A system includes one or more sensors coupled to industrial equipment, wherein the one or more sensors are configured to obtain one or more measurements associated with one or more operating characteristics of the industrial equipment. The system also includes a computing device that includes a user interface and one or more processors. The one or more processors are configured to: receive the one or more measurements of the one or more operating characteristics of the industrial equipment; determine a status of the industrial equipment based on the one or more measurements; determine a date and a time based on the one or more measurements; and update a cell in a grid of cells organized according to time increments based on the status, the date, and the time.

A computer-based monitoring system and monitoring method implemented in computer software for analyzing the reliability data collected in the maintenance and repair operations in a fleet of assets with parts of the same type over a period of time. The reliability data include reliability event data, such as failures, repairs, and replacements of the parts. The reliability data further include asset usage data, such as usage time, or usage missions, or usage mileage, or usage flight hours, or such. The monitoring system analyses historical reliability data to build a reliability model. This model is further used in reliability SPC algorithms detecting bad actor assets and bad actor parts that show consistently worse reliability than normal parts and assets.

A light engineering client graphical user interface (GUI) for monitoring and troubleshooting an industrial automation system via an electronic display, includes an alarm listing window and an alarm details window. The GUI presents, in the alarm listing window, an alarm listing associated with an alarm based a condition of an industrial automation device, presents, in the alarm details window, detailed information for the alarm, presents, in the alarm details window, a portion of code associated with the industrial automation device, receives an input making modifications to the portion of code associated with the industrial automation device, and modifies the portion of code associated with the industrial automation device based on the received input.

There are provided a slave, a work machine, and a method for storing log information, which are capable of appropriately store the log information when a communication abnormality occurs such that communication cannot be kept between a master and the slave in an industrial network. The control section performs first storage processing storing the log information into a volatile storage section when a communication abnormality occurs such that communication with the master cannot be kept, communication abnormality determination processing for determining whether the communication abnormality has occurred, and second storage processing storing the log information into a non-volatile storage section by acquiring the log information from the volatile storage section, in response to a determination made in that the communication abnormality has occurred as a result of the communication abnormality determination processing.

A computer-implemented method of predicting vehicle faults, the method comprising, at a data processing stage: receiving: i) sets of telematics data each associated with a vehicle identifier, and ii) a vehicle fault dataset, which records historic vehicle fault events, wherein the vehicle fault events are associated in the datasets with cooperating vehicle identifiers; for each of the vehicle identifiers, determining i) a feature object by processing the associated set of telematics data to determine at least one driving style parameter therefrom, the feature object comprising the at least one driving style parameter, and ii) a training label for the feature object based on one or more of the vehicle fault events associated with that vehicle identifier; and using the feature objects and their training labels to train a predictive component, executed at the data processing stage, to learn causal associations between the driving style parameters and the vehicle fault events, such that a feature object comprising at least one target driving style parameter, associated with a target vehicle, inputted to the trained predictive component causes the predictive component to output a corresponding vehicle fault prediction.

A method for extracting a set of principal time series data of dynamic latent variables. The method includes detecting, by a plurality of sensors, dynamic samples of data each corresponding to one of a plurality of original variables. The method also includes analyzing, using a controller, the dynamic samples of data to determine a plurality of latent variables that represent variation in the dynamic samples of data. The method also includes selecting, by the controller, at least one inner latent variable that corresponds to at least one of the plurality of original variables. The method also includes estimating an estimated current value of the at least one inner latent variable based on previous values of the at least one inner latent variable.

A domestic appliance may include a cabinet and a controller mounted to the cabinet. The controller may be configured to direct a diagnostic operation that includes receiving a dense diagnostic signal, transmitting historical use data from a plurality of historical memory slots from the controller of the domestic appliance, clearing the historical use data from the plurality of historical memory slots following transmitting historical use data, designating the plurality of historical memory slots as a unified logging slot for a single diagnostic cycle following clearing, initiating the single diagnostic cycle at the domestic appliance to collect single-cycle data within the unified logging slot, and transmitting the collected single-cycle data from the controller of the domestic appliance.

A device includes a catalog storage unit that stores a catalog having versatility; a catalog control unit that specifies the catalog corresponding to a target machine tool to be subjected to state determination based on machine tool information including an item of facility type and acquires the catalog from the catalog storage unit; a feature quantity extraction unit that extracts a feature quantity from sensor data detected from the target machine tool; a sensor data processing unit that performs state determination of the target machine tool based on the feature quantity distribution included in the acquired catalog and the feature quantity extracted from the sensor data; a feature quantity tuning unit that performs tuning of the feature quantity distribution by mapping the extracted feature quantity to the feature quantity distribution; and a catalog updating unit that updates the catalog based on the feature quantity distribution after tuning.

A system including a deep learning processor obtains response data of at least two data types from a set of process stations performing operations as part of a manufacturing process. The system analyzes factory operation and control data to generate expected behavioral pattern data. Further, the system uses the response data to generate actual behavior pattern data for the process stations. Based on an analysis of the actual behavior pattern data in relation to the expected behavioral pattern data, the system determines whether anomalous activity has occurred as a result of the manufacturing process. If it is determined that anomalous activity has occurred, the system provides an indication of this anomalous activity.

In various embodiments, a method includes receiving one or more sensor streams with an engine. The engine identifies one or more actions that are performed at first and second stations of a plurality of stations within the sensor stream(s). The received sensor stream(s) and identified one or more actions performed at the first and second stations are stored in a data structure. The identified one or more actions are mapped to the sensor stream(s). The engine characterizes each of the identified one or more actions performed at each of the first and second stations to produce determined characterizations thereof. Based on one or more of the determined characterizations, automatically producing a recommendation, either dynamically or post-facto, to move at least one of the identified one or more actions performed at one of the stations to another station to reduce cycle time.