756results about "Reliability/availability analysis" patented technology

Provided are a method, a system, and a storage medium for intelligent diagnosis of a device failure based on an IIoT. The method includes: obtaining device-related data of an abnormal device from a total sensor database; predicting failure warning information of the abnormal device and a confidence level corresponding to the failure warning information based on the device-related data; determining a failure processing parameter based on the failure warning information and the confidence level, and generating a failure processing instruction; and sending the failure processing instruction via the device sensor network platform to a maintenance personnel terminal of the device perception and control platform or to a failure shooting device, and controlling the failure shooting device to perform failure shooting.

Method for prediction of test failures are disclosed. A method may include retrieving, by a test failure prediction computer program, test results for a test; training, by the test failure prediction computer program, a machine learning engine based on one or more hypotheses; receiving, by the test failure prediction computer program, metrics for a code file; providing, by the test failure prediction computer program, the metrics for the code file to the trained machine learning engine; receiving, by the test failure prediction computer program, an output from the trained machine learning engine that provides a probability of failure; and outputting, by the test failure prediction computer program, the output.

Systems and methods for detailed cloud posture remediation recommendations utilizing custom Large Language Models (LLMs). The present systems and methods are configured to perform the steps of scanning a cloud environment for posture control data; generating one or more alerts related to any of risky configurations and risky activities associated with the cloud environment; generating one or more remediation recommendations based on the one or more alerts; and providing the one or more alerts and the one or more remediation recommendations to administrators of the cloud environment.

A method for time series anomaly detection with rare event failure prediction includes determining, by a device including a processor and using a first machine learning model, a location of a data anomaly within sequential data. The method also includes determining, by the device and using a second machine learning model that is not the first machine learning model, whether a probability of a future failure event is at least a threshold probability based on the data anomaly and the sequential data.

A memory state detection method and apparatus, and a communication device and a non-volatile readable storage medium. The method comprises: acquiring memory data; dividing the memory data into first memory data and second memory data on the basis of the type of the memory data; determining a preliminary memory health degree score on the basis of a preset memory health degree evaluation model and the first memory data, and processing the second memory data by means of an input and output model, so as to determine a health degree influence factor, wherein the preset memory health degree evaluation model is determined on the basis of first historical memory data, and the input and output model is generated by means of training a preset initial model on the basis of second historical memory data; and determining a memory state on the basis of the preliminary memory health degree score and the health degree influence factor.

A system for predicting system component failures within a computer system that comprises a plurality of hardware components and a plurality of software components. The system may comprise memory storing instructions that, when executed, cause a processor to: obtain performance metrics by monitoring a network interface of the computer system; generate component failure probabilities by processing the performance metrics; determine that a first component failure probability among the component failure probabilities exceeds a risk threshold; determine remedial actions that mitigate a first component failure probability; and mitigate the first component failure probability by initiating an execution of the remedial actions.

The invention relates to the technical field of fault diagnosis, in particular to a fault self-diagnosis system and method for a high and low temperature aging test chamber of a storage chip and computer equipment. The temperature control abnormal index is obtained through the data of the temperature control system sensor, early warning can be performed when the temperature control system has abnormal fluctuation, the performance evaluation basis of the air circulation system is provided by the air system efficiency coefficient, the heating efficiency and the refrigeration coefficient are obtained through the data of the heating and refrigerating system sensor, and the performance of the heating and refrigerating system is evaluated. An electrical stability index is obtained through data of a test system sensor, the operation stability of an electrical system is evaluated, the stability of the electrical system is detected in time, and a fault probability index is obtained through a system entropy increase rate, the electrical stability index, a thermal coupling factor, a wind system efficiency coefficient and a temperature control abnormal index. Therefore, the stability and the reliability of the equipment can be improved, the maintenance cost can be reduced, the use efficiency of the equipment is improved, and the accuracy and the safety of test results are ensured.

An apparatus for data fault detection and repair is disclosed. The apparatus comprises at least a processor and a memory communicatively connected to the at least a processor. The memory instructs the processor to receive a user profile relating to a user, wherein the user profile comprises at least provider data of a user. The memory instructs the processor to generate practitioner data as a function of the user profile. The memory additionally instructs the processor to retrieve remittance data as a function of the practitioner data. The memory then instructs the processor to identify a data fault in at least one of the practitioner data and the remittance data. The memory instructs the processor to initiate a data correction action based on the identified data fault.

Methods and systems for managing data processing systems are disclosed. A data processing system may include and depend on the operation of hardware and / or software components. Inference models may be implemented to predict future system infrastructure outcomes (e.g., component failures) using information recorded in logs that reflect the operation of the components. However, the models may be complex “black boxes” and may generate critical outcome predictions for downstream consumers without explanations of how the predictions are determined, resulting in downstream consumers having low confidence in the predictions. Therefore, hidden knowledge (e.g., structured knowledge attributes) of the models may be extracted and / or used to understand the underlying processes that the models use to predict the system infrastructure outcomes. The hidden knowledge may be provided for interactively managing data processing system(s) failures in order to increase the likelihood of preventing and / or mitigating future data processing system failures.

The invention discloses an industrial equipment health state analysis method, and relates to the technical field of industrial equipment health monitoring, and the method comprises the steps: obtaining vibration data, lubricating oil pollution image data and real-time motor current data of an automobile factory punching machine; generating a dynamic weight coefficient according to the ratio of the real-time motor current data to the rated current of the equipment; performing weighted summation to calculate a real-time health score; constructing a wear-fatigue coupling model to calculate and output an alarm prediction L value; and when the alarm prediction L value exceeds a set threshold value, generating an equipment maintenance instruction and adjusting equipment operation parameters. According to the method, through multi-physics field coupling modeling, dynamic weight distribution and closed-loop self-optimization mechanisms, the precision and real-time performance problems of equipment health state analysis in an automobile manufacturing scene are solved, the method is particularly suitable for high-load equipment such as a punching machine tool and a welding robot, and compared with a traditional method, the comprehensive operation and maintenance cost is reduced, and the non-planed downtime is shortened.

The invention provides a cluster reliability test method based on fault simulation, which comprises the following steps: acquiring injection parameter information, and performing fault simulation in a cluster based on the injection parameter information and a fault transfer model to obtain a complex fault scene; key performance index records are obtained in real time based on the complex fault scene; executing a reliability detection step based on a preset reliability detection model and the key performance index record, and obtaining a data analysis report; and obtaining a reliability score, an influence analysis result and a risk prediction result based on a data analysis report, a weighted scoring algorithm, an anomaly detection algorithm and linear regression analysis, and completing reliability detection of the cluster. According to the cluster reliability test method and system based on fault simulation provided by the invention, the test efficiency and the reliability detection level are greatly improved, the fault simulation is carried out in the cluster and the reliability detection model is combined, so that the accurate quantification of the reliability detection result is realized, and the reliability detection efficiency and the detection capability are improved.

In various examples, confirming or evaluating availability requirements as part of a safety analysis for autonomous and semi-autonomous systems and applications is described herein. For instance, systems and methods are disclosed that may add availability requirements to one or more safety analysis techniques, such as failure mode and effects analysis (FMEA), critical path analysis (CPA), and / or any other safety analysis technique. In some examples, an availability requirement may indicate whether a failure mode associated with a detected failure will impact an availability of an element and / or one or more measures that may be taken to avoid impacting the availability of the element. Systems and methods may then analyze a machine using these updated safety analysis techniques to detect failure modes in a system, causes of the failure modes, and effects of the failure modes, as well as determine whether these failure modes will impact the availability of the system.

Aspects of the disclosure identify a pattern of features associated with a computing component indicating an increased probability that the component incurs an uncorrectable error. Historical component data, component features, server data, and server services are utilized to identify the patterns that correlate to an increased probability that the component incurs an uncorrectable error. For example, this data is used as input into a machine learning platform. Proactive and / or mitigating actions that reduce the probability of an uncorrectable error or its negative effects are presented and / or implemented to minimize or eliminate disruption in cloud computing services.

The present application relates to the technical field of device detection. Provided are a fault early-warning method and apparatus for a heterogeneous hard disk system. The method comprises: obtaining hard disk state attribute data of hard disks of different model numbers in a heterogeneous hard disk system, and performing cluster grouping on the hard disk state attribute data on the basis of the model numbers of the hard disks and a distribution difference of the hard disk state attribute data, so as to determine a hard disk cluster to which the hard disk state attribute data belongs (101); inputting data corresponding to each hard disk cluster into a hard disk fault prediction model of a multi-tower structure for abnormality detection processing, so as to obtain hard disk health indicator information, wherein the hard disk fault prediction model is obtained by means of performing training on the basis of sample hard disk state attribute data and hard disk health label information corresponding to the sample hard disk state attribute data (102); and performing fault early-warning on the heterogeneous hard disk system on the basis of the hard disk health indicator information (103).

A computerized method for predicting a failure of a component based on environmental conditions is described. Environmental conditions proximate a component in a server are monitored. When the current environmental conditions exceed a threshold level, the current environmental conditions are applied against historical data to predict when the component will fail. Mitigating actions are performed in response to, and prior to, the predicted failure of the component.

A method of monitoring behavior of an AVC system that may include communicatively connecting one or more devices to a server of a cloud platform, creating one or more device digital twins, synchronizing the device digital twins to the devices, creating one or more space digital twins of the physical spaces that the devices are located in, synchronizing the space digital twins to a corresponding physical space, analyzing data associated with the one or more devices to create an output indicative of at least one of the health and reliability of the one or more devices, usage and operational status of the physical space or presenting the output on a computing device.

Some embodiments provide a method for a first data compute node (DCN) operating in a public datacenter. The method receives an encryption rule from a centralized network controller. The method determines that the network encryption rule requires encryption of packets between second and third DCNs operating in the public datacenter. The method requests a first key from a secure key storage. Upon receipt of the first key, the method uses the first key and additional parameters to generate second and third keys. The method distributes the second key to the second DCN and the third key to the third DCN in the public datacenter.

Conventional error detection for integration processes in an integration platform are inefficient and require significant expertise. Accordingly, an error prediction model is disclosed. The error prediction model may be operated to produce error predictions, based on the current design (e.g., lineage) of an integration process, during construction of that integration process (e.g., on a virtual canvas). A generative language model may also be used to provide the error predictions in natural language. This enables the efficient troubleshooting and resolution of errors in an integration process, prior to that integration process being deployed and executed, and without requiring significant expertise.

The invention discloses a communication data verification method and device, electronic equipment and a storage medium, and relates to the technical field of communication, and the method comprises the steps: after executing a write-in operation on a target terminal, immediately initiating a read-in operation, and through the design of writing first and reading second, immediately checking whether the written-in communication data is correctly received and processed after the data is sent, thereby improving the verification efficiency. According to the method and the device, the communication verification format is improved, and the write operation is seamlessly switched to the read operation under the condition of keeping the communication continuity, so that the reliability of data transmission is improved, and the response speed and the performance of the server are optimized. Therefore, the problems that a verification mechanism is incomplete during data transmission, and the reliability and accuracy of data transmission are poor in the prior art are solved.

A maintenance system for maintaining a hardware system is described. The maintenance system includes an acquisition circuit and an analysis circuit. The acquisition circuit can be configured to acquire a plurality of operational parameters of the hardware system, the plurality of operational parameters being relevant to a health state of the hardware system. The acquisition circuit can be configured to forward the plurality of operational parameters to the analysis circuit. The analysis circuit can be configured to determine a device health score for the hardware system, a remaining useful life of the hardware system, and / or a user recommendation for operating the hardware system, each based on the plurality of operational parameters. Further, a method of analyzing and / or creating health information for a hardware system is described.

The disclosure herein describes training and using an uncorrectable error (UE) state prediction model based on telemetry error data. Sets of UE state labels and non-UE state labels are generated from a first set of collected telemetry data, wherein the UE state labels each reference a UE and telemetry data of an interval prior to the referenced UE. Statistical features are extracted from telemetry data of the sets of UE state labels and non-UE state labels, and the extracted statistical features are used to train a UE state prediction model. A second set of collected telemetry data is obtained, and a UE event is predicted based on the second set of collected telemetry data using the trained UE state prediction model. A preventative operation is performed on a memory page of the system based on the predicted UE event, whereby the predicted UE event is prevented from occurring.

Embodiments relate to early detection of information technology (IT) failures in a computing system. A technique is executed by one or more processors and includes receiving multiple IT records including past and recent historical data, extracting first and second time series sections from the past and recent historical data, respectively, training a failure detection model to correlate metric patterns in the first time series sections with at least one of other metric patterns and previous IT failures and, in response to the training, using the failure detection model to predict at least one of upcoming metric patterns and upcoming IT failures from metric patterns in the second time series sections.

An illustrative method includes a storage management system accessing metrics data associated with storage pools of a cluster; performing, based on the metrics data, a predictive failure analysis with respect to the storage pools, the predictive failure analysis indicating a likelihood of failure of each of the storage pools; selecting, based on the predictive failure analysis, a storage pool from the storage pools as an optimal location for a volume; and creating the volume on the storage pool.

The invention discloses a memory fault prediction method and device, a chip, equipment and a storage medium, and belongs to the technical field of computers. The method comprises the steps that error position distribution of an error correction unit associated with a memory is obtained, the memory comprises at least one memory particle, the error correction unit comprises a plurality of data bits obtained from a plurality of storage units of the memory through continuous multiple burst reading, and each data bit corresponds to one storage unit in the memory; the error position distribution is used for indicating the position of an error data bit in a plurality of data bits, and the error position distribution causes the occurrence of CE in the memory; a memory fault model is obtained, the memory fault model comprises at least one reference position distribution, and the reference position distribution is associated with the UCE of the memory; according to the similarity between the error position distribution and the at least one reference position distribution, a prediction result is output, and the prediction result is used for indicating the probability that the UCE occurs in the memory. The method is beneficial to improving the timeliness of memory fault prediction.

The invention discloses an intelligent fault prediction system for a solid state disk, relates to the technical field of solid state disks, solves the problems that only the test condition of a single chip can be identified, and the test effect of comprehensive radiation cannot be achieved, and is based on the number of threads corresponding to the solid state disk, and different processing processes are executed. The method comprises the following steps: processing the solid state disk in different combination states, confirming numerical value characteristics associated with different processing processes, analyzing reading speed fluctuation states of the solid state disk in different combination states according to the confirmed numerical value characteristics, carrying out multidirectional characteristic testing according to a specific analysis process, and identifying the use performance of the solid state disk. According to the multi-azimuth and multi-chip testing method, the multi-azimuth and multi-chip testing method and the multi-azimuth and multi-chip testing system, the characteristic data associated in the use performance is subjected to interval range confirmation, whether the fault hidden danger exists or not is confirmed according to the change amplitude of the corresponding interval range, the comprehensiveness in the fault hidden danger testing process can be effectively guaranteed by adopting the multi-azimuth and multi-chip testing mode, and a better testing processing effect is achieved.

The system obtains performance signals associated with respective hard disks of a volume of hard disks including a plurality of hard disks that are dedicated to activities of a service. The system determines a volume failure prediction for the volume of hard disks by, for each respective hard disk of the volume of hard disks, determining a hard disk failure prediction. The system determines a hard disk failure prediction by: inputting the respective performance signals into a supervised machine learning model; and receiving as output from the machine learning model the hard disk failure prediction for the respective hard disk. The system based on the received outputs, determines that the volume failure prediction is associated with a migration condition. The system, responsive to determining that the volume failure prediction is associated with the migration condition, migrates data from the volume of hard disks to a second volume of hard disks.

A method for operating a storage device capable of improving reliability of a memory system is provided. The method includes providing a storage device which includes a first component and a second component; receiving, via a host interface of the storage device, a command for requesting failure possibility information about the storage device from an external device; and providing, via the host interface, the failure possibility information about the storage device to the external device in response to the command.

Embodiments of the present disclosure provide a context based health visualization system and method for a computing environment that indicates context-based health information for the computing resources of a computing environment. According to one embodiment, an Information Handling System (IHS) includes computer-executable instructions to identify a maintenance task that needs to be performed on a computing resource, classify the maintenance task according to one of a plurality of health contexts, and generate, using information associated with the maintenance task, a health context score for the one health context. The maintenance task may be one that impacts an overall health score of the computing resource. The instructions may then cause the IHS to display the health context score for view by a user.

The invention relates to the technical field of server aging testing, in particular to a server aging testing method and device, electronic equipment and a storage medium. Then convolution processing is carried out on each aging data queue in a sliding mode through a difference operator, the obtained multiple first convolution queues are segmented one by one, queue segments obtained through segmentation are normalized, and multiple queue segments are obtained; performing dot product calculation on each queue segment, each same-time-segment queue segment and each same-part queue segment to obtain a plurality of dot product results, and adding representative values determined according to the plurality of dot product results into a test analysis matrix; and finally, according to the plurality of row vectors of the test analysis matrix, determining the abnormal probability of the plurality of tested parts of the server. According to the method, the state of the detected component is determined based on the cross point product and probability estimation, so that the problem component can be found early.

The present invention extends to methods, systems, and computer program products for predictively addressing hardware component failures. Network packets can be received over time at a platform. Metrics derived from platform hardware components and derived from one or more workloads utilizing the platform hardware components can be monitored. Model training data can be formulated from the metrics. A health check model can be trained using the model training data. The health check model can be executed to compute a probability that a monitored platform hardware component is on a path to failure. It can be determined that the probability exceeds a threshold. A workload can be relocated from a pod containing the monitored platform hardware component to another pod. Additional network packets can be received over time at the platform. The workload can process data contained in the additional network packets at the other pod.