Abstract: A computing system detects an event. (A) A frequency spectrum is computed using a Fourier transform. (B) (A) is repeated a predefined plurality of times with successive windows of observation vectors. Each window of the successive windows includes a subset of the observation vectors. The successive windows include successive subsets selected sequentially in time. (C) An average frequency spectrum is computed from the frequency spectrum computed the predefined plurality of times. (D) A plurality of segmented average frequency spectra is computed from the computed average frequency spectrum. Each segmented average frequency spectrum of the plurality of segmented average frequency spectra is computed for a frequency band of a plurality of predefined frequency bands. (E) When an event has occurred is determined based on the computed plurality of segmented average frequency spectra and a predefined threshold value.

Abstract: A frequency spectrum is computed using a Fourier transform a predefined plurality of times with successive windows of observation vectors, wherein each window of the successive windows includes a subset of observation vectors, wherein the successive windows include successive subsets selected sequentially in time. An average frequency spectrum is computed. A plurality of segmented average frequency spectra is computed, wherein each segmented average frequency spectrum is computed for a frequency band of predefined frequency bands. A distance value is computed using a trained support vector data description model with the segmented average frequency spectra. When an event has occurred is determined based on a comparison between the distance value and a predefined threshold. A vector is computed from the segmented average frequency spectra using t-stochastic neighbor embedding. When an event has occurred based on the comparison, an event indicator is presented on a graph using the vector.

Abstract: A computing system detects an event. (A) A frequency spectrum of observation vectors is computed using a Fourier transform. Each observation vector includes a sensor value. (B) (A) is repeated a predefined plurality of times with successive windows of the observation vectors. Each window of the successive windows includes a subset of the observation vectors. The successive windows include successive subsets selected sequentially in time. (C) An average frequency spectrum is computed from the frequency spectrum computed the predefined plurality of times. (D) A predefined noise filter is applied to the average frequency spectrum to define a filtered frequency spectrum. (E) A distance value is computed between the filtered frequency spectrum and a predefined reference spectrum using a distance computation function. (F) When an event has occurred is determined based on a comparison between the computed distance value and a predefined distance threshold.

Abstract: A computing device identifies outliers. Support vectors, Lagrange constants, a center threshold value, an upper control limit value, and a lower control limit value are received that define a normal operating condition of a system. The center threshold value, the upper control limit value, and the lower control limit value are computed from the vectors and the Lagrange constants. A first plurality of observation vectors is received for a predefined window length. A window threshold value and a window center vector are computed. A window distance value is computed between the window center vector and the support vectors. Based on comparisons between the computed values and the received values, the first plurality of observation vectors is identified as an outlier relative to the normal operating condition of the system. When the first plurality of observation vectors are identified as the outlier, an alert is output.