A system for controlling an operation of a machine including a plurality of actuators assisting one or multiple tools to perform one or multiple tasks, in response to receiving an acoustic mixture of signals generated by the tool performing a task and by the plurality of actuators actuating the tool, submit the acoustic mixture of signals into a neural network trained to separate from the acoustic mixture a signal generated by the tool performing the task from signals generated by the actuators actuating the tool to extract the signal generated by the tool performing the task from the acoustic mixture of signals, analyze the extracted signal to produce a state of performance of the task, and execute a control action selected according to the state of performance of the task.

A diagnostic system includes a tool to perform machining on a workpiece, a test piece to be subjected to test machining by the tool for abnormal detection, a sensor to generate sensor data representing a physical quantity generated in the test machining, and circuitry. The test piece is different from the workpiece. The circuitry controls the tool to perform the test machining in each cycle of machining on the workpiece and calculates a degree of abnormality from the sensor data of the test machining. The test machining is performed on the test piece under a test machining condition corresponding to a machining condition of the machining on the workpiece.

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 method for configuring an interface device connected to a control device and a field device, wherein the method includes receiving a first machine learning application having a plurality of logical components connected in a pipeline, where the first machine learning application serves to analyze a signal from the field device utilizing a first machine learning model, generating a plurality of code blocks utilizing a translator based on the plurality of logical components of the first machine learning application, connecting the plurality of code blocks in accordance with the pipeline of the first machine learning application to generate a first output from the signal from the field device, and deploying the connected code blocks on firmware of the interface device including creating a virtual port connectable to the control device, and where the virtual port serves to transmits the first output to the control device.

In an approach to CRAI and risk framework for manufacturing applications, there is thus provided a computer-implemented method for causal effect prediction, the computer-implemented method including: identifying, by one or more computer processors, an intervention, wherein the intervention is selected from the group consisting of threats, failures, corrections, and relevant outputs; collecting, by the one or more computer processors, process dependency data; creating, by the one or more computer processors, an intervention model; combining, by the one or more computer processors, the process dependency data and the intervention model to create a combined process dependency graph; training, by the one or more computer processors, a causal relational artificial intelligence (CRAI) model; and determining, by the one or more computer processors, an estimate of an intervention efficacy.

A data classification apparatus and method for providing expanded information are proposed. The method may include collecting time-series sensor data from an Internet-of-Things (IoT) sensor provided in or installable in a machine, and generating first processed data in which the time-series sensor data is highlighted. The method may also include generating, based on the first processed data, second processed data for determining a state of the machine, and classifying the state of the machine, based on the second processed data. The state of the machine may include one or more of a first state in which the machine is active and the first processed data is included in a non-pattern section in which no pattern is visualized, and a second state in which the machine is active and the first processed data is included in a pattern section in which an arbitrary pattern is visualized.

A life expectancy prediction system for a target tool includes a processing machine body, a detector to detect a state data, a learned model storage unit to store learned models generated by executing machine learning using training datasets, including an explanatory variable and an objective variable, the explanatory variable being the state data and the objective variable being a number of first remaining machining times, the learned model storage unit being to store the learned models, each for each of the tools and a remaining machining times prediction unit to select, based on the state data, one learned model and predict a number of second remaining machining times, using the one learned model and the state data.

An online monitoring device of 3D printing equipment includes a signal collection module, a signal processing module, a feature extraction module, a monitoring module and a knowledge base module. A vibration signal of a preset component of the 3D printing equipment is collected by a vibration sensor. The collected vibration signal of each preset component is converted from an analog signal to a digital signal and the spectrum characteristics are extracted. Based on the spectrum characteristics of each preset component, the operation state type of the preset component is obtained by a comparative analysis model. The knowledge base module is configured to store newly added samples and initial samples of the 3D printing equipment. The initial samples include spectrum characteristic information and corresponding fault category of known faults, and the newly added samples include spectrum characteristic information and corresponding fault category of new faults.

A device for determining a spindle status of a spindle of a machine tool includes a detector for detecting sensor data of the spindle for a defined time window. A processing unit analyses the sensor data through artificial intelligence by calculating a defined feature of the sensor data for the defined time window and determining a spindle status from the sensor data. An output member outputs the determined spindle status.

A method for monitoring during a robot-assisted first or second process-includes (a.1) detecting process data; and (a.2) performing a model-based assessment with the aid of a machine-learned model on the basis of the detected process data; wherein, if the model-based assessment satisfies an examination criterion, in particular depending on an external confirmation: (b.1) performing a test assessment with the aid of a testing authority; and (b.2) training the machine-learned model further on the basis of the test assessment; and then, for the first process optionally performed again: (c.1) detecting process data; (c.2) performing the model-based assessment with the aid of the further trained model on the basis of the detected process data; and (c.3) monitoring during the first process is performed on the basis of this assessment.