Abstract: Systems, methods, and apparatuses for improving future reliability prediction of a measurable system by receiving operational and performance data, such as maintenance expense data, first principle data, and asset reliability data via an input interface associated with the measurable system. A plurality of category values may be generated that categorizes the maintenance expense data by a designated interval using a maintenance standard that is generated from one or more comparative analysis models associated with the measurable system. The estimated future reliability of the measurable system is determined based on the asset reliability data and the plurality of category values and the results of the future reliability are displayed on an output interface.

Abstract: The present disclosure relates to an information processing device, an information processing method, and a program for enabling more accurate prediction of a crack to be made. A model acquisition unit acquires a structure model MD from a model generation unit, an external device (not illustrated), or the like. Amplitude load energy A in an element E0 having no cracks is set on the basis of a relationship between an equivalent stress ? and an equivalent elastic strain ? experimentally obtained according to a material constituting the element E0. Since the equivalent elastic strain ? depends on a crack variable ?, the amplitude load energy A is expressed as a function of the crack variable ?. A crack prediction unit predicts a crack to be generated in a structure D by calculating a differential equation having a term proportional to the amplitude energy. The present disclosure can be applied to, for example, a crack prediction device that predicts a crack.

Abstract: There is provided a load controller for a system, the system comprising a first sub-system arranged to deliver a first load, the load controller being operable to: acquire a first target load profile, being the load initially desired for delivery by the first sub-system over an operational period; measure in real time during an update window within the operational period: a first parameter of the first sub-system, to obtain a first measured Load Controller monitor signal; and the first load, to obtain a first measured load signal; develop in real time a model of the first sub-system, using the first measured monitor signal and the first measured load signal, the model relating the first load to the first parameter; given the first target load profile, and the model of the first sub-system, generate for a future period a first predicted monitor signal, the future period being ahead of the update window; and determine whether the first predicted monitor signal satisfies at least one predetermined criterion.

Abstract: A wind turbine generator fault detection method is described. The method includes obtaining a first signal from a generator of a wind turbine and a second signal from a vibration sensor coupled to the wind turbine, the first signal representing an output current of the generator, and the second signal being a time-sampled signal representing vibrations of a bearing in the wind turbine. Determining a shaft rotation frequency signal from the first signal, the shaft rotation frequency signal representing a time-varying rotational speed of a shaft of the wind turbine. Resampling an envelope of the second signal based on the shaft rotation frequency signal to provide a third signal, the third signal being an angular sampled signal. Detecting, by the at least one processor, a fault in the bearing of the wind turbine by identifying a characteristic signature of a bearing fault in the third signal.

Abstract: A computer is configured to enable a rapid, consistent, ply-by-ply, quantitative analytical assessment of various Finite Element Method (FEM) material models based on metrics defined for impact damage. Additionally, the computer is configured to provide a method for determining the accuracy of such FEM material model(s) by comparing the output of those models to non-destructive evaluation (NDE) test data.

Abstract: A contamination sensor for a gas turbine engine is disclosed herein. The contamination sensor is made of a selected composition of material and includes a base alloy, an alloy for improving oxide formation, and at least one element from the transition metal group. The composition of the contamination sensor can be adjusted to react with specific contaminants at specific temperature ranges.

Abstract: A processing device (120) of a tire state detection system (100) is provided with an acceleration data acquisition unit (123) for acquiring the acceleration data detected by an acceleration sensor (111) at every predetermined acquisition interval, an acceleration data extraction unit (125) for extracting the maximum acceleration data indicating at least the maximum acceleration and the intermediate acceleration data indicating the intermediate acceleration excluding the minimum acceleration data indicating the minimum acceleration from 3 or more acceleration data acquired sequentially, and a calculation unit (127) for executing an calculation using the extracted intermediate acceleration data.

Abstract: A method is provided. An actual fatigue curve limit is determined for actual stress of a drilling component based on an actual yield strength of a material of the drilling component. A plurality of drilling parameters is simulated for the drilling component to determine one or more estimated stresses enacted on the drilling component for one or more combinations of the plurality of drilling parameters. A component life cycle of the drilling component is determined based on the actual fatigue curve limit and the plurality of drilling parameters. A consumed component life of the drilling component is determined for an actual drilling step utilizing the drilling component, and a remaining life of the drilling component after the actual drilling step is determined.

Abstract: Systems and methods for determining a damage value of one or more composite components and/or a vehicle using digital image correlation are disclosed. Digital image correlation is used to evaluate a displacement of one or more nanoparticles that are deposited on and/or embedded within the composite component. Digital image correlation is performed by identifying a first reference entry indicating a reference position of the one or more nanoparticles and correlating the first reference entry with sensor data of the composite component indicating a position of the one or more nanoparticles. The damage value of the composite component is determined based on the digital image correlation between the sensor data and the first reference entry.

Abstract: A metadata generation apparatus capable of generating metadata, as well as a metadata generation method and program are provided. The sensing device is configured to generate an output value based on a physical amount that is input. The metadata generation apparatus includes a probability density function generation unit and a metadata generation unit. The probability density function generation unit is configured to generate a probability density function of an output value when a specific physical amount is input to the sensing device. The metadata generation unit is configured to generate metadata based on the probability density function.

Abstract: A method for predicating a service life of a fuel filter includes causing a fuel pump of a fuel system to direct fuel through a fuel filter of a fuel system. The method also includes receiving work cycle data from machines that implement a same or similar fuel system. The method further includes receiving, via sensors of the fuel system, fuel system data and determining, from the fuel system data, a pressure difference across the fuel filter. The method further includes determining, based on the work cycle data, predicted load cycle data for the fuel system and determining, via a filter life model, a filter service interval representing an amount of time the fuel filter is operable in the fuel system prior to the pressure difference reaching a predetermined value.

Abstract: A health monitoring system includes at least one sensor. A health monitoring unit (HMU) is operatively connected to the at least one sensor to receive data therefrom and to generate at least one status indicator (SI) based on the data from the at least one sensor. A computing device is operatively connected to the HMU to receive data therefrom and is configured to generate a health notification when a quantity of the SI that are greater than or equal to a pre-determined SI threshold exceed a pre-determined peak count threshold. A method for monitoring and determining the health of a component includes collecting data during a sample cycle from at least one sensor to generate a numerical SI for a given component with a computing device. The method includes identifying if the numerical SI qualifies as a peak SI, which is greater or equal to a pre-determined SI threshold.

Abstract: A method for detecting damage to one or more mobile blades of an impeller of an aircraft engine, includes measuring the speed of the engine, and for each blade acquiring, by a plurality of sensors, measurements of blade-tip passage times; calculating for each sensor, a deflection of the blade tip; extracting a dynamic component of deflection for each of the calculated deflections; detecting the number of functioning sensors; selecting the dynamic components to be processed based on the detecting step; determining, for at least one blade, a variation of the dynamic behavior of the blade for each functioning sensor; and, for each blade for which a variation of the dynamic behavior has been determined, identification of potential damage to the blade.

Abstract: Provided is a sensor mounting structure for a rolling guide device, which enables easy mounting and removal of a sensor of a type among various types with respect to the track rail, the sensor mounting structure including: a sensor holder to be fixed to an end surface of the track rail in a longitudinal direction of the track rail; and a sensor casing to be held on the track rail by the sensor holder, the sensor holder including: a base plate to be fixed to the end surface of the track rail, and a holding plate having one end continuous with the base plate as a fixed end, and another end formed as a free end elastically deformable with respect to the base plate, the holding plate being configured to urge the sensor casing toward the end surface of the track rail by the free end.

Abstract: Embodiments of the present disclosure relate to the field of intelligent devices. The present disclosure provides a method for monitoring motor vibration, a terminal device, and a computer readable storage medium. In the method, vibration data of the motor is acquired in real time when it is detected that a motor is vibrating; the vibration data is converted into image data indicating a vibration sensation; and the image data is displayed. In this way, the strength of motor vibration is visualized, and the distinguishability of tactile perception is improved.

Abstract: Techniques for implementing Bayesian model for refining and petrochemical wall thickness monitoring are presented. The construction of a model may be automated for each piping circuit or piece of major fixed equipment, utilizing, for example, specific component data, historical thickness measurements, inspection practices and related inspection program information. The model contains nodes describing the most significant sources of variability, namely component original thicknesses, wall thickness degradation over time, corrosion rates and thickness measurement error.

Abstract: In order to supplement results of diagnosis of degradation of an object that has been implemented at set intervals using a degradation progression model for simulating the progression of degradation of the object, a degradation prediction apparatus 100 is provided with: a data generation unit 112 configured to generate, as supplement data, diagnosis results that would be obtained if the degradation diagnosis were performed at an interval shorter than the set interval; a prediction model generation unit 113 configured, using the supplement data, to generate a prediction model for predicting a degradation index indicating a degradation state of the object at a specific point in time; and a degradation index prediction unit 114 configured to predict the degradation index of the object based on the prediction model.

Abstract: The present invention provides a smart air filter blockage detection and alert communication system for use with filters in air circulation systems such as HVAC, vehicles, server systems, and dryers. The system attached to the frame of a filter comprises a light source, a light sensor, two actuators and a control unit. The control unit based on a stored program or an external command activates the actuators to place the source and the sensor devices attached to the two arms of the actuators on the two sides of the filter membrane. The sensor measures the light intensity transmitted through the filter. The control unit upon receiving the intensity data determines the filter blockage level and communicates alert to the user when the blockage exceeds a predetermined value. The system uses a 4G or a 5G IoT network capability for data collection and communication with servers and user devices.

Abstract: A technique facilitates tracking and assessing a fatigue life of a tubing string utilizing, for example, estimation of cycles to failure when used in a wellbore operation. The technique may comprise initially determining a fatigue life of a tubing string. Additionally, the technique comprises utilizing a sensing device, e.g. a magnetic flux leakage (MFL) device, to monitor the tubing string. When an anomaly, e.g. a new defect, is detected by the sensing device, a new fatigue life of the tubing string is determined based on the change. The new fatigue life may be used to estimate a fatigue life in terms of cycles to failure.

Abstract: Methods for quantitatively determining the time (TNI) between (1) the application of this method and (2) the time at which the next out-of-service API 653 internal inspection of a steel, field-erected, aboveground storage tank (AST) containing petroleum/water products should be performed. These methods combine four in-service measurements of the thickness, integrity, and corrosion rate of the tank bottom with an empirical corrosion rate cumulative frequency distribution (CFD) for the tank of interest to develop a Bayesian tank bottom survival probability distribution to determine TNI. During this entire TNI time period, the risk of tank bottom failure is less than at the time these methods were applied. If available, the results of a previous out-of-service API 653 internal inspection are also used. These methods can be applied at any time while the tank is in-service to update the internal inspection interval previously determined in an out-of-service internal inspection of the tank.

Abstract: A control system and method for operating an articulating header of a combine harvester in a failsafe mode. The method includes monitoring the articulating header, alerting a user when a fault condition is detected in the articulating header, and converting the articulating header to a non-articulating header in response to the fault condition so that the combine harvester may be operated in a failsafe mode.

Abstract: A system including at least one image acquisition module to acquire images of the environment of the aircraft and of the runway, a module to recognize in the images at least one element representative of at least one contaminant likely to influence the state of the runway, a module to determine at least one property of the contaminant or contaminants and a module to transmit to a user device the property or properties of the contaminant or contaminants. The system allows contaminants to be identified and to be located on the runway or in the environment of the aircraft.

Abstract: Systems, methods, and other embodiments associated with providing long-term predictions for specific maintenance are described. In general, the system or method will: Retrieve a current amount of usage for a component part of a device, a failure probability curve for the component part, and a planned maintenance schedule for the device. Update the failure probability curve based on trends of data obtained from sensors associated with the component part, and causal factors associated with the device. Determine that the likelihood of failure for the component part after a first upcoming planned maintenance and before a second upcoming planned maintenance exceeds a threshold. Generate a work order for specific maintenance on the component part to be performed during the first upcoming planned maintenance. Transmit the work order to cause resources to be timely obtained and labor allocated to perform the specific maintenance during the first upcoming planned maintenance.

Abstract: Techniques, including systems and methods for monitoring a rotating equipment, are provided. A sensor that is in proximity of the rotating equipment senses vibrations of the rotating equipment. The sensor generates a digital signal corresponding to the vibrations of the rotating equipment and transmits the digital signal over a communication network. A server receives the digital signal and pre-processes the digital signal using ensemble empirical mean decomposition (EEMD) technique. The server processes the digital signal using wavelet neural network (WNN) to detect faults in the rotating equipment. Further, the server processes the digital signal using the wavelet neural network to predict remaining useful life (RUL) of the rotating equipment.

Abstract: A control system includes one or more processors configured to determine when to extend a life span of an engine by applying an additional restorative coating to the engine based on one or more monitored parameters of the engine. The monitored parameters include a condition of a previously applied restorative coating. The one or more processors are configured to determine the condition of the previously applied restorative coating based on an optical response of the previously applied restorative coating. The one or more processors also are configured to direct application of the additional restorative coating based on the one or more monitored parameters of the engine.

Abstract: A method includes confirming when a vehicle accesses a region of interest, obtaining data associated within the region of interest, and determining, based on analytics performed on the data obtained of the region of interest, whether one or more anomalies are present at the region of interest. Obtaining data includes at least one of collecting, with an imaging sensor associated with the vehicle, image data of a given point of interest within the region of interest or collecting, with a motion sensor associated with the vehicle, motion data of the vehicle in a given fragment of interest within the region of interest. The confirming, obtaining and determining steps are performed by at least one processing device comprising a processor operatively coupled to a memory.

Abstract: A life prediction apparatus is configured to accurately predict a life of a cable wired at a joint part of a robot. The life prediction apparatus includes a fatigue-level estimation unit for estimating a fatigue level of the cable based on encoder information of an actuator which moves the joint part of the robot, and a life prediction unit for predicting the life of the cable based on a fatigue level of the cable estimated by the fatigue-level estimation unit and an allowable value of the cable.

Abstract: A non-transitory computer readable medium having instructions stored thereon that, when executed by at least one processor, cause the at least one processor to receive road quality data from at least one vehicle in a fleet of vehicles and determine a route for the at least one vehicle in the fleet of vehicles. The road quality data may be gathered by at least one sensor configured to detect autonomous vehicle movements and orientation. The route may avoid a segment of road that is associated with poor road quality data.

Abstract: A computer-implemented method and a system parse and aggregate unstructured data objects. The method includes obtaining the unstructured data objects from description fields of records in a database comprising client tickets that are created for application maintenance, transforming the obtained unstructured data objects to create transformed data objects, identifying a number of criteria attributes of the client tickets, where the number of criteria attributes are determined according to at least part of the transformed data objects, word importance, word sentiment, a user input, or client ticket priorities, or any combinations, and generating a plurality of ticket reports by aggregating the client tickets according to ranking orders of numbers of tickets for the client tickets and the identified criteria attributes.

Abstract: A method for classifying depth scan data at a computing device includes: obtaining, at the computing device, a set of depth measurements and a graphical representation of the depth measurements; automatically selecting, at the computing device, a subset of the depth measurements indicating a region of interest; rendering, on a display of the computing device, an image including (i) the graphical representation of the depth measurements and (ii) a graphical indication of the region of interest overlaid on the graphical representation of the depth measurements; receiving, via an input device, a selection associated with the image; and generating a region of interest indicator based on the subset of the depth measurements and the selection.

Abstract: A machine tool health monitoring method which is to use a predetermined plurality of vibration sensors on a plurality of components of a machine tool and to drive motors of the machine tool to excite the machine tool using an electronic device while the health status of the machine tool is good, and then to perform a diagnostic process to obtain a characteristic cluster consisting of a plurality of modals, and then to define the characteristic cluster as a sample health characteristic cluster. The diagnostic process includes the procedures of vibration transmissibility obtaining, singular value decomposition, curve fitting and modal establishing. In addition, excite the machine tool and proceed the diagnosis process to obtain a current health characteristic cluster. Finally, the current health characteristic cluster is compared with the sample health characteristic cluster to judge whether the machine tool is healthy or not.

Abstract: A client computing system (CCS) receives a tool measurement from a measurement tool other than by determining the tool measurement from a vehicle data (VD) message. The CCS generates a VD report including a vehicle identifier of a vehicle associated with the tool measurement and the VD report. The VD report can include temporal and/or spatial identifiers pertaining to the tool measurement. The VD report can be provided to a server computing system (SCS) for storage as part of a VD record associated with the vehicle. The CCS that generated the VD report or another CCS can request the VD report from the SCS. The SCS can determine if the requesting CCS is authorized to receive the VD report to maintain the vehicle owner's privacy. The tool measurement can be selected from a service procedure provided to the CCS from SCS and displayed at the CCS.

Abstract: A system and method for predicting remaining lifetime of a component of equipment is provided. The prediction system includes a data module, a feature module, a current data-based prediction module, a historical data-based prediction module, and a confidence module. The data module obtains a test sensor data of the component of equipment. The feature module obtains a historical health indicator and the current-health indicator. The current data-based prediction module obtains a first predicted remaining lifetime and a first prediction confidence according to the current-health indicator. The historical data-based prediction module obtains a second predicted remaining lifetime and a second prediction confidence according to the historical health indicator.

Abstract: A medical fluid delivery system includes: a medical fluid delivery machine including a component having at least one of associated output data or associated test data; at least one of a (i) component output replacement limit and a component output soft limit for the component or (ii) a component testing replacement limit or a component testing soft limit for the component; and a computer programmed to store at least one of (i) or (ii), and for (i) analyze the output data to provide a first indication of how well the component is performing relative to the component output replacement limit and the component output soft limit, and for (ii) analyze the test data to provide a second indication of how well the at least one component is testing relative to the component testing replacement limit and the component testing soft limit.

Abstract: Methods and systems for evaluating integrity of a tubular located within a wellbore are provided. The method includes measuring an operation parameter of the wellbore, measuring a feature of the tubular two or more times to produce an integrity log each time the feature is measured, and determining a tubular integrity analysis for the tubular by using the integrity logs and the operation parameter. The tubular integrity analysis contains parameter limitations for the tubular. The method also includes determining if tubular integrity is within or outside the parameter limitations. If the tubular integrity is within the parameter limitations, then determine a duration of integrity for the tubular. If the tubular integrity is outside of the parameter limitations, then determine a location on the tubular for loss of tubular integrity.

Abstract: A machine tool includes: a first calculator configured to calculate a first frequency characteristic based on a first oscillation signal and a measurement signal of a physical quantity measured by a measurement unit when the drive shaft of a servo motor swings in accordance with the first oscillation signal; and a second calculator configured to calculate a second frequency characteristic based on a second oscillation signal and a measurement signal of the physical quantity measured by the measurement unit when the drive shaft of the servo motor swings in accordance with the second oscillation signal.

Abstract: A predictive maintenance system for an aircraft-based power converter subsystem (PCS) continuously measures primary system parameters such as input and output currents and voltages, ambient and PCS component temperatures, and hold-up capacitance voltage. The predictive maintenance system updates the likely failure rate of the PCS based on the primary parameters and secondary parameters (e.g., power consumption, power efficiency, thermal resistance, hold-up storage capacitance) derived therefrom. Sensed primary parameters and derived secondary parameters, as well as the updated failure rate, are stored to memory in order to monitor their time derivatives and regression over the PCS lifespan. Should the updated failure rate ever reach or exceed an acceptable failure rate threshold, the predictive maintenance system alerts the aircraft control system, end user, or provider to service the PCS in advance of an unanticipated field failure.

Abstract: An actuator for a medium voltage circuit breaker or recloser includes: at least one movable contact with a contact stem, driven by an electromagnetic drive or a motor drive; and a spring, the spring being positioned in a kinematic chain between the drive and the at least one movable contact or contact stem. An arrangement of the at least one movable contact and the electromagnetic drive or motor drive is coupled to a detection unit for detecting a micromotion activation in order to register an actual movability and availability of the electromagnetic drive or motor drive of the at least one movable contact without changing an actual switch position itself.

Abstract: A machine learning device included in a tool selecting apparatus includes a state observing unit that observes, as state variables indicative of a current environmental state, data related to machining condition, data related to cutting condition, data related to machining result, and data related to a tool, and a learning unit that, by using the state variables, learns distribution of the data related to the machining condition, the data related to the cutting condition, and the data related to the machining result, with respect to data related to the tool.

Abstract: A method of identifying a fault in a system of gears in a wind turbine is provided. The method determines a first centre harmonic frequency amplitude according to vibrations of the system of gears and determines a plurality of sideband amplitudes of the first centre harmonic frequency amplitude. Further, the method calculates an average sideband amplitude from the plurality of sideband amplitudes and determines a value indicative of damage incurred by the system of gears based upon the first centre harmonic frequency amplitude and the average sideband amplitude. The centre harmonic frequencies may be harmonic tooth mesh frequencies and the value indicative of damage may be a ratio of the centre harmonic frequency amplitude and a difference between the centre harmonic frequency amplitude and the associated average sideband amplitude or vice versa. The method may analyse the value of any ratio obtained and use the ratio values to identify, and monitor the progress of, a fault.

Abstract: A system and method as disclosed herein develops a predicted current remaining useful life (RUL) of a component through a generalized fault and usage model that is designed through a process of simplifying Paris' Law (or other power law) in conjunction with a Kalman Smoother (or other filtering technique). One of the many advantages of this state observer technique is that the backward/forward filtering technique employed by the Kalman Smoother has no phase delay, which allows for the development of a generalized, zero tuning model that provides an improved component health trend, and therefore a better estimate of the predicted current RUL.

Abstract: A method for predicting equipment failure includes receiving parameter sample values associated with parameters of benchmark equipment and operational status information associated with the benchmark equipment. The parameter sample values and operational status information are periodically acquired. A model is generated for relating one or more of the parameters to benchmark equipment failure. For each parameter, a threshold value at which an output of the model indicates benchmark equipment failure is determined. Next, parameters of an equipment under test having parameter sample values that match the determined threshold values are determined. For each determined parameter, benchmark equipment having parameter sample values that match the parameter sample values of the equipment under test that match the determine threshold values are determined. Survivability data for the equipment under test is generated based on survivability data associated with the determined benchmark equipment.

Abstract: A method and device for predicting a wind turbine fault is provided. The method includes: initializing a wind turbine model of the wind turbine; updating wind turbine model parameters of the wind turbine model based on a current environment condition and current actual wind turbine state parameters of the wind turbine periodically; establishing a variation model of the wind turbine model parameter based on historical wind turbine model parameters and corresponding historical environment conditions; predicting the wind turbine model parameters at a future moment by using the variation model of the wind turbine model parameters based on a future environment condition at the future moment; establishing a wind turbine fault model based on the historical wind turbine model parameters and corresponding historical wind turbine faults; predicting the wind turbine fault at the future moment based on the predicted wind turbine model parameters and the wind turbine fault model.

Abstract: Systems, methods, and media for detecting abnormalities in equipment that emit ultrasonic energy into a solid medium during failure are provided. In some embodiments, devices for monitoring a piece of equipment are provided, the devices comprising: a piezo microphone having a signal output; a mechanical structure acoustically coupling the piezo microphone to the piece of equipment; a hardware processor configured to: receive intensity signals based on the signal output of the piezo microphone; perform a fast Fourier transform on the received intensity signals to produce a set of output band values for each of a plurality of frequency bands; average at least some of the output band values to produce an average value; and calculate a sum of at least some of the output band values; and a transceiver that transmits the sum to a remote device.

Abstract: A pump control system receives inputs from a low level fluid level sensor, a high level fluid level sensor, and/or a temperature sensor. Based on inputs from the sensors, and in response to sensed trouble conditions, duplex pumping or pump specific simplex operation is restricted based on past and present pump current draws. Furthermore, using the temperature sensor and a temperature sensor output device, the temperature of the sump environment can be monitored at will from a remote location.

Abstract: Methods and systems for determining the integrity of a manufactured board are disclosed. An example system includes a testing platform configured to secure the manufactured board, a sensor configured to measure a parameter corresponding to a flatness of a surface of the board, and a controller. The controller is configured to identify regions on the surface corresponding to one of a peak or a valley based on the parameter, and calculate a score representing the integrity of the manufactured board based on the identified peaks and valleys. The controller adjusts a flow rate, a pressure, a temperature, and position of a deposited substance in a manufacturing process based on a comparison with a height of the peak and/or a depth of the valley to stored peak heights and/or valley depths. In some examples, a mechanical tester determines a compressive strength and a density of the board at the identified regions.

Abstract: To acquire information relating to degradation of a pipe on the basis of information that can be acquired using a simple method. The analysis device according to one embodiment is provided with: a determining unit for determining whether or not the accuracy of a pipe network model based on information that includes a parameter that changes in value in accordance with degradation of a pipe satisfies a predetermined criterion; and a derivation unit for deriving information relating to degradation of the pipe, based on the parameter, if the accuracy satisfies the predetermined criterion.

Abstract: There is provided a contact force evaluation method for evaluating a contact force against a supporting member of a tube bundle positioned in a fluid and supported by the supporting member, including a contact force setting step of setting a contact force of the tube bundle, a probability density function calculation step of calculating a probability density function of a reaction force received by the supporting member from the tube bundle in response to a predetermined input, using a vibration analysis model of the tube bundle and the supporting member, a probability calculation step of calculating a probability that a reaction force equal to or higher than the set contact force occurs, based on the calculated probability density function, and an evaluation step of evaluating the set contact force, based on the calculated probability.

Abstract: Techniques that facilitate optimization of prototype and machine design within a three-dimensional fluid modeling environment are presented. For example, a system includes a modeling component, a machine learning component, and a graphical user interface component. The modeling component generates three-dimensional model of a mechanical device based on a library of stored data elements. The machine learning component predicts one or more characteristics of the mechanical device based on a first machine learning process associated with the three-dimensional model. The machine learning component also generates physics modeling data of the mechanical device based on the one or more characteristics of the mechanical device. The graphical user interface component provides, via a graphical user interface, a three-dimensional design environment associated with the three-dimensional model and a probabilistic simulation environment associated with optimization of the three-dimensional model.

Abstract: Disclosed, is a system for pipe network failure classification, including a network part sensor layer for monitoring pipe(s) operation condition and collecting related parameter values. A system pipe network parts database includes feature parameter value records of pipes in the pipe network. A classification processing logic, intermittently receive sensor collected parameters from said network part sensor layers, references records of the pipe network parts database and retrieves one or more feature parameter values associated with the failed pipe's operational or environmental conditions, and classifies the pipe failure into one of two or more failure categories.