US 12,181,865 B2 · Operating state classification system, and operating state classification method

Abstract

The objective of the present invention is to realize an operating state classification system having a classification accuracy that continuously improves through additional learning, and which is appropriately protected from unauthorized duplication of a classification function. In this operating state classification system, in which an edge device and a server are connected by means of a communication network, and which inputs sensor data into a neural network and outputs a state label: the edge device includes a first storage unit which stores an upstream side of the neural network, and a dimensionality reduction unit which inputs the sensor data into the upstream side of the neural network and outputs intermediate data; and the server includes a second storage unit which stores a downstream side of the neural network, an inference executing unit which inputs the intermediate data into the downstream side of the neural network and outputs the state label, and a learning unit which updates the downstream side of the neural network by means of additional learning.

Claims9

Dependent claims

An operating state classification system having an edge device and a server connected via a communication network, which inputs sensor data to a neural network and outputs a state label,

wherein the edge device includes:
a first storage device which stores a pre-stage side of the neural network, and
a dimension compression system which inputs the sensor data to the pre-stage side of the neural network and outputs intermediate data, and
wherein the server includes:
a second storage device which stores a post-stage side of the neural network,
an inference execution system which inputs the intermediate data to the post-stage side of the neural network and outputs the state label, and
a learning system which updates the post-stage side of the neural network by additional learning;
wherein the pre-stage side of the neural network stored in the edge device is an encoder which is a pre-state portion of a self-encoder learnt for each state to be classified, and
wherein the post-stage side of the neural network stored in the server is a state classifier which inputs intermediate data output by the encoder and outputs the state label.

6 more elements

An operating state classification method comprising:

a learning step of learning a neural network inputting sensor data and outputting a state label on a server;
a first storing step of storing a pre-stage side of the neural network in an edge device, wherein the pre-stage side of the neural network stored in the edge device is an encoder which is a pre-state portion of a self-encoder learnt for each state to be classified;
a second storing step of storing a post-stage side of the neural network in the server, and wherein the post-stage side of the neural network stored in the server is a state classifier which inputs intermediate data output by the encoder and outputs the state label;
a third storing step of storing a decoder which is a post-stage portion of the self-encoder is also stored in the server;
and
an additional learning step of additionally learning the post-stage side of the neutral network on the server.

3 more elements

Dependencies are inferred from the claim text.

Operating state classification system, and operating state classification method

Life of this patent

Operating state classification system, and operating state classification method

Life of this patent

Abstract

Claims9