Methods of managing a fleet of devices are provided, as are methods for configuring a standby device for a job in a workflow environment, and methods for performing a job in a workflow environment. Device information is analyzed, such as information pertaining to verification systems. Device instructions are sent to various locations on a device network in response to a deviation from a parameter value having been detected. The deviation from the parameter value may correspond to printed media and/or indicia produced by one or more devices. A workflow device and a standby device are provided, and the workflow device sends configuration data to the standby device. The standby device installs configuration data and is introduced into the workflow environment.

An unsteadiness detection device (30) is provided which is capable of detecting the operation state of facilities using binary digital signals, the unsteadiness detection device including: a model generation unit (313) to generate a normal model for determining operation states of a plurality of facilities (11) on the basis of operation data which are binary digital signals obtained from the facilities (11) in their steady operation states; an expectation value calculation unit (315) to calculate an expectation value of operation data by applying the normal model to past operation data of the facilities (11); and an unsteadiness detection unit (316) to detect whether or not an operation state of one of the facilities (11) is unsteady by comparing the expectation value of the operation data and a measured value of the operation data.

This disclosure relates to a method and system for monitoring health and predicting failure of an electro-mechanical machine. In an embodiment, the method may include receiving a plurality of operational parameters with respect to the electro-mechanical machine and determining a set of features and a set of events, based on the plurality of operational parameters. The method may further include detecting one or more fault signatures associated the electro-mechanical machine based on at least one of the plurality of operational parameters, the set of features, or the set of events. The method may further include determining at least one of a time to the possible failure and a remaining useful life of the electro-mechanical machine based on at least one of the plurality of operational parameters, the set of features, the set of events, or the one or more fault signature, by using a hybrid machine learning model.

A method and apparatus of monitoring computer devices operating on a network is disclosed. Computer devices are all different and require monitoring settings that are tailored to their specific requirements. One example of the present invention may include a method of monitoring at least one computer device operating on a network. The method may include receiving audit information representing attributes of the computer device and storing the audit information in memory. The method may also include comparing the audit information to a predefined monitor set of objects to be monitored. The method may further include creating a new monitor set based on the comparison of the audit information and the predefined monitor set. The new monitor set is different from the predefined monitor set and is generally used to monitor objects which are included in the audited device. The method may also include monitoring the at least one computer device based on the new monitor set.

A parallel processing system includes at least three parallel processors, state monitoring circuitry, and state reload circuitry. The state monitoring circuitry couples to the at least three parallel processors and is configured to monitor runtime states of the at least three parallel processors and identify a first processor of the at least three parallel processors having at least one runtime state error. The state reload circuitry couples to the at least three parallel processors and is configured to select a second processor of the at least three parallel processors for state reload, access a runtime state of the second processor, and load the runtime state of the second processor into the first processor. Monitoring and reload may be performed only on sub-systems of the at least three parallel processors. During reload, clocks and supply voltages of the processors may be altered. The state reload may relate to sub-systems.

A method for testing compatibility between application software and a mobile working machine. The method includes: operating data that are captured during operation of the mobile working machine are aggregated in a cloud, with the aid of the operating data, the mobile working machine is modeled by a digital twin, and the compatibility of the application software is tested in the cloud with the aid of the digital twin.

Provided is a log analysis system including: an identifying unit that identifies transactions from logs output from a device; a grouping unit that categorizes the transactions having both the same log related to start and the same log related to end into the same group; a learning unit that creates a learning model that defines the number of occurrences on a log type basis in the transactions of the same group; and an inspection unit that inspects a transaction of an inspection target based on the learning model.

Systems and methods for detecting an anomaly in a power semiconductor device are disclosed. A system includes a server computing device and one or more local components communicatively coupled to the server computing device. Each local component includes sensors positioned adjacent to the power semiconductor device for sensing properties thereof. Each local component receives data corresponding to one or more sensed properties of the power semiconductor device from the sensors and transmits the data to the server computing device. The server computing device utilizes the data, via a machine learning algorithm, to generate a set of eigenvalues and associated eigenvectors and select a selected set of eigenvalues and associated eigenvectors. Each local component conducts a statistical analysis of the selected set of eigenvalues and associated eigenvectors to determine that the data is indicative of the anomaly.

A child/infant safety seat comprising a computing device and at least one sensing unit connected with the computing device; wherein at least one of the at least one sensing unit is configured to detect electromagnetic (EM) and/or magnetic radiation and send measurements to the computing device; and wherein the computing device is configured to process the measurements, calculate the EM and/or the magnetic radiation detected by the at least one of the at least one sensing unit, and determine whether the detected EM and/or magnetic radiation is higher than at least one threshold value.

Techniques are disclosed relating to accessing, by an evaluation computer system, transaction details from a subset of current transactions being processed by a production version of a transaction processing service that is implemented on a production computer system. The evaluation computer system may perform, in real-time, tests on a particular product using the transaction details. The evaluation computer system may then store output from the tests that are performed using the transaction details.