A diagnostic apparatus of the invention acquires normal data related to an operating state during normal operation of an industrial machine, stores the normal data, generates a learning model by learning based on the stored normal data, and performs an estimation process for normality or abnormality of an operation of the industrial machine using the learning model. The diagnostic apparatus of the invention further generates verification data including at least one piece of abnormal data based on the stored normal data to verify validity of the learning model on receiving a result of the estimation process using the learning model based on the verification data.

A pet toy includes a wheel base that moves randomly or pseudo-randomly and a rod detachably connected to the wheel base configured to hold a treat or other food item desirable to the pet companion is described herein. The wheel base and/or the rod may have one or more modes of movement that may be activated based on a movement algorithm that randomly or pseudo-randomly moves/turns the wheel base and/or the rod and/or a one or more sensors that detect obstacles. A proximate end of the may be detachably connected to a corresponding rod connector of the wheel base. The distal end of the rod may be configured to grab and/or hold the food item and/or have the food item wedged into it.

A method that is used for self-learning manufacturing scheduling for a flexible manufacturing system that is used to produce at least a product is provided. The manufacturing system consists of processing entities that are interconnected through handling entities. The manufacturing scheduling will be learned by a reinforcement learning system on a model of the flexible manufacturing system. The model represents at least a behavior and a decision making of the flexible manufacturing system. The model is realized as a petri net.

An order of the processing entities and the handling entities is interchangeable, and therefore, the whole arrangement is very flexible.

The method for self-learning manufacturing scheduling for a flexible manufacturing system (FMS) with processing entities that are interconnected through handling entities is disclosed. The manufacturing scheduling is learned by a reinforcement learning system on a model of the flexible manufacturing system. The model represents at least the behavior and the decision making of the flexible manufacturing system, and the model is transformed in a state matrix to simulate the state of the flexible manufacturing system. A self-learning system for online scheduling and resource allocation is also provided. The system is trained in a simulation and learns the best decision from a defined set of actions for many every situation within an FMS. A decision may be made in near real-time during a production process and the system finds the optimal way through the FMS for every product using different optimization goals.

A robot includes a movable section capable of moving, a driving section configured to drive the movable section, a transmitting section located between the movable section and the driving section, a first position detecting section configured to detect a position on an input side of the transmitting section, a second position detecting section configured to detect a position on an output side of the transmitting section, and an inertial sensor provided in the movable section. The driving section is driven on the basis of a detection result of the first position detecting section, a detection result of the second position detecting section, and a detection result of the inertial sensor.

A machine learning apparatus includes: a state observation unit that observes a characteristic shape, an adopted plan, and a determination result as state variables, the characteristic shape representing a shape of a part of a product of wire electric discharge machining, adjustment of machining conditions being deemed as necessary for the part of the product, the adopted plan being an adjustment method selected from among one or more adjustment methods for adjusting the machining conditions to improve machining performance for the part indicated by the characteristic shape, the determination result indicating whether implementation of the adopted plan is effective in improving machining performance for the part corresponding to the characteristic shape; and a learning unit that learns the machining condition adjustment method according to a data set created based on the state variables.

A machine learning device, which performs a task using a plurality of industrial machines and learns task sharing for the plurality of industrial machines, includes a state variable observation unit which observes state variables of the plurality of industrial machines; and a learning unit which learns task sharing for the plurality of industrial machines, on the basis of the state variables observed by the state variable observation unit.

A machine learning apparatus includes: a state observation unit that observes a characteristic shape, an adopted plan, and a determination result as state variables, the characteristic shape representing a shape of a part of a product of wire electric discharge machining, adjustment of machining conditions being deemed as necessary for the part of the product, the adopted plan being an adjustment method selected from among one or more adjustment methods for adjusting the machining conditions to improve machining performance for the part indicated by the characteristic shape, the determination result indicating whether implementation of the adopted plan is effective in improving machining performance for the part corresponding to the characteristic shape; and a learning unit that learns the machining condition adjustment method according to a data set created based on the state variables.

The present disclosure provides systems and methods for analyzing runs. In an aspect, the present disclosure provides a method for extracting semantic artifacts from manufacturing data. The method may comprise: (a) receiving manufacturing data corresponding to a manufacturing process, wherein the manufacturing data comprises at least one of observation data, context data, temporal metric data, and/or overall equipment effectiveness (OEE) components associated with the manufacturing process; (b) extracting one or more semantic artifacts from the manufacturing data; and (c) using the one or more semantic artifacts to generate a summary representation of the manufacturing process.

A method and dynamic learning system for providing dynamic cross learning is disclosed. The dynamic learning system identifies one or more changes in an environment in which an automated task performing device is scheduled to perform one or more activities. The dynamic learning system initiates a dynamic learning associated with the one or more changes for the automated task performing device based on pre-stored contextual information. Based on the dynamic learning, one or more actions is provided to the automated task performing device to perform the one or more activities in view of the one more changes. Therefore, the present disclosure facilitates dynamic determination and analysis of environment and situation for the automated task performing device for performing the activities. Thus, leading to dynamic decision-making to provide adjustment to the automated task performing device in any situation.