Disclosed is a method for detecting an abnormality performed by a computing device including one or more processors according to some aspects of the present disclosure. The method may include: acquiring first sensor data output from a first sensor among a plurality of sensors; selecting a first neural network model determined as an optimal network model from among a plurality of neural network models different from each other; generating first output data by inputting the first sensor data to the first neural network model; and determining whether an abnormality exists in the first sensor data based on the first sensor data and the first output data.

An electric motor drive system is provided wherein safety is achieved by commanding the process system of the relevant axis or set of axes to execute motion that follows a defined path, namely trajectory of position, velocity and acceleration against time, re-constructing an identical trajectory in the safety system and in, the safety system, supervising deviations between the safely reconstructed target position and the safe measurement of position.

To provide a numerical controller that can detect the position at which the look-ahead blocks used to determine an acceleration/deceleration operation is insufficient in a machining program in order to stabilize feed rate, cutting speed and other factors. A numerical controller includes a program execution unit that executes a machining program, a program look-ahead unit that looks ahead at the machining program in parallel with execution of the machining program, a theoretical value calculation unit that calculates theoretical values per block in processing time for the machining program from the feed rate of the machine tool and lengths of minute straight lines that make up the machining path followed by the machine tool, a measured value calculation unit that calculates actually measured values per block in the look-ahead time taken by the program look-ahead unit and the processing time for the machining program while the machining program is being executed, and an abnormal block detection unit that detects an abnormal block that is a block at which the result of subtracting the total of the theoretical values from the total of the actually measured values is more than a prescribed value.

A wireless endpoint provided in an automatic inspection system includes a sound collection unit that collects sounds produced by an inspection target, an analysis unit that analyzes the collected sound and obtains a degree of difference between the collected sound and a normal sound learned in advance as an analysis result, a wireless communication unit that wirelessly transmits data including the analysis result to a wireless base station, and a power supply unit that supplies power to the above units. The wireless base station manages the data received from the wireless endpoint. In response to a request from a monitoring terminal that monitors the status of the inspection target, the wireless base station transmits the analysis result retrieved from the data to the monitoring terminal. The monitoring terminal performs a process of disclosing a status of the inspection target determined from the analysis result.

A smart accessory device is designed to be used with a circuit breaker so as to be able to actuate an operating mechanism of the circuit breaker in order to either open or close the separable contacts of the circuit breaker. The accessory device can be one of a shunt trip, spring release, or under voltage release device. The accessory device continually executes diagnostics to determine whether the power being supplied by an external power source is sufficient to enable the accessory device to actuate the operating mechanism of the associated circuit breaker. If the accessory device determines that the power being supplied by the external source is insufficient, the device triggers an alarm in order to notify a user that the power source is not meeting the power requirements of the device.

A system for monitoring conditions associated with operation of an underground machine includes a sensor that is disposed on the machine. The sensor is configured to output tri-axial acceleration data associated with the underground machine. The system also includes at least one controller that is disposed in communication with the sensor. The at least one controller is configured to evaluate the tri-axial acceleration data with at least one pre-defined criteria, and determine whether the tri-axial acceleration data is in excess of a threshold associated with the at least one pre-defined criteria based on the evaluation. The controller is also configured to record an occurrence of an abnormality in the condition associated with operation of the underground machine on the basis of whether the tri-axial acceleration data is in excess of a threshold associated with the at least one pre-defined criteria.

A state determination device acquires data related to an injection molding machine, stores a learning model obtained by learning an operation state of the injection molding machine with respect to the data, and performs estimation using the learning model based on the data. Further, the state determination device acquires a correction coefficient, which is associated with a type of the injection molding machine and equipment attached to the injection molding machine and numerically converts and corrects the estimation result with a predetermined correction function to which the acquired correction coefficient is applied.

A method and safety-oriented control device for determining and/or selecting a safe condition using a safety-oriented control device configured for safety-oriented control of an apparatus or installation via execution of a safety-oriented control program which, when executed, results in a safe reaction being triggered in the safety-oriented controller, wherein the method is implemented such that an ML model is configured and formed as a result, which is stored in a memory device, of the application of a machine learning method, such that data relevant to the determination of a safe condition are stored in connection with the triggering of the safe reaction, and such that a first safe condition is determined via the data relevant to the determination of the safe condition being applied to the ML model.

A machine tool operation monitoring system which detects an abnormal operation of a machine tool 1 and in which when the operation of each machine tool 1 exceeds a normal operating range of each machine tool 1 and/or when a moving state of a constituent portion of the machine tool 1 and a material exceeds a normal range, wherein the machine tool 1 breaking out an operation abnormality is identified by any one of the following operations:

a. monitoring an image obtained by projecting reflected light from a reflecting display plate 22 provided for each machine tool 1 onto a camera 31; and
b. monitoring a difference in projecting direction of emitted light from a lamp 21 provided for each machine tool 1 onto an optical sensor 32.

A machine tool operation monitoring system which detects an abnormal operation of a machine tool 1 and in which when the operation of each machine tool 1 exceeds a normal operating range of each machine tool 1 and/or when a moving state of a constituent portion of the machine tool 1 and a material exceeds a normal range, wherein the machine tool 1 breaking out an operation abnormality is identified by any one of the following operations: a. monitoring an image obtained by projecting reflected light from a reflecting display plate 22 provided for each machine tool 1 onto a camera 31; and b. monitoring a difference in projecting direction of emitted light from a lamp 21 provided for each machine tool 1 onto an optical sensor 32.