Abstract: A method for generating a multi-layer predictive model includes collecting historical observable data from one or more pieces of equipment of a same type, wherein the historical observable data is collected at different hierarchical levels of the one or more pieces of equipment; collecting operational state indications of the pieces of equipment corresponding to the collected historical observable data; generating, from the collected historical observable data, a set of operational state models, wherein each operational state model corresponds to one of the different hierarchical levels; and generating, from outputs of the set of operational state models, a top-level operational model for the piece of equipment. The top-level operational model is operable to determine maintenance and replacement timing for the piece of equipment.

Abstract: A method is used to control a fuel level gauge of a vehicle system and includes: monitoring, via an engine controller, a fuel economy of the vehicle system; comparing, via the engine controller, the fuel economy with a predetermined fuel economy threshold to determine whether the fuel economy is less than the predetermined fuel economy threshold; adjusting, via the engine controller, a sensitivity of a display filter in response to determining that the fuel economy is less than the predetermined fuel economy threshold for a predetermined amount of time in order to maximize an accuracy of the fuel level gauge, wherein the display filter smoothes an unfiltered fuel level signal received from a fuel level sensor of the vehicle system, thereby generating a filtered fuel level signal; and controlling, via an instrument panel controller, the fuel level gauge of the vehicle system.

Abstract: Methods, apparatus, systems and articles of manufacture to trigger calibration of a sensor node using machine learning are disclosed. An example apparatus includes a machine learning model trainer to train a machine learning model using first sensor data collected from a sensor node. A disturbance forecaster is to, using the machine learning model and second sensor data, forecast a temporal disturbance to a communication of the sensor node. A communications processor is to transmit a first calibration trigger in response to a determination that a start of the temporal disturbance is forecasted and a determination that a first calibration trigger has not been sent.

Abstract: An example method for identifying an internal flaw in a part produced using additive manufacturing includes calculating a proof load of a part, in which the proof load is a load that when applied to the part will cause the part to fail based on presence of an internal flaw in the part, determining whether the part can withstand the proof load based on a geometry of the part and static strength data, and based on a determination that the part can withstand the proof load, applying the proof load to the part during a compliance test of the part. The proof load causes the part to fracture, when applied to the part, based on presence of the internal flaw in the part that is of a threshold size at which the internal flaw would cause cracking and potential part failure when the part is placed under the operational load.

Abstract: A sensor abnormality determining apparatus is configured to determine an abnormality in a sensor apparatus, which is used to recognize a surrounding environment of a vehicle and which includes a heater for heating a cover. The sensor abnormality determining apparatus is provided with: a detector configured to detect a temperature of the heater; an estimator configured to estimate a temperature of the heater on the basis of a speed of the vehicle and an outside air temperature; and a determinator configured to determine an abnormality in the sensor apparatus on the basis of the detected temperature and the estimated temperature.

Abstract: A method and controller system are described for automatically tuning a controller of a drilling rig. The controller system includes a controller that determines a moving average of measured values, determines a moving standard deviation of the measured values, defines an outer zone of measured values with the determined moving average and the determined moving standard deviation, defines an inner zone of measured values with the determined moving average and the determined moving standard deviation, monitors the measured values for the occurrence of statistical events with respect to the outer zone and inner zone. Based on the detected statistical event, the controller adjusts the gain of the controller the based on the statistical event being associated with the outer zone or inner zone.

Abstract: When two event prediction models produce different numbers of catches, a computer system may be configured to determine which of the two models has the higher net value based on how a “Break-Even Alert Value Ratio” for the models compares to an estimate of the how many false flags are worth trading for one catch. Further, when comparing two event prediction models, a computer system may be configured to determine “catch equivalents” and “false-flag equivalents” numbers for the two different models based on potential-value and impact scores assigned to the models' predictions, and the computing system then use these “catch equivalents” and “false-flag equivalents” numbers in place of “catch” and “false flag” numbers that may be determined using other approaches.

Abstract: Systems and methods for estimating reservoir productivity as a function of position in a subsurface volume of interest are disclosed. Exemplary implementations may: obtain subsurface data and well data corresponding to a subsurface volume of interest; obtain a parameter model; use the subsurface data and the well data to generate multiple production parameter maps; apply the parameter model to the multiple production parameter maps to generate refined production parameter values; generate multiple refined production parameter graphs; display the multiple refined production parameter graphs; generate one or more user input options; receive a defined well design and the one or more user input options selected by a user to generate limited production parameter values; generate a representation of estimated reservoir productivity as a function of position in the subsurface volume of interest using the defined well design and visual effects; and display the representation.

Abstract: An apparatus configured to be removably attached to a tire/hub assembly of a vehicle via lug nuts of the tire/hub assembly is used to determine imbalance forces of the tire/hub assembly during motion of the vehicle. The apparatus includes a disk and a plurality of cup-shaped objects fixed at one end to the disk and extending perpendicularly outward from the disk. The plurality of cup-shaped objects are arranged a fixed radial distance from a radial center of the disk in a pattern that matches the lug nuts of the tire/hub assembly. The apparatus is mounted to the vehicle's tire/hub assembly by fitting the plurality of cup-shaped objects over the lug nuts of the tire/hub assembly. The apparatus further includes one or more inertial measurement units (IMU's) mounted to the disk to measure parameters that are used for calculating the imbalance forces during motion of the vehicle.

Abstract: Disclosed herein is a system and process for monitoring and controlling fire suppression systems in commercial kitchens. Repeatedly various components of the fire suppression systems are tested for faults. When a fault is detected in a particular component, the fault is communicated through a communications module associated with the commercial kitchen via the internet to one or more remote servers. Upon receiving information indicating a particular fault, the one or more remote servers acts to alert interested parties of the default.

Abstract: A method of estimating a temperature of a reactor provided in a boost converter disclosed herein, a temperature of a bus bar connected with the reactor and a current flowing in the bus bar are measured; when an absolute value of a DC component ILdc of the measured current exceeds a current threshold Ith, an estimated value of the temperature of the reactor is calculated by adding a first correction value, which depends on the DC component ILdc, to the measured temperature TL and is outputted as an estimated temperature TR; and when the absolute value of the DC component is lower than the current threshold Ith, the estimated value of the temperature of the reactor is calculated by adding a second correction value, which does not depend on the measured current, to the measured temperature and is outputted as the estimated temperature TR.