Anomaly detection apparatus, method and computer-readable medium

A computer-implemented anomaly detection method comprising:

• obtaining a plurality of sets of waveform data of a periodic electric waveform signal of an electric appliance, with a length of each of the plurality of sets of waveform data being set to one cycle time of the periodic electric waveform signal;
• transforming, using FFT (fast Fourier transform) processing, each of the plurality of sets of waveform data from a time domain into a frequency domain representation including a plurality of frequency bins of a fundamental frequency which is a reciprocal of the one cycle time and harmonics of the fundamental frequency;
• applying a frequency processing including normalization and filtering to the frequency domain representation of each of the plurality of sets of waveform data to obtain a frequency spectrum for each of the plurality sets of waveform data;
• extracting and separating odd and even frequency harmonics of the fundamental frequency from the frequency spectrum of each of the plurality of sets of waveform data to create odd and even frequency harmonic matrices;
• performing canonical correlation analysis (CCA) on the odd and even frequency harmonic matrices to obtain CCA features;
• performing linear transformation on the CCA features to obtain linear transformed features;
• generating a cluster model based on the linear transformed features;
• performing magnitude quantization of the frequency spectrum of each of the plurality of sets of waveform data to identify normal and anomalous waveform signals;
• and
• outputting at least the identified result,
• wherein the magnitude quantization includes:
• applying filtering to the frequency spectrum of each of the plurality of sets of waveform data;
• transforming, using inverse FFT processing, the frequency spectrum of each of the plurality of sets of waveform data into a time domain representation;
• creating a two bins histogram based on an effective magnitude of each of the plurality of sets of waveform data;
• creating a list or a vector, composed of a larger histogram count value out of the two-bins;
• and
• identifying the normal and anomalous waveform signals.

A non-transitory computer-readable recording medium storing a program causing a computer to execute processing comprising:

• obtaining a plurality of sets of waveform data of a periodic electric waveform signal of an electric appliance, with a length of each of the plurality of sets of waveform data being set to one cycle time of the periodic electric waveform signal;
• transforming, using FFT (fast Fourier transform) processing, each of the plurality of sets of waveform data from a time domain into a frequency domain representation including a plurality of frequency bins of a fundamental frequency which is a reciprocal of the one cycle time and harmonics of the fundamental frequency;
• applying a frequency processing including normalization and filtering to the frequency domain representation of each of the plurality of sets of waveform data to obtain a frequency spectrum for each of the plurality sets of waveform data;
• extracting and separating odd and even frequency harmonics of the fundamental frequency from the frequency spectrum of each of the plurality of sets of waveform data-sets to create odd and even frequency harmonic matrices;
• performing canonical correlation analysis (CCA) on the odd and even frequency harmonic matrices to obtain CCA features;
• performing linear transformation on the CCA features to obtain linear transformed features;
• generating a cluster model based on the linear transformed features;
• performing magnitude quantization of the frequency spectrum of each of the plurality of sets of waveform data to identify normal and anomalous waveform signals;
• and
• outputting at least the identified result,
• wherein the magnitude quantization includes:
• applying filtering to the frequency spectrum of each of the plurality of sets of waveform data;
• transforming, using inverse FFT processing, the frequency spectrum of each of the plurality of sets of waveform data into a time domain representation;
• creating a two bins histogram based on an effective magnitude of each of the plurality of sets of waveform data;
• creating a list or a vector, composed of a larger histogram count value out of the two-bins;
• and
• identifying the normal and anomalous waveform signals.