A model generation apparatus (2000) acquires component failure data in which a usage status is associated with a failure record of a component. The model generation apparatus (2000) generates, for each of a plurality of component groups, a prediction model for predicting the number of failures of each component included in the component group by using the component failure data relating to the component belonging to the component group. The prediction model computes a prediction value of the total number of failures of the components belonging to a corresponding component group from the usage status, and computes a prediction value of the number of failures of each component belonging to the component group from the computed prediction value of the total number of failures.

Systems and methods for determining a source of anomaly in a cyber-physical system (CPS). A forecasting tool can obtain a plurality of CPS feature values during an input window and forecast the plurality of CPS feature values for a forecast window. An anomaly identification tool can determine a total forecast error for the plurality of CPS features in the forecast window, identify an anomaly in the cyber-physical system when the total forecast error exceeds a total error threshold, and identify at least one CPS feature as the source of the anomaly.

Techniques for determining reliability of a workload migration activity are disclosed. In one embodiment, sub-tasks associated with the workload migration activity may be determined. Further, statistical data associated with an execution of the sub-tasks corresponding to different instances of the workload migration activity may be retrieved. Furthermore, a reliability model may be trained through machine learning using the statistical data to determine reliability of the workload migration activity. Then, the reliability of a new workload migration activity may be determined using the trained reliability model.

Techniques for predicting failure mode specific reliability characteristics of tangible equipment using parametric probability models are disclosed. In some example embodiments, a computer system receives a model training configuration entered via a user interface, trains a failure curve model for a selected failure mode of a selected equipment model based on the model training configuration at a time indicated by training schedule data, and generates analytical data for the selected failure mode of the selected equipment model using the trained failure curve model. The failure mode corresponds to a specific way in which the equipment model is capable of failing. In some example embodiments, the training of the failure curve model comprises determining a shape parameter and a scale parameter for the failure curve model based on a fitting of failure event data to a continuous probability distribution, and storing the parameters for use in generating the analytical data.

A memory system includes a memory device and a memory controller. The memory device includes a plurality of memory cells. The memory controller is configured to manage the memory device using a cell level assignment with respect to a plurality of memory cell levels, determine a cell count for each of the cell levels associated with original data of the memory device that is to be accessed, predict an error rate from the cell counts, and selectively adjust the cell level assignment based on the error rate.

Methods, apparatuses, or computer program products provide for providing a component score interface within a multi-component system. A component metadata vector associated with a first component identifier may be retrieved from a component metadata repository. One or more component metadata values are extracted from the component metadata vector based at least in part on a first component score type. A first component score may be generated based at least in part on the one or more component metadata values extracted from the component metadata vector. Additionally, the first component score generation may comprise applying a unique weight value to each component metadata value in accordance with the first component score type. A component score interface comprising instructions for rendering a first component score interface element representing the first component score may be generated. Furthermore, the component score interface may be transmitted to a first client device.

A control unit is configured to determine success or failure of a rewrite process after end of the rewrite process, and write success or failure display data into a success or failure determination result storage area in accordance with the success or failure, and read the success or failure display data present in the success or failure determination result storage area after startup of an electronic device, and execute a control program, in lieu of carrying out the rewrite process, when it is determined that the success or failure display data indicate a success of the rewrite process.

A material failure forecasting system accesses historical failure data to forecasts future failures. The failure data of a material is analyzed using text processing techniques to identify failures and suspensions. The text processing techniques provide for identifying failures when fault words are associated with negations. A fault ontology establishes different failure modes that include primary, secondary and tertiary levels which enable identifying a sequence of failures. The failures thus identified are fitted to a data distribution selected from a plurality of data distributions. The parameters from the data distribution are used for simulating a demand profile for the material which considers interchangeability. Similarly failure data of the materials in an equipment can be analyzed and the reliability of the equipment can be estimated.

An apparatus and a method for optimizing a satellite system considering a hard error stability and a soft error stability are disclosed. The satellite system optimizing method which considers a hard error stability and a soft error stability according to an exemplary embodiment of the present disclosure includes acquiring hardware information of a processor which is loaded in the satellite system; acquiring workload information including a task which is performed by the processor; establishing a scheduling policy for the task based on the hardware information and the workload information; and quantifying a soft error stability and a hard error stability in accordance with the scheduling policy.

Performing predictive analysis on running batch jobs is provided. A series of batch end time predictive models is retrieved according to a sequence of milestone jobs in a batch of jobs. Retrieved batch end time predictive models are assembled into an aggregate batch end time predictive model to increase accuracy and stability of an end time prediction of the batch of jobs. The aggregate batch end time predictive model is utilized to predict an end time of the batch of jobs during running of the batch of jobs to form a predicted end time of the batch of jobs.