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Reliable monitoring for detection of damage in epicyclic gearboxes is a serious concern for all industries in which these gearboxes operate in a harsh environment and in variable operational conditions. In this paper, autonomous multidimensional novelty detection algorithms are used to estimate the gearbox’ health state based on vectors of features calculated from the vibration signal. The authors examine various feature vectors, various sources of data and many different damage scenarios in order to compare novel detection algorithms based on three different principles of operation: a distance in the feature space, a probability distribution, and an ANN (artificial neural network)-based model reconstruction approach. In order to compensate for non-deterministic results of training of neural networks, which may lead to different network performance, the ensemble technique is used to combine responses from several networks. The methods are tested in a series of practical experiments involving implanting a damage in industrial epicyclic gearboxes, and acquisition of data at variable speed conditions.
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