An arrangement for automated testing of mobile devices comprising a learning arrangement for learning how to use test devices that do not match with an earlier already defined test case pattern. In the arrangement the learning arrangement generates instructions for performing a set of tasks. The tasks are then executed in the mobile device being tested. The mobile device provides feedback in form of error/success messages, screenshots, source code, return values and similar. Based on the feedback and earlier accumulated information the learning entity can generate a new set of instructions in order to execute the set of tasks successfully.

Techniques are described herein for predicting disk drive failure using a machine learning model. The framework involves receiving disk drive sensor attributes as training data, preprocessing the training data to select a set of enhanced feature sequences, and using the enhanced feature sequences to train a machine learning model to predict disk drive failures from disk drive sensor monitoring data. Prior to the training phase, the RNN LSTM model is tuned using a set of predefined hyper-parameters. The preprocessing, which is performed during the training and evaluation phase as well as later during the prediction phase, involves using predefined values for a set of parameters to generate the set of enhanced sequences from raw sensor reading. The enhanced feature sequences are generated to maintain a desired healthy/failed disk ratio, and only use samples leading up to a last-valid-time sample in order to honor a pre-specified heads-up-period alert requirement.

This disclosure describes automatically collecting, analyzing, and remediating operational issues with respect to systems executing within a network. For example, a service provider network may include a monitoring service may generate notifications related to operational issues upon detection of operational issues within a system executing within the service provider network. The monitoring service may provide one or more notifications related to an aggregation service that may aggregate the one or more notifications into a standardized format. Contextual information related to the operational issues may be automatically gathered by an analytics service, which may analyze the contextual information to determine a potential cause of the operational issues. Based on the potential cause, a remediation service may automatically remediate the operational issues.

Estimating Remaining Useful Life (RUL) from multi-sensor time series data is difficult through manual inspection. Current machine learning and data analytics methods, for RUL estimation require large number of failed instances for training, which are rarely available in practice, and these methods cannot use information from currently operational censored instances since their failure time is unknown. Embodiments of the present disclosure provide systems and methods for estimating RUL using time series data by implementing an LSTM-RNN based ordinal regression technique, wherein during training RUL value of failed instance(s) is encoded into a vector which is given as a target to the model. Unlike a failed instance, the exact RUL for a censored instance is unknown. For using the censored instances, target vectors are generated and the objective function is modified for training wherein the trained LSTM-RNN based ordinal regression is applied on an input test time series for RUL estimation.

An electronic evaluation device and method thereof for optimizing an operation of computer-controlled metric appliances in a network. The method includes determining whether a fault associated with computer-controlled metric appliance is valid based on a feedback received in real time from a validation entity and updating pre-defined programmable instructions assigned to the computer-controlled metric appliance in response to determining that the fault is invalid. The predefined programmable instructions are used to determine whether the computer-executable metric is achieved or not. The method includes applying a machine learning model on the plurality of parameters and the computer-executable goal to determine a computer-executable task list to be assigned to the computer-controlled metric appliance in order to achieve the computer-executable goal.

Provided is a computer capable of reducing a diagnosis load. For each predetermined diagnosis target node among a plurality of nodes in a neural network, a determination processing unit calculates an expected output value expected as a calculation result of a node calculation process corresponding to the predetermined diagnosis target node, which is obtained when the node calculation process is executed using a predetermined input value. For each diagnosis target node, a generation processing unit generates as a diagnosis program a program for comparing the calculation result of the node calculation process corresponding to the diagnosis target node, which is obtained when the node calculation process is executed by an NN calculation processor using the input value, with the expected output value.

A system, method, and computer program product for identifying location-specific faults are provided. Some embodiments may include receiving first device status data associated with a first computing device and the first device status data may comprise first location-indicative data indicative of a location. Some embodiments may include comparing the first device status data with second device status data associated with one or more second computing devices and the second device status data may comprise second location-indicative data indicative of the location. In some embodiments, based on the comparison of the first device status data and the second device status data, determining that the first computing device is affected by one or more of a device-specific fault or a location-specific fault. Some embodiments may include causing information regarding the device-specific fault or the location-specific fault to be displayed via a graphical user interface.

The invention relates to a method for diagnosing a fieldbus type network. The method comprises the steps of measuring, using a signal measuring device such as an oscilloscope, a bus signal of the fieldbus type network, providing the measured bus signal to a computer system, and generating, by the computer system, a diagnosis. The diagnosis is performed by executing a step of comparing, by the computer system, the measured bus signal with signals in a database of bus signals and corresponding diagnoses; and/or feeding, by the computer system, the measured bus signal to a trained statistical model trained to diagnose the fieldbus type network; as well as a step of outputting the diagnosis based on the output of the comparison and/or the output of the statistical model.

A device trains a classification model with defect classifier training data to generate a trained classification model and processes information indicating priorities and rework efforts for defects, with a Pareto analysis model, to select a set of classes for the defects. The device calculates defect scores for the set of the classes and selects a particular class, from the set of the classes, based on the defect scores. The device processes a historical data set for the particular class to identify a root cause corrective action (RCCA) recommendation and processes information indicating a defect associated with the particular class, with the trained classification model, to generate a predicted RCCA recommendation for the defect. The device processes the predicted RCCA recommendation and the RCCA recommendation, with a linear regression model, to determine an effectiveness score for the predicted RCCA recommendation and retrains the classification model based on the effectiveness score.

Disclosed here is a system to automatically predict and reduce hardware related outages. The system can obtain a performance indicator associated with a wireless telecommunication network including a system performance indicator or an application log, along with a machine learning model trained to predict and resolve a hardware error based on the performance indicator. The machine learning model can detect an anomaly associated with the performance indicator by detecting an infrequent occurrence in the performance indicator. The machine learning model can determine whether the anomaly is similar to a prior anomaly indicating a prior hardware error. Upon determining that the anomaly is similar to the prior hardware error, the machine learning model can predict an occurrence of the hardware error.