A machine learning device for detecting an indication of an occurrence of chatter in a tool for a machine tool, includes a state observation unit which observes at least one state variable of a vibration of the machine tool itself, a vibration of a building in which the machine tool is installed, an audible sound, an acoustic emission and a motor control current value of the machine tool, in addition to a vibration of the tool; and a learning unit which generates a learning model based on the state variable observed by the state observation unit.

A method (1000) of diagnosis of operation of a machine tool (10, 100) that includes one or more axes (X, Y, Z) moved by one or more actuators (101, 102, 104) and at least one sensor (30) coupled to the machine tool (10, 100), the method (1000) comprising operations of: generating (1200) a programming sequence of movement of the axes (X, Y, Z) of the machine tool (10, 100); controlling (1210) the movement of the axes (X, Y, Z) of the machine tool (10, 100) according to the programming sequence; receiving (1220) a read-out signal (S) of the at least one sensor (30) coupled to the machine tool (10, 100); and processing (1230) the read-out signal (S) of the at least one sensor (30) coupled to the machine tool (10, 100). The programming sequence comprises instructions that are such as to apply (T) at least one single impulsive variation of a kinematic quantity that regards one or more actuators (101, 102, 104). The operation (1230) of processing the read-out signal (S) comprises processing a response of the machine tool (10, 100) to at least one single impulsive variation. The operation (1230) of processing the read-out signal (S) comprises artificial-neural-network processing (206) via one or more artificial neural networks (206, 2060) configured for analysing operating profiles in particular, one or more signals indicative of the status of the machine tool (W) in the read-out signal (S).

A cell control system capable of estimating a cause of an alarm by estimating an influence of noise in a plurality of machines includes a machine operation instruction unit for transmitting an operation instruction to a managed manufacturing machine, a noise value collection unit for collecting detected noise information, an operation information collection unit for collecting operation information of a manufacturing machine, a learning unit for creating a learning model by performing machine learning using the collected operation information collected as an input signal and the detected noise information as an instruction signal, an estimation unit for analyzing the learning model to estimate operation information corresponding to a noise factor, and an operation instruction change unit for instructing the machine operation instruction unit to change instruction content based on the operation information corresponding to the noise factor.

A monitoring apparatus may include reception logic operable to receive processing characteristic data generated during the processing of the effluent stream; segregation logic operable to segregate the processing characteristic data into contributing processing characteristic data associated with contributing periods which contribute to a condition of the at least one processing tool and non-contributing processing characteristic data associated with non-contributing periods which fail to contribute to the condition; and fault logic operable to utilise the contributing processing characteristic data and to exclude the non-contributing processing characteristic data when determining a status of the condition.

A numerical controller acquires state data including information indicating a machining state and information indicating a selected menu item, creates a machine learning model for determining a menu item display order in menu display based on the state data, and determines a menu item display order in the menu display based on the created machine learning model and the state data.

In an approach to CRAI and risk framework for manufacturing applications, a computer-implemented method for causal effect prediction includes identifying, by one or more computer processors, an intervention, where 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 method of generating audio to obtain manipulated audio data includes receiving textual descriptions of audio associated with operation of a device, receiving audio data associated with the operation of the device, generating, based on the textual descriptions, descriptive text inputs of audio features associated with the operation of the device, generating the manipulated audio data based on the descriptive text inputs and the audio data, the manipulated audio data including the one or more audio features indicative of faults associated with the descriptive text inputs, training a machine learning (ML) model to diagnose the faults using the manipulated audio data, the ML model being trained to generate an output indicative of the faults based on audio data obtained during the operation of the device, and, based on convergence during the training, outputting a trained ML model configured to generate the output indicative of the faults.