Patent ID: 11966218
Assignee: MITSUBISHI ELECTRIC CORPORATION
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A diagnosis device comprising:
processing circuitry configured to
acquire a series of input values from a device having a signal source in a factory via a network as an input signal to be diagnosed as to presence or absence of abnormality;
diagnose a diagnosis of presence or absence of abnormality from
a first index value indicating a distance between an input vector and a predetermined first reference vector, the input vector having components that are the input values of the series acquired, and
a second index value indicating an angle between the input vector and a predetermined second reference vector; and
output the diagnosis, wherein
the processing circuitry is configured to
diagnose the presence or absence of abnormality from a weighted sum of the first index value and the second index value,
learn the first reference vector and the second reference vector from a learning signal,
acquire the learning signal and the input signal,
learn the first reference vector and the second reference vector from the learning signal,
calculate respective weights of the first and second reference vectors in accordance with results of the learning, and
the weighted sum is equal to a sum of values obtained by multiplying the first index value and the second index value by the respective weights calculated, the first index value indicating the distance between the input vector and the first reference vector, the second index value indicating the angle between the input vector and the second reference vector, and wherein the processing circuitry is further configured to
cut out, from the learning signal, a plurality of series of learning values, to generate multiple learning vectors that each have, as components, the learning values of a series of the plurality of series,
perform clustering in accordance with a distance between the learning vectors, to learn the first reference vector, and
perform clustering in accordance with an angle between the learning vectors, to learn the second reference vector.