2009 IEEE International Conference on Automation Science and Engineering 2009
DOI: 10.1109/coase.2009.5234094
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Fault diagnosis and failure prognosis for engineering systems: A global perspective

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Cited by 41 publications
(46 citation statements)
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“…1) The first one compares the amplitude of the low sideband harmonic, LSH, to the amplitude of the fundamental component from the current spectrum (1). In order to increase its accuracy, a Blackman window was used to avoid leakage effects and the Teager-Kaiser algorithm, having a minimal computational cost, was utilized to enhance the automatic peak detection [24] in an interval determined by the encoder reading.…”
Section: B Stationary Indicators Computedmentioning
confidence: 99%
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“…1) The first one compares the amplitude of the low sideband harmonic, LSH, to the amplitude of the fundamental component from the current spectrum (1). In order to increase its accuracy, a Blackman window was used to avoid leakage effects and the Teager-Kaiser algorithm, having a minimal computational cost, was utilized to enhance the automatic peak detection [24] in an interval determined by the encoder reading.…”
Section: B Stationary Indicators Computedmentioning
confidence: 99%
“…Following the system architecture depicted in [1,6], a Particle Filtering approach is implemented as diagnosis module, which permits the combination of both Boolean and continuous variables, the use of non-linear models (7) and an easy linking to prognosis routines, among other advantages.…”
Section: B Diagnosis Modulementioning
confidence: 99%
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