@CONFERENCE\{IMM2004-03131,
    author       = "N. H. Pontoppidan and J. Larsen",
    title        = "Non-stationary condition monitoring through event alignment",
    year         = "2004",
    month        = "sep",
    keywords     = "Condition Monitoring, Non-stationarity, Diesel Engine",
    pages        = "499-508",
    booktitle    = "{IEEE} Workshop on Machine Learning for Signal Processing",
    volume       = "",
    series       = "",
    editor       = "",
    publisher    = "{IEEE} Press",
    organization = "",
    address      = "Piscataway, New Jersey",
    url          = "http://isp.imm.dtu.dk/mlsp2004",
    abstract     = "We present an event alignment framework which enables
change detection in non-stationary signals. change detection.
Classical condition monitoring frameworks have been restrained to
laboratory settings with stationary operating conditions, which are
not resembling real world operation. In this paper we apply the
technique for non-stationary condition monitoring of large diesel
engines based on acoustical emission sensor signals. The performance
of the event alignment is analyzed in an unsupervised probabilistic
detection framework based on outlier detection with either
Principal Component Analysis or Gaussian Processes modeling.
We are especially interested in the true performance of the condition
monitoring performance with mixed aligned and unaligned
data, e.g. detection of fault condition of unaligned examples versus
false alarms of aligned normal condition data. Further, we expect
that the non-stationary model can be used for wear trending due
to longer and continuous monitoring across operating condition
changes."
}