Anomaly detection apparatus, anomaly detection method, and program

An anomaly detection apparatus comprising:

• a neural network comprising:
• an encoder for projecting an input feature value into a latent space, and a decoder for reconstructing the output of the encoder, wherein the latent space is a closed manifold and holds a normal distribution obtained by learning normal data and an anomalous distribution obtained by learning anomalous data on the manifold;
• and
• a processor configured to:
• obtain a reconstruction result output by the decoder when a feature value of target data is input to the encoder, wherein the target data represents operation data of a machine as waveform data processed by a sensor;
• and
• calculate an anomaly score of the target data based on distances between the reconstruction result and the normal distribution and distances between the reconstruction result and the anomalous distribution,
• wherein the encoder outputs angle parameters and a scalar parameter corresponding to a logarithmic variance of a Gaussian distribution, and
• the processor further configured to:
• set two different distributions on the manifold as a prior distribution of the normal distribution and a prior distribution of the anomalous distribution;
• learn the normal distribution and the anomalous distribution with the concentration parameter set to a predetermined constant among the outputs of the encoder obtained when a feature value of learning data composed of the normal data and the anomalous data is input;
• and
• learn the normal distribution and the anomalous distribution by obtaining vectors on a unit hyperplane that are the result of multiplying a rotation matrix and a fixed vector having any value in common overall, multiplying the concentration parameter of the prior distribution by the inner product of the vectors on the unit hyperplane and the direction vector of the prior distribution, subtracting the resulting value from an exponent of the scalar parameter output by the encoder when the feature value of the learning data is input, and setting the resulting value as a KL divergence value, wherein the rotation matrix is constituted by the angle parameters output by the encoder when a feature value of the learning data is input, and is constructed using vectors obtained by adding a value obtained by multiplying a random number generated from a Gaussian distribution by the scalar parameter output by the encoder when the feature value of the learning data is input, to the angle parameters output by the encoder;
• and
• determine, based on the calculated anomaly score of the target data and a predetermined threshold, an anomalous state of operation of the machine.

An anomaly detection method comprising:

• storing, in a storage of an anomaly detection apparatus, a neural network comprising an encoder for projecting an input feature value into a latent space, and a decoder for reconstructing the output of the encoder, wherein the latent space is a closed manifold, and holds a normal distribution obtained by learning normal data and an anomalous distribution obtained by learning anomalous data on the manifold, the encoder outputs angle parameters and a concentration parameter, and;
• obtaining, by a reconstruction result output by the decoder when a feature value of target data is input to the encoder, wherein the target data represents operation data of a machine as waveform data processed by a sensor;
• and
• calculating, by an anomaly score of the target data based on distances between the reconstruction result and the normal distribution and distances between the reconstruction result and the anomalous distribution,
• setting, two different distributions on the manifold as a prior distribution of the normal distribution and a prior distribution of the anomalous distribution;
• learning, the normal distribution and the anomalous distribution with the concentration parameter set to a predetermined constant, among the outputs of the encoder obtained when a feature value of learning data composed of the normal data and the anomalous data is input;
• and
• learning, the normal distribution and the anomalous distribution by using a KL divergence upper limit value, wherein the KL divergence upper limit value is calculated based on a vector obtained by multiplying a rotation matrix and a fixed vector having any value in common overall, a direction vector of the prior distribution, and a concentration parameter of the prior distribution;
• and the rotation matrix is constituted by the angle parameters output by the encoder when a feature value of the learning data is input, and is constructed using vectors obtained by adding a value obtained by multiplying a random number generated from a Gaussian distribution and a value obtained by converting the concentration parameter output by the encoder when the feature value of the learning data is input into a standard deviation equivalent of a Gaussian distribution, to the angle parameters output by the encoder;
• and
• determining based on the calculated anomaly score of the target data and a predetermined threshold, an anomalous state of operation of the machine.