1. A method of machine condition monitoring, comprising: receiving, by a computer, historic operating data including operating data from O signals over time; extracting I patterns x, each pattern x being extracted from operating data from an individual signal; 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 operating data from the O signals; receiving an annotated training data sample containing training data from N signals selected from the O signals, the training data having at least one marked failure time period; creating a K×N confidence matrix 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 matrix; receiving, by a computer, a monitored data sample including monitored data from the O signals; and classifying, by the classifier running on a computer, the monitored data sample as indicating or not indicating a failure based on confidence vectors computed for a plurality of sub-combinations of the monitored data sample, the sub-combinations each having data representing all of the N signals, at least one of the N signals being represented in at least one sub-combination by data from another signal in the same signal cluster c k as the represented signal.