence that a pattern x extracted from data in the marked failure time period of a signal belongs to one of the K pattern clusters; training a classifier using the K×N confidence vector; receiving, by a computer, a monitored data sample including data from the O signals; and classifying, by a computer, the monitored data sample as indicating a failure based on at least one of the O signals not among the I signals being in a same signal cluster as one of the I signals and further based on a determination that the at least one of the O signals has confidence values similar to confidence values of the one of the I signals contained in the K×N confidence vector.