An apparatus is disclosed for making circuitry with passive fundamental components more robust. In example implementations, an apparatus includes at least one passive fundamental component and at least one redundant passive fundamental component. The apparatus also includes fault tolerant circuitry coupled to the at least one passive fundamental component and the at least one redundant passive fundamental component. The fault tolerant circuitry includes fault detection circuitry configured to detect a fault of the at least one passive fundamental component. The fault tolerant circuitry also includes component repair circuitry configured to disconnect the at least one passive fundamental component based on the fault.

In general, embodiments of the invention relate to a method for enabling enhanced logging. The method includes obtaining historical data for a target entity, determining a steady state error probability of the target entity using the historical data, and enabling, based on the steady state error probability, a first level of enhanced logging on the target entity.

Methods, devices, and systems related to memory device on-die ECC data are described. In an example, a scrub operation can be performed on data in order to determine which rows of memory cells in an array include a particular number of errors. The particular number of errors can be a number of errors that exceed a threshold number of errors. An address of the determined rows with the particular number of errors can be stored in memory cells of the array for later access. The address of the determined rows can be accessed to perform a user-initiated repair operation, a self-repair operation, a refresh operation, and/or to alter timing of access of the cells or alter voltage of the cells.

Automated recovery of execution roles in a distributed historian system in accordance with actions and rules customized to each execution role. A monitoring service monitors the health status of execution roles and automatically performs a corrective action in response to the health state of an execution role triggering a predetermined rule.

An anomaly location identification device includes a determination unit configured to determine presence or absence of an anomaly by inputting part or all of information items output from a plurality of devices into an anomaly detection algorithm; a calculation unit configured to calculate, in response to a determination made by the determination unit that an anomaly is present, with respect to one of the information items, an index indicating a degree of contribution to the anomaly; and an identification unit configured to perform calculation by an analysis algorithm using a causal model receiving the index as input, to identify an anomalous device, to improve the precision and calculation speed related to identification of an anomaly location.

A fault prediction system based on sensor data on a numerical control machine tool and a method therefor. The fault prediction system includes a plurality of sensors for collecting numerical control machine tool operation state data serving as multi-channel data, wherein an output end of a sensor is connected to an input end of a multi-channel sensor interface circuit, and an output end of the multi-channel sensor interface circuit is connected to a controller. The plurality of sensors can be multi-path temperature sensors, multi-path vibration sensors or multi-path noise sensors. The defects in the prior art of there being no model for researching a cross correlation of multi-channel data, collected by a plurality of sensors, of an operation state of a numerical control machine tool, and a device fault subspace of the multi-channel data being unable to be obtained are effectively prevented.

A method for detecting abnormal transactional behavior in a financial account is provided. The method includes: accessing first information that includes a textual description of a first transaction of a plurality of transactions associated with a first account; analyzing the text by applying tags thereto; assigning, based on a result of the analyzing, the first transaction to a respective cluster of the plurality of transactions; and designating each respective cluster as corresponding to one from among a normal transactional behavior group, an abnormal transactional behavior group, and an anomalous transactional behavior group. When a proportion of abnormal and anomalous transactions exceeds a threshold, the account may be flagged for further investigation.

Systems and/or techniques for facilitating online-monitoring of machine learning models are provided. In various embodiments, a system can receive monitoring settings associated with a machine learning model to be monitored. In various cases, the monitoring settings can identify a first set of data features that are generated as output by the machine learning model. In various cases, the monitoring settings can identify a second set of data features that are received as input by the machine learning model. In various aspects, the system can compute a first set of statistical metrics based on the first set of data features. In various cases, the first set of statistical metrics can characterize a performance quality of the machine learning model. In various instances, the system can compute a second set of statistical metrics based on the second set of data features. In various cases, the second set of statistical metrics can characterize trends or distributions of input data associated with the machine learning model. In various aspects, the system can store the first set of statistical metrics and the second set of statistical metrics in a data warehouse that is accessible to an operator. In various embodiments, the system can render the first set of statistical metrics and the second set of statistical metrics on an electronic interface, such that the first set of statistical metrics and the second set of statistical metrics are viewable to the operator.

A data processing apparatus includes a first processing unit that executes real-time processing with respect to data, a second processing unit that executes batch processing with respect to data that is output from the first processing unit as a result of processing by the first processing unit, and a monitor that monitors a status of the processing by the first processing unit and a status of processing by the second processing unit. The first processing unit includes a plurality of subprocessing units and buffers, and the second processing unit also includes a plurality of subprocessing units and buffers. The second processing unit includes a storage. The monitor includes a first monitor that monitors, for each of the buffers included in the first processing unit, an amount of the data stored in the corresponding buffer and a second monitor that monitors a total amount of the data stored in the buffers included in the second processing unit and the data stored in the storage.

Methods, systems, and devices for persistent health monitoring for volatile memory devices are described. A memory device may determine that an operating condition associated with an array of memory cells on the device, such as a temperature, current, voltage, or other metric of health status is outside of a range associated with a risk of device degradation. The memory device may monitor a duration over which the operating condition is outside of the range, and may determine whether the duration satisfies a threshold. In some cases, the memory device may store an indication of when (e.g., each time) the duration satisfied the threshold. The memory device may store the one or more indications in one or more non-volatile storage elements, such as fuses, which may enable the memory device to maintain a persistent indication of a cumulative duration over which the memory device is operated with operating conditions outside of the range.