A component includes a housing mounted at an entry point of an enclosure protecting a machine. The component includes a first RFID sensor, in the housing, that reads a first RFID tag in close proximity to the first RFID sensor indicating a closed status of a movable barrier of the enclosure at the entry point. Opening of the movable barrier allows entry to the enclosure. The component includes a second RFID sensor, in the housing, that reads a second RFID tag of an authorized person. The component includes a barrier access module that sends an open alert that the movable barrier is in an open state in response to the first RFID sensor not being close enough to read the first RFID tag, and an identification module that sends tag information unique to a person from the second RFID tag.

In one embodiment, a non-transitory computer readable medium may include computer-executable instructions that, when executed by a processor, may cause processor to receive a set of user data associated with a user that is attempting to access an electronic lock, receive a request to actuate a locking mechanism of the electronic lock configured to prevent the user from accessing a machine in an industrial automation system, actuate the locking mechanism in response to the request and the set of user data corresponding to an expected set of data, store a log of the request and the set of user data, and send the log to a cloud-based computing system.

In one embodiment, a non-transitory computer readable medium may include computer-executable instructions that, when executed by a processor, may cause processor to receive a set of user data associated with a user that is attempting to access an electronic lock, receive a request to actuate a locking mechanism of the electronic lock configured to prevent the user from accessing a machine in an industrial automation system, actuate the locking mechanism in response to the request and the set of user data corresponding to an expected set of data, store a log of the request and the set of user data, and send the log to a cloud-based computing system.

Disclosed is a process for creating an event prediction model that employs a data-driven approach for selecting the model's input data variables, which, in one embodiment, involves selecting initial data variables, obtaining a respective set of historical data values for each respective initial data variable, determining a respective difference metric that indicates the extent to which each initial data variable tends to be predictive of an event occurrence, filtering the initial data variables, applying one or more transformations to at least two initial data variables, obtaining a respective set of historical data values for each respective transformed data variable, determining a respective difference metric that indicates the extent to which each transformed data variable tends to be predictive of an event occurrence, filtering the transformed data variables, and using the filtered, transformed data variables as a basis for selecting the input variables of the event prediction model.

A workwear unit for detecting, documenting, analyzing, monitoring and/or teaching processes includes a portable control system, at least one functional module connected with the portable control system, and which comprises at least a sensor module and a peripheral device, and at least a glove fastening the portable control system and the functional module to a user's body. The portable control system and the sensor module form a structural unit connected with the glove. The sensor module has a barcode scanner fastened to the back of a hand. The peripheral device has an electrical release which is arranged outside on an index finger of the glove.

There is provided a configuration at least including a screen control part configured to display on a display part a maintenance component management screen displaying a component, a mechanism, or both, as a maintenance component, a collection part configured to collect component data related to the maintenance component, a determination part configured to compare a cumulative value of the component data with a predetermined threshold value to determine the cumulative value exceeding the threshold value, a calculation part configured to calculate a replacement time, and an operation part configured to calculate replacement times based on an average value of the component data and a cumulative value of the component data for each predetermined cycle, display the maintenance component sequentially from the maintenance component reaching the earliest replacement time, and display the maintenance component on the display part in a state where the component data is updated for the predetermined cycle.

An electronic processor of a wireless fob is configured to establish a first communication link between the wireless fob and an external device, and receive, over the first communication link, first identification information and credential information of a power tool device from the external device. The electronic processor is further configured to receive, via the wireless transceiver, an identification signal including second identification information from the power tool device. The electronic processor is further configured to identify the power tool device by determining that the first identification information matches with the second identification information. The electronic processor is further configured to transmit the credential information to the power tool device to establish a second communication link between the wireless fob and the power tool device and transmit, over the second communication link, a command to the power tool device to control an operation of the power tool device.

Disclosed is a process for creating an event prediction model that employs a data-driven approach for selecting the model’s input data variables, which, in one embodiment, involves selecting initial data variables, obtaining a respective set of historical data values for each respective initial data variable, determining a respective difference metric that indicates the extent to which each initial data variable tends to be predictive of an event occurrence, filtering the initial data variables, applying one or more transformations to at least two initial data variables, obtaining a respective set of historical data values for each respective transformed data variable, determining a respective difference metric that indicates the extent to which each transformed data variable tends to be predictive of an event occurrence, filtering the transformed data variables, and using the filtered, transformed data variables as a basis for selecting the input variables of the event prediction model.

A diagnostic device includes a reception unit and a determination unit. The reception unit is configured to receive context information and sensing information. The context information corresponds to a certain operation of a target item that constitutes a target device. The context information is a piece of a plurality of pieces of context information each describing an operation of the target item determined depending on a type of operation of the target device. The sensing information is on a physical quantity that varies in accordance with the operation of the target item. The determination unit is configured to determine a state of the target item based on the sensing information detected while the target item is performing the certain operation, and based on a model corresponding to the received context information. The model is a model of one or more models respectively defined for one or more pieces of the context information.

A diagnostic device includes a reception unit and a determination unit. The reception unit is configured to receive context information and sensing information. The context information corresponds to a certain operation of a target item that constitutes a target device. The context information is a piece of a plurality of pieces of context information each describing an operation of the target item determined depending on a type of operation of the target device. The sensing information is on a physical quantity that varies in accordance with the operation of the target item. The determination unit is configured to determine a state of the target item based on the sensing information detected while the target item is performing the certain operation, and based on a model corresponding to the received context information. The model is a model of one or more models respectively defined for one or more pieces of the context information.