A computer for a manufacturer includes: a storage device storing amount-of-change determination values specified for respective amount-of-change indexes indicative of tendencies of temporal changes in pieces of sensor data A, B, and C, about a plurality of oil properties including a viscosity, a density, and a dielectric constant of oil; an abnormality determining section determining abnormality of the oil on the basis of the pieces of sensor data A, B, and C about the plurality of oil properties and abnormality determination values SAh, SAl, SBh, and SCh prescribed for the respective pieces of sensor data A, B, and C about the plurality of oil properties; a cause identifying section identifying, when the abnormality determining section determines the oil to be abnormal, the cause of the abnormality on the basis of the type of the oil property determined to be abnormal and the amount-of-change determination value of the oil property.

A method for detecting anomalies in a piece of wellsite equipment. The method may include measuring data related to the piece of wellsite equipment. The method may also include encoding the measured data with a first autoencoder to produce a first set of encoded data. The method may further include performing a first Gaussian process regression (“GPR”) on the first set of encoded data to produce a first set of results that identifies a first anomaly in the measured data and that provides a first confidence interval for the first anomaly.

A method for determining an anomalous operating state in a charging infrastructure system for batteries is proposed. For a charging process at a charging station, the method includes obtaining target characteristics of the charging process, determining process parameters for the charging process, performing the charging process, determining a performance metric for the performed charging process, generating and storing a data set for the performed charging process in a database. For multiple charging processes, the method includes calculating and storing at least one first set of statistic data for a first time interval and at least one second set of statistic data for a second time interval, comparing the first set of statistic data with the second set of statistic data to compute a set of difference values for each stored data set, and determining whether the charging infrastructure system operates in an anomalous operating state.

In one embodiment, a method performed by a microcontroller of an electronic device includes accessing one or more real-time sensor data associated with one or more sensors of the electronic device, determining, by a machine-learning model running on the microcontroller, that an anomalous event has occurred on the electronic device by processing the one or more real-time sensor data with the machine-learning model, and sending, upon the determination that the anomalous event has occurred, a notification regarding the anomalous event to an application running on the electronic device.

Providing application-specific storage by a cloud-based storage system, including: identifying, for an application that utilizes resources within the cloud-based storage system, one or more characteristics associated with the application; and selecting, in dependence upon the one or more characteristics associated with the application and characteristics of resources within the cloud-based storage system, one or more resources within the cloud-based storage system to support the execution of the application, wherein at least a portion of a dataset associated with the application is stored as blocks within block storage resources in the cloud-based storage system and also stored as objects within object storage resources in the cloud-based storage system.

Disclosed herein is a method for making embryonic bio-inspired hardware efficient against faults through self-healing, fault prediction, and fault-prediction assisted self-healing. The disclosed self-healing recovers a faulty embryonic cell through innovative usage of healthy cells. Through experimentations, it is observed that self-healing is effective, but it takes a considerable amount of time for the hardware to recover from a fault that occurs suddenly without forewarning. To get over this problem of delay, novel deep learning-based formulations are utilized for fault predictions. The self-healing technique is then deployed along with the disclosed fault prediction methods to gauge the accuracy and delay of embryonic hardware.

A treatment method for an OOC action during a semiconductor production process includes: multiple Out Of Control Action Plan IDs (OCAPID) respectively corresponding to multiple semiconductor production process steps and multiple identified contents in one-to-one correspondence with the multiple OCAPIDs are established, and an OOC action checklist including multiple OOC action check items according to the identified contents is established; it is determined whether the OOC action occurs to a wafer subjected to the current semiconductor production process step, and if the OCC action occurs to the wafer, the current OCAPID corresponding to the current semiconductor production process step is automatically obtained, and the wafer is inspected according to the current identified content corresponding to the current OCAPID.

A method and a system for detecting anomalies in a water distribution system comprises a network of nodes, and is equipped with sensors of at least water velocity at a subset of the nodes. The water distribution system is modeled by a hydraulic model. The method comprises parametrizing the hydraulic model with initial values of a set of control variables, using the sensors to obtain values of state variables of the network at the nodes, using the hydraulic model to calculate predicted values of state variables, recursively calculating the values of control variables which, applied to the hydraulic model, permit to obtain the predicted values of state variables the closest to the observed values, and classifying nodes of the network based on the values of control variables.

A management device for managing a component mounter that mounts components onto a substrate using some of constituent elements each selected from one of constituent element groups each including the constituent elements. One group is a nozzle group including nozzles. The device includes: a true displacement amount calculator that calculates, for each target component which is the component, a true amount of displacement which is a sum of (i) an amount of pickup displacement being an amount of displacement between the target component and a target nozzle being one of the nozzles and picks up the target component and (ii) an amount of correction being an amount of offset of a position of the target nozzle when picking up the target component; and a statistical processor that performs parameter estimation for a predetermined statistical model using the true amount to calculate a degree of malfunction of each constituent element.

A monitoring system that monitors a steam-using facility includes a temperature sensor that is a trap temperature sensor configured to detect a temperature of a steam trap provided in a steam discharge unit and/or a steam temperature sensor configured to detect a temperature of steam flowing into the steam trap and a pressure sensor configured to detect a pressure of steam flowing into the steam trap. The monitoring system determines that there is an occurrence of an abnormality or a sign of the abnormality in the steam trap when (i) a temperature detection value obtained by the temperature sensor and/or statistical temperature data obtained by performing statistical processing on the temperature detection value deviates from a predetermined criterion thereof and (ii) a pressure detection value obtained by the pressure sensor and/or statistical pressure data obtained by performing statistical processing on the pressure detection value deviates from a predetermined criterion thereof.