Disclosed is a system comprising a memory component having a plurality of memory cells capable of being in a plurality of states, each state of the plurality of states corresponding to a value stored by the memory cell, and a processing device, operatively coupled with the memory component, to perform operations comprising: obtaining, for the plurality of memory cells, a plurality of distributions of threshold voltages, wherein each of the plurality of distributions corresponds to one of the plurality of states, classifying each of the plurality of distributions among one of a plurality of classes, generating a vector comprising a plurality of components, wherein each of the plurality of components represents the class of a respective one of the plurality of distributions, and processing, using a classifier, the generated vector to determine a likelihood that the memory component will fail within a target period of time.

A fault indicator diagnostic system and fault indicator diagnostic method, with which a fault indicator of a machine can be more accurately diagnosed, has an operation sensor data table which indicates an association between sensor data and an acquisition time of the sensor data. An operation mode data table indicates an association between an operation mode and a time of operation in the operation mode. An operation data table is created by merge processing the operation sensor data table and the operation mode data table comprising the sensor data with regard to the operation mode at a given time. The system compares, in a given operation mode, a threshold determined on the basis of a diagnostic model created by learning from normal sensor data with a value computed on the basis of the diagnostic model from the sensor data to be diagnosed, and determines whether a malfunction is occurring.

The subject disclosure relates to employing grouping and selection components to facilitate a grouping of failure data associated with oil and gas exploration equipment into one or more equipment failure type groups. In an example, a method comprises grouping, by a system operatively coupled to a processor, training data of a set of equipment failure data into one or more failure type groups based on one or more determined failure criteria, wherein the one or more failure type groups represent equipment failure classifications associated with energy exploration processes; and selecting, by the system, first ungrouped data from the set of equipment failure data based on a level of similarity between the first ungrouped data and the training data.

In accordance with embodiments of the present disclosure, an information handling system may include a processor and a non-transitory computer-readable medium having stored thereon a program of instructions executable by the processor. The program of instructions may be configured to, when read and executed by the processor, receive telemetry data associated with one or more information handling resources, receive failure statistics associated with the one or more information handling resources, and correlate the telemetry data and the failure statistics to create training data for a pattern recognition engine configured to predict a failure status of an information handling resource from operational data associated with the information handling resource.

A transaction terminals reports information regarding operation of terminal to a server-based analyzer. The analyzer labels the information and normalizes the labeled information into a model format. The analyzer reports the model format to a problem identifier/resolver. The problem identifier/resolver identifies a closest likely problem and a resolution for that closest likely problem based on the labeled information in the model format and reports the likely problem and resolution back to the analyzer for resolution on the transaction terminal.

A system, method, and apparatus for detecting errors in an artificial intelligence engine. The method includes processing a medical image of a patient at an artificial intelligence engine, and producing a first test result at the artificial intelligence engine based on the medical image. The method also includes detecting an error in the first test result using a server emulator, and producing a second test result that corrects the error in the first test result. In addition, the method includes transmitting the second test result from the artificial intelligence engine to a picture archiving and communication systems server.

A method for automatic error diagnosis in a test environment is provided. The method comprises the step of providing a plurality of test logs associated with known types of failures, each comprising a set of files. The method further comprises the step of arranging the plurality of test logs in a defect database. Moreover, the method comprises the step of transforming the set of files of the plurality of test logs into vectors adapted to be fed into a machine learning model.

A method includes detecting an error that has occurred in one or more assets of an enterprise system and generating a visual error signature of the detected error, the visual error signature comprising at least a portion of a graph-based visualization of operation of the assets. The method also includes providing the generated visual error signature for the detected error as input to a machine learning model and utilizing the machine learning model to classify the visual error signature for the detected error as belonging to at least a given one of a plurality of error classes, the machine learning model being trained using historical visual error signatures for previously-detected errors. The method further includes identifying at least one action taken to remediate each of one or more previously-detected errors of the given error class and remediating the detected error utilizing one or more of the identified actions.

A flight deck system for an aircraft includes a display device for providing a graphical interface for displaying flight-related information including a plurality of windows to an operator. The display device is configured for displaying the plurality of windows within a plurality of regions. The plurality of regions can each have a predefined shape and orientation on the display screen according to a regular grid layout. A touch interface is coordinated with the display device for receiving touch information from the operator and allowing the operator to interact with the graphical interface. A processor is communicatively coupled with the touch interface device and operatively coupled with the display device. The processor can be configured to dynamically recreate a selected window of flight-related information within one or more of the plurality of regions corresponding to an operator-selected icon. In such embodiments, the operator can operate the graphical interface through direct touch.

A system and a method for assisting with troubleshooting a complex system is disclosed in which the troubleshooting procedure can be modeled by a Markov decision process. Combining the fault tree technique with a Markov decision process, in order to determine in an optimal manner the sequence of troubleshooting actions will quickly address the consequences of a failure and ensure maintainability of the complex system.