A system for condition monitoring and fault diagnosis includes a data collection function that acquires time histories of selected variables for one or more of the components, a pre-processing function that calculates specified characteristics of the time histories, an analysis function for evaluating the characteristics to produce one or more hypotheses of a condition of the one or more components, and a reasoning function for determining the condition of the one or more components from the one or more hypotheses.

A system for automated and intelligent error correction within an electronic blockchain ledger is provided. The system may analyze unformatted/unstructured blockchain event logs using machine learning algorithms in order to identify and label the errors within the event logs. Based on the identified errors, the system may use predictive analysis in conjunction with error or rule repositories and/or machine learning to identify potential solutions to the identified errors. Once the potential solutions have been identified, the system may automatically attempt to rectify the blockchain transaction errors using the potential solutions. The system may further comprise trend/correlation analyses and reporting functions regarding various metrics and may output said metrics in various accessible formats.

There is provided an anomaly diagnosis method performed by an anomaly diagnosis apparatus that diagnosis to determine whether an observed value composed of values of variables representing a state of a monitoring target obtained by observing the monitoring target is anomalous. The anomaly diagnosis apparatus includes a processor and a memory. The memory stores an anomaly detection model generated by learning using observed values. The processor acquires group information indicating one or more groups each constituted by a combination of at least two mutually-related variables, acquires the observed value, determines whether the observed value is anomalous by employing the anomaly detection model read from the memory, and in a case where the observed value is determined to be anomalous, identifies a group causing an anomaly among the one or more groups in the observed value.

A method for establishing fault diagnosis technique based on contingent Bayesian networks, comprising steps of: step (1) determining a domain of an unknown object to be reasoned; step (2) defining a model structure by adopting a first-order logic language; step (3) generating a Blog model; step (4) transforming the Blog model into the contingent Bayesian networks; step (5) defining the contingent Bayesian networks; step (6) learning parameters of the contingent Bayesian networks; and step (7) reasoning a fault of the contingent Bayesian networks by utilizing a Markov chain Monte Carlo Method. By the steps mentioned above, establishing fault diagnosis technique based on contingent Bayesian networks is achieved.

Detecting data anomalies by receiving a first data set related to a first variable metric, determining data anomaly detection scores for data points of the first data set according to a plurality of data anomaly detection techniques, generating an adaptive ground-truth window according to the data anomaly detection scores, assigning a weighting value to each data point within the adaptive ground-truth window, training a machine learning system using the set of data anomaly detection scores and weighting values, and providing a trained machine learning system for evaluating a second data set.

A plurality of potential fault scenarios are accessed, wherein a given potential fault scenario of the plurality of potential fault scenarios has at least one corresponding root cause, and a representation of the given potential fault scenario comprises a don't care value. An actual fault scenario from telemetry received from a monitored system is generated. The actual fault scenario is matched against the plurality of potential fault scenarios. One or more matched causes are output as one or more probable root cause failures of the monitored system.

Embodiments for preemptive deep diagnostics of resources in a disaggregated computing environment. Responsive to detecting a threshold breach of a recurrent event associated with a first resource of a first resource type executing a workload, an alert is generated; and responsive to receiving the alert, the execution of the workload on the first resource is ceased. Health check diagnostics are identified and invoked on the first resource based on the alert and a server telemetry. Results of the health check diagnostics are mapped to a set of learned failure patterns; and a potential failure of the first resource is predicted based on the mapping.

A design support system includes memory, a receiving unit, and an associating unit. The memory stores information on design element classification that classifies a design element included in a product, and information on design requirement classification that classifies a design requirement required for the product. The receiving unit receives technical information regarding a design trouble. The associating unit refers to technical information regarding a design trouble, received by the receiving unit, and associates a classification item in the design requirement classification to which the design trouble belongs and a classification item in the design element classification to which a design element causing the design trouble belongs with each other, along with information on a phenomenon indicating a failure status of the design element included in the technical information.

A method for detecting repairable boards requiring repair amongst many boards which may or may not require repair applies a board detection model based on training features of many sample repairable boards. The method obtains repair-relevant information of all the sample repairable boards, extracts predetermined features from the repair-relevant information, and analyzes the predetermined features to obtain the training features. The board detection model is established and trained based on the training features, and receives repair-relevant information of each repairable board to obtain a result of detection repairable board according to the board detection model. A device for detecting repairable boards is also provided.

A method for locating and isolating a failed node of an electromechanical management bus in a communication device. The method includes, in a communication process, an SHMC in operation records communication states of electromechanical management buses; the SHMC in operation performs calculation and analysis operations on data associated with the communication states, and determines whether there is an irrecoverable communication abnormality in a corresponding bus; if so, the SHMC sends, by means of a normal electromechanical management bus, a command to an electromechanical management node subordinate to the abnormal electromechanical management bus, such that the electromechanical management node controls a corresponding mechanical switch of the bus, coordinates respective nodes of the abnormal electromechanical management bus to conduct mutual communication tests with each other, locates a failed node, and returns location information of the failed node.