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.

The first part of this article brings the author’s reply to the participants of the panel discussion of his book Culture as Verb (Anna Łebkowska, Jakub Momro, Tomasz Rakowski and Dorota Wolska). In the second part he outlines his premises and explains the analytical vocabulary that has enabled him to move from an active to a passive ‘verbal’ understanding of culture. He also draws a broad outline of prospective new research that would complement his project. Central to it is the exploration of what he believes is the dominant contemporary cultural experience, which is based on active participation. To characterize its most important features and forms we should make use of the following, newly defi ned analytical concepts – passivity, the present moment, immersion, and testimonial authority.