There is described a system and method for managing control performance of a field device receiving variable data. Variable and setpoint references corresponding to a control loop of the field device are identified. A time delay normal period based on expected oscillations of the variable reference and first settling thresholds associated with the setpoint reference are also identified. An offnormal timestamp is generated based on the variable reference relative to one or more second settling thresholds associated with the setpoint reference. A normal timestamp is generated based on the variable reference relative to the first settling thresholds. A settling time of the control performance is determined based on the normal timestamp, the offnormal timestamp, and the time delay normal period. One or more performance features of the field device are modified based on the determined settling time.

A method includes obtaining sensor data captured by a sensor of an aircraft during a power up event. The sensor data includes multiple parameter values, each corresponding to a sample period. The method further includes determining a set of delta values, each indicating a difference between parameter values for consecutive sample periods of the sensor data. The method further includes determining a set of quantized delta values by assigning the delta values to quantization bins based on magnitudes of the delta values. The method further includes determining a normalized count of delta values for each quantization bin. The method further includes comparing the normalized counts of delta values to anomaly detection thresholds. The method further includes generating, based on the comparisons, output indicating whether the sensor data is indicative of an operational anomaly.

The present disclosure relates to computer-implemented methods, software, and systems for predicting failure event occurrence for a machine asset. Run-to-failure sequences of time series data that include an occurrence of a failure event for the machine asset are received. One or more candidate cut-off values are determined based on iterative evaluation of a plurality of potential cut-off points. A candidate cut-off value is identified as substantially corresponding to a local peak point for calculated distances between relative frequency distributions of positive and negative sub-sequences. A failure prediction model is iteratively trained to iteratively extract sets of relevant features to determine a prediction horizon for an occurrence of the failure event for the machine asset. A candidate cut-off value associated with a model of highest quality from a set of failure prediction models determined during the iterations is selected to determine the prediction horizon for the machine asset.

There is provided an equipment element maintenance analysis system including: a history information acquirer that is attached with at least one equipment element and acquires, at a predetermined timing, operation history information on a piece of manufacturing equipment for manufacturing a product; an error rate calculator that calculates an error rate based on the number of errors related to each of the at least one equipment element, included in the operation history information; a maintenance determiner that determines maintenance necessity of each of the at least one equipment element; and a notifier that notifies an information item on an equipment element determined to require maintenance among the at least one equipment. The error rate calculator calculates, as a latest error rate, an error rate in a latest predetermined period from the acquired operation history information, the maintenance determiner determines an equipment element with the latest error rate greater than or equal to a predetermined value among one or more equipment elements with a large number of errors as the equipment element that requires maintenance, the one or more equipment elements being included in the at least one equipment element, and the notifier lists the information on the equipment elements with a large number of errors in order, and notifies the number of errors and requirement of maintenance.

Rotary knifes/cutters play an important role in manufacturing of finished products. The rotary cutters tend to lose their cutting material over time. Hence to compensate, pressure applied by cylinder over rotary cutter needs to be changed. But this change in pressure needs to be optimum as too high pressure can lead to loss of material and too low pressure can stop cutting operation. Present application provides methods and systems for real time estimation of pressure change requirements for rotary cutters. The system first determines minimum and maximum usage limit for rotary cutter based on historical rotary cutter usage data and real-time pressure value using first trained model. The system, upon determining that minimum usage limit is reached, determines time for next pressure change based on physical parameters using second trained model. Thereafter, system compares estimated time with estimated maximum usage limit and displays notification to change pressure based on comparison.

Systems and methods for asset management are provided. Event data characterizing events experienced by assets distributed among different sites of a fleet is maintained. The event data includes an asset location within an asset hierarchy of the fleet and an event parameter corresponding to the event. A graphical user interface (GUI) is generated that displays a first window including a hierarchical list of assets organized according to their position within the asset hierarchy. When the GUI receives a selection of a level within the hierarchical list, events associated with the selected level can be identified. Identified events can be classified based upon their event data as a unique event having a single occurrence or a repeat event having multiple occurrences. In response to receipt of the selection, the GUI is updated to display a second window listing single entries for respective unique events and single entries for respective repeat events.

A method and system for reliability reporting for electrical hardware can involve analyzing stress data referenced to a predicted reliability of electrical hardware, the stress data including real-time stress information related to the electrical hardware. Reliability data associated with the electrical hardware based on the real-time information and the predicted reliability of the electrical hardware can be calculated. The reliability data can be presented in a graphical user interface that displays indicators of the reliability data including runtime data associated with the electrical hardware.

A fan system and a monitoring method are provided. The fan system includes a fan device and a controller. The fan device includes a fan unit, a detector, and a memory. The detector detects an operating state of the fan unit during operation to obtain operating raw data corresponding to the operating state. The memory records the operating raw data and stores a data protocol. The controller provides a monitoring request to allow the memory to provide the operating raw data and a data protocol to the controller, converts the operating raw data into operating state data through the data protocol, and provides an early warning notification signal according to the operating state data. When the operating raw data is provided to the controller, the operating raw data stored in the memory is erased.

The present disclosure relates to a task processing method and device based on defect detection, a computer readable storage medium, and a task processing apparatus . The method includes receiving a detection task; determining a task type of the detection task; storing the detection task in a task queue if the task type is a target task type; and executing the detection task in a preset order and generating a feedback signal when a processor is idle. The detection task of the target task type includes an inference task and a training task. Executing the training task includes modifying configuration information according to a preset rule based on product information in the detection task; acquiring training data and an initial model according to the product information; and using the training data to train the initial model according to the configuration information to obtain a target model and store it in memory.

A method of salvaging an output is provided. The method includes defining a condition for terminating a run of a tool, checking whether the condition is likely to be met during a running of the tool, terminating the running in an event the condition is likely to be met, checking a validity of an incomplete output of the tool generated during the running and finalizing the incomplete output in an event the incomplete output is valid.