Abstract: The present disclosure provides an Industrial Internet of Things system conducive to system scalability and control method thereof. The system comprises a user platform, a service platform, a management platform, a sensor network platform, and an object platform. The service platform or the sensor network platform adopts independent layout. The service platform and the sensor network platform both include sub platforms and each sub platform is provided with a database, a processor, and/or an information channel. The object platform includes a production line, and the production line is configured with a plurality of sensors. In each sub platform of the sensor network platform, a same communication protocol is used with the sensors connected to a same sub platform of the sensor network platform. Any sub platform of the sensor network platform is connected with a unique sub platform of the service platform through the management platform.

Abstract: A system and method for providing a corrective solution recommendation for an industrial machine failure, the method including: monitoring a plurality of segments of at least an industrial machine behavioral model to identify a first segment having at least a first set of characteristics associated with a previous machine failure; determining a corrective solution recommendation that solved the previous machine failure; identifying at least a second set of characteristics associated with a second segment; and generating a notification comprising the corrective solution recommendation when the second set of characteristics is determined to be similar to the first set of characteristics above a predetermined threshold.

Abstract: A machine learning method according to a viewpoint of the present disclosure is a machine learning method for creating a learning model configured to estimate the life of a consumable of a laser apparatus, the method including acquiring first life-related information containing data on a parameter relating to the life of the consumable, the data recorded in correspondence with different numbers of oscillation pulses during a period from the start of use of the consumable to replacement thereof, dividing the first life-related information into a plurality of levels each representing the degree of degradation of the consumable in accordance with the numbers of oscillation pulses to create training data, creating the learning model by performing machine learning using the created training data, and saving the created learning model.

Abstract: Computer-implemented methods and systems for identifying topographic features that optimises and subsequently implements a machine learning model to automatically classify and extract topographic features from a set of target imagery are described herein. In particular, the optimised machine learning model creates heat maps from the target imagery corresponding to each class of feature, wherein the intensity of each pixel indicates whether a certain type of feature is present. The resulting heat maps are then processed to transform each pixel, specifically those identifying a topographic feature, into a geospatial vector.

Abstract: Determination of Ground Thermal Conductivity (TC) in the field of Geothermal Heat Pumps (GHPs). The invention provides a simple mathematical approach using minimal terms with apparatus not dependent on stable electric power generation.

Abstract: Systems, methods and apparatus for modifying a data collection trajectory for centrifuges are described. An example system may include a data acquisition circuit to interpret a plurality of detection values, each corresponding to at least one of a plurality of input sensors communicatively coupled to the data acquisition circuit. The system may further include a data storage circuit to store specifications and anticipated state information for a plurality of centrifuge types and an analysis circuit to analyze the plurality of detection values relative to specifications and anticipated state information to determine a centrifuge performance parameter. A response circuit may initiate an action in response to the centrifuge performance parameter.

Abstract: A rotation speed sensor, configured to detect a rotation speed of a rotating body, is composed of a cable composed of an electric wire including a conductor wire, and a sheath provided over the electric wire, a circuit substrate mounted with an integrated circuit thereon and connected to the conductor wire being exposed from the cable, and a housing including a first housing, which is configured to hold the circuit substrate therein, and a second housing, which is configured to hold the cable therein, with the first housing being welded to the second housing, and with the second housing being welded to a surface of the sheath of the cable.

Abstract: A system and method for acquiring accurate vibration data, time of arrival, or other mechanical state for a mechanical system with rotary elements, such as a planetary gear system with multiple planet gears that rotate relative to a central sun gear, or blades in a turbine engine. Multiple vibration sensors are arranged at spaced intervals along the exterior of the gear system (such as along the ring gear of the system or the exterior housing of the system). Each vibration sensor is so situated as to periodically detect vibrations from each one of the planet gears in succession, as the planet gears rotate around the central sun gear. Using timing and gear-system configuration information, a determination is made as to which gear is being sensed by any one sensor at any time; also determined is which teeth of each gear are being sensed by a particular sensor at any one time.

Abstract: A learned model generation method involves: outputting, when it is determined that the sequence of the positions of a first end surface of a first sheet material indicated by first data intersects with the sequence of the positions of a second end surface of a second sheet material indicated by second data, information indicating that a roll is defective and that the defect is caused by a first feed reel or a first affixing roller; and generating a facility state diagnosis model by using the first data and the second data read before preservation of the first feed reel when it is determined that a first difference between a first defect rate of the roll before the preservation of the first feed reel and a second defect rate of the roll after the preservation of the first feed reel is equal to or larger than a prescribed value.

Abstract: Multi-metric artificial intelligence (AI)/machine learning (ML) models for detection of anomalous behavior of a machine/system are disclosed. The multi-metric AI/ML models are configured to detect anomalous behavior of systems having multiple sensors that measure correlated sensor metrics such as coolant distribution units (CDUs). The multi-metric AI/ML models perform the anomalous system behavior detection in a manner that enables both a reduction in the amount of sensor instrumentation needed to monitor the system's operational behavior as well as a corresponding reduction in the complexity of the firmware that controls the sensor instrumentation. As such, AI-enabled systems and corresponding methods for anomalous behavior detection disclosed herein offer a technical solution to the technical problem of increased failure rates of existing multi-sensor systems, which is caused by the presence of redundant sensor instrumentation that necessitates complex firmware for controlling the sensor instrumentation.

Abstract: A vibration analysis apparatus includes: a storage unit storing a regression equation indicating correspondence between rotation speed change of a rotation mechanism and a peak occurrence frequency of acceleration of vibration, for each acceleration peak; an analysis unit extracting a peak occurrence frequency of acceleration of vibration for each acceleration peak, based on vibration data of the rotation mechanism and calculating, for each acceleration peak, a waveform area of the acceleration peak by integrating the acceleration peak over a specific frequency section; and an anomaly determination unit determining whether operational anomaly occurs in the rotation mechanism for each acceleration peak.

Abstract: Systems and methods of applying a prediction model to metal oxide sensors that change their electric resistances in response to surrounding gas concentrations for predicting equilibrium state resistance values of the sensors. The method includes heating a metal oxide sensor to for a predetermined period of time for the metal oxide sensor to interact with a surrounding gas; sampling transient resistance values of the metal oxide sensor to obtain sampled transient resistance values; determining an electrical resistance of the metal oxide sensor in a chemical equilibrium state of the interaction of the metal oxide sensor and the surrounding gas via applying a neural network; and determining a concentration level of the surrounding gas at the chemical equilibrium state by mapping the determined electrical resistance to a corresponding concentration level of the surrounding gas.

Abstract: A permutation that optimizes correspondence between the seismic data and the simulated data is computed using a graph space optimal transport formulation-based misfit. The seismic data or simulated data are transformed into auxiliary data by applying a portion of time shifts computed from the optimal permutation before updating the structural model of the explored underground formation. The full-waveform inversion minimization of the distance between auxiliary data and the seismic data or simulated data to which partial time shifts have not been applied, may be embedded in a Kantorovich-Rubinstein norm.

Abstract: A method of calculating a time difference is disclosed. The method includes sampling a first ultrasonic signal (r21) to produce a first sampled signal (y1(i)) and sampling a second ultrasonic signal (r12) to produce a second sampled signal (y2(i)). A first time (LEAD_LAG) is determined between a time the first sampled signal crosses a threshold (?1) and a time the second sampled signal crosses the threshold. The first sampled signal is cross correlated with the second sampled signal to produce a second time (SAMP_OFFSET). The time difference is calculated in response to the first and second times.

Abstract: A method for identifying an operating frequency may comprise performing a frequency sweep using one or more injector electrodes disposed on a downhole tool, recording one or more measurements from the frequency sweep, identifying one or more stable frequencies from the frequency sweep, and identifying one or more operating frequencies from the one or more stable frequencies. A system for electrical isolation may comprise a downhole tool, a pad, an arm, wherein the arm is attached to the mounting bracket and the mandrel, and an information handling system connected to the pad through the first set of electronics and the second set of electronics. The downhole tool may comprise a mandrel, a chassis disposed in the mandrel, and a first set of electronics disposed in the chassis.

Abstract: Systems and methods include a computer-implemented method for providing flare header information. Instantaneous flaring flowrate data is received from flaring sources of a flare network of a processing facility. The instantaneous flaring flowrate data is analyzed in conjunction with physical properties of relief sources obtained from a heat and material balance of the processing facility. A heating value and a molecular weight are determined for each relief source and flare header using a processing model associated with a relief source type, size, and identifications. The relief sources are connected using a data signal received and processed using the processing model. Reports are generated showing average daily heating values and molecular weights for each flare header. A real-time display is provided for monitoring instantaneous heating values and molecular weights for each flare header on a real-time basis.

Abstract: An industrial asset may have monitoring nodes that generate current monitoring node values representing a current operation of the industrial asset. An abnormality detection computer may detect when a monitoring node is currently being attacked or experiencing a fault based on a current feature vector, calculated in accordance with current monitoring node values, and a detection model that includes a decision boundary. A model updater (e.g., a continuous learning model updater) may determine an update time-frame (e.g., short-term, mid-term, long-term, etc.) associated with the system based on trigger occurrence detection (e.g., associated with a time-based trigger, a performance-based trigger, an event-based trigger, etc.). The model updater may then update the detection model in accordance with the determined update time-frame (and, in some embodiments, continuous learning).

Abstract: The invention relates to a safety link for use in lifting, winching, towing or securing apparatus or similar. The safety link including, a first connection point, a second connection point, and an indicator means positioned substantially between and associated with the first connection point and the second connection point. If an overload force is applied, the forces between the first connection point and the second connection point cause a change in the indicator means, whereby the change indicates the overload to the user so safety checks may be conducted. The invention also relates to a method of use.

Abstract: A method may include obtaining seismic data regarding a geological region of interest. The method may further include obtaining a property model regarding the geological region of interest. The method may further include determining an adjoint migration operator based on the property model. The method may further include updating the property model using the seismic data and a conjugate gradient solver in a least-squares reverse time migration to produce a first updated property model. The conjugate gradient solver is based on the adjoint migration operator. The method may further include updating the first updated property model using a threshold shrinkage function to produce a second updated property model. The threshold shrinkage function comprises a sign function and a maximum function that are applied to the first updated property model. The method may further include generating a seismic image of the geological region of interest using the second updated property model.

Abstract: A computer monitors a state of a system. A time branch is defined for each valid value of each discrete variable. A system model is executed with observed values to update each time branch and determine a probability associated with each time branch. A discrete variable is selected, and a sequence duration value is incremented. When the incremented sequence duration value is greater than a predefined minimum sequence duration value, a probability change value is computed for the discrete variable, and, when the computed probability change value is less than or equal to a synchronization probability change value, a continuous value for each continuous variable for each time branch of the discrete variable is synchronized, and the sequence duration value for the selected discrete variable is reinitialized. The continuous value for at least one non-observed continuous variable is output.