@ARTICLE\{IMM2005-03602,
    author       = "N. H. Pontoppidan and S. Sigurdsson and J. Larsen",
    title        = "Condition monitoring with Mean field independent components analysis",
    year         = "2005",
    month        = "nov",
    keywords     = "Condition monitoring, Acoustic Emmission, Components Analysis, Unsupervised learning, {ICA,} {MFICA}",
    pages        = "1337-1347",
    journal      = "Mechanical Systems and Signal Processing",
    volume       = "19",
    editor       = "J\'{e}r\^{o}me Antoni",
    number       = "6",
    publisher    = "Elsevier",
    note         = "Special Issue: Blind Source Separation",
    url          = "http://dx.doi.org/10.1016/j.ymssp.2005.07.005",
    abstract     = "We discuss condition monitoring based on mean field independent
components analysis of acoustic emission energy signals. Within this
framework it is possible to formulate a generative model that
explains the sources, their mixing and also the noise statistics of
the observed signals. By using a novelty approach we may detect
unseen faulty signals as indeed faulty with high precision, even
though the model learns only from normal signals. This is done by
evaluating the likelihood that the model generated the signals and
adapting a simple threshold for decision. Acoustic emission
energy signals from a large diesel engine is used to demonstrate this
approach. The results show that mean field independent components
analysis gives a better detection of fault compared to principal
components analysis, while at the same time selecting a more compact
model",
    isbn_issn    = "0888-3270"
}