Abstract: A method for motor fault detection comprises acquiring sensor data from each sensor of a plurality of sensors associated with a motor. Each sensor of the plurality of sensors provides data corresponding to a unique modality. The method further comprises extracting features from the sensor data of each sensor and mapping the extracted features to a shared latent space using a deep generalized canonical correlation analysis model that aligns the features by learning nonlinear transformations that maximize correlations among the sensor data of different sensors. An attention-based fusion mechanism is applied to the aligned features to dynamically compute attention weights for each sensor modality based on a relevance of a respective sensor modality to a fault detection task. The method further comprises generating a fused feature representation by aggregating the aligned features weighted by the attention-based fusion mechanism and generating motor fault data based on the fused feature representation.

Abstract: A non-intrusive load monitoring method based on V-I trajectory and neural network includes: collecting the household voltage, current and active power data in real time; determining whether there is a switching event and whether the load operating state has reached a steady state through the change of the active power; obtaining the voltage, current and power data of the load, converting the V-I trajectory into RGB color image containing the phase difference between the voltage and current, power and other information. After obtaining the RGB color image, performing normalization processing and performing load monitoring through pre-trained convolutional neural network. The present disclosure fully extracts the steady-state feature of the load through the convolutional neural network, and the neural network model can directly run on an embedded device, and does not need to rely on the computing support of a server.

Abstract: A system and method for motor eccentricity fault detection is disclosed. The method includes extraction of fault-related features through topological data analysis (TDA) for motor current signals and apply them to motor eccentricity fault detection. The method further includes the procedure of obtaining topological features from time-domain data and representing them in persistence diagrams and vectorized Betti sequences. The method further includes the extraction of fault-related features from the obtained topological features of the data, which can be distinctively associated with not only fault type but also fault severity level. Further, the method includes use of machine learning models to extract fault-related features from TDA, for the prediction of motor eccentricity fault level, even for data from new eccentricity levels that are not seen in the training data.

Abstract: A system for magnetic refrigeration is provided. The system includes a layered structure formed by a sequence of a plurality of magnetocaloric material (MCM) components interlinked with a sequence of a plurality of Peltier modules. Each Peltier module of the plurality of Peltier modules is sandwiched between two MCM components of the plurality of MCM components. The system further includes a power source configured to concurrently power each Peltier module in the sequence of the plurality of Peltier modules. Current in each powered Peltier module flows in a constant direction. The system further includes a magnetic source configured to apply spatially uniform magnetic field to the sequence of the plurality of MCM components.

Abstract: A system and method for motor fault detection and classification, which includes extraction of fault-related features through topological data analysis (TDA) for motor current signals is disclosed. Given time-domain data, the system maps data points of the time series data into a three-dimensional space of the three-phase current to form a three-phase point cloud. The system then obtains topological features using TDA and applies them to motor fault detection and classification. Further, the system includes the use of machine learning models to extract fault-related features from TDA, for the prediction of motor faults and/or severity levels.

Abstract: Methods and systems are presented for providing a machine learning model framework that provides an adaptive machine learning model base on providing quick and incremental trainings to the machine learning model. Instead of using the entire available training dataset to train the machine learning model, a subset of the available training dataset that accurately represents the characteristics of the training data set is extracted to be used in each iteration of incremental training. Furthermore, labels of unmatured dataset are imputed to provide additional training datasets that correspond to any emerging pattern. Synthetic training datasets are also generated to mimic datasets that correspond to an emerging pattern to strengthen the machine learning model's ability to recognize the emerging pattern.

Abstract: A data processing apparatus relates to fields of artificial intelligence, chips, large models, and distributed computing platform technologies and includes: a quantization unit for quantizing floating-point data to be processed into first fixed-point data according to a first maximum/minimum value corresponding to the floating-point data to be processed; and quantizing second floating-point data into second fixed-point data according to a second maximum/minimum value corresponding to the floating-point data to be processed, the second floating-point data being obtained according to the floating-point data to be processed and first floating-point data obtained by dequantizing the first fixed-point data; and a first computing unit for obtaining a first calculation result according to fixed-point data to be processed and the first fixed-point data; obtaining a second calculation result according to the fixed-point data to be processed and the second fixed-point data; and obtaining a target calculation result acc

Abstract: A method for training a shape optimization neural network to produce an optimized point cloud defining desired shapes of materials with given properties is provided. The method comprises collecting a subject point cloud including points identified by their initial coordinates and material properties and jointly training a first neural network to iteratively modify a shape boundary by changing coordinates of a set of points in the subject point cloud to maximize an objective function and a second neural network to solve for physical fields by satisfying partial differential equations imposed by physics of the different materials of the subject point cloud having a shape produced by the changed coordinates output by the first neural network. The method also comprises outputting optimized coordinates of the set of points in the subject point cloud, produced by the trained first neural network.

Abstract: An abnormality diagnosis device includes: a first interface to obtain a value of a driving current for driving a motor; and a processor to access a database including calculation data to be used to calculate a degree of abnormality of the motor, wherein the processor extracts a feature quantity for calculating the degree of abnormality from a current waveform specified by a value of the driving current, and calculates the degree of abnormality of the motor based on the extracted feature quantity and the calculation data.

Abstract: The present disclosure provides a system and a method for controlling a motion of a robot from a starting point to a target point within a bounded space with a floorplan including one or multiple obstacles. The method includes solving for an electric potential in a bounded virtual space formed by scaling the floorplan of the bounded space with the one or multiple obstacles and applying charge to at least one bound of the bounded virtual space while treating the scaled obstacles as metallic surfaces with a constant potential value, wherein the electric potential provides multiple equipotential curves within the bounded virtual space. The method further includes selecting an equipotential curve with a potential value different from a potential value of an obstacle equipotential curve, determining a motion path based on the selected equipotential curve, and controlling the motion of the robot based on the determined motion path.

Abstract: A motor assembly comprising a mover assembly and a stator. The mover assembly comprising a plurality of mover assembly units, each unit comprising: a non-ferromagnetic core with a profile of an outer surface having at least two flat sections lying on intersecting planes; at least two ferromagnetic laminated structures arranged on the at least two flat sections of the non-ferromagnetic core, each ferromagnetic laminated structure includes a stack of ferromagnetic plates, each plate is covered with a non-conductive coating. Each ferromagnetic laminated structure is arranged on a corresponding flat section of the at least two flat sections of the non-ferromagnetic core such that side of each ferromagnetic plate is adjacent to the non-ferromagnetic core thereby making the ferromagnetic plates of the at least two ferromagnetic laminated structures lie on intersecting planes.

Abstract: A system for magnetic refrigeration is provided. The system includes a layered structure formed by a sequence of a plurality of magnetocaloric material (MCM) components interlinked with a sequence of a plurality of Peltier modules. Each Peltier module of the plurality of Peltier modules is sandwiched between two MCM components of the plurality of MCM components. The system further includes a power source configured to concurrently power each Peltier module in the sequence of the plurality of Peltier modules. Current in each powered Peltier module flows in a constant direction. The system further includes a magnetic source configured to apply spatially uniform magnetic field to the sequence of the plurality of MCM components.

Abstract: An abnormality diagnosis device includes: a first interface to obtain a value of a driving current for driving a motor; and a processor to access a database including calculation data to be used to calculate a degree of abnormality of the motor, wherein the processor extracts a feature quantity for calculating the degree of abnormality from a current waveform specified by a value of the driving current, and calculates the degree of abnormality of the motor based on the extracted feature quantity and the calculation data.

Abstract: A system and method for motor eccentricity fault detection is disclosed. The method includes extraction of fault-related features through topological data analysis (TDA) for motor current signals and apply them to motor eccentricity fault detection. The method further includes the procedure of obtaining topological features from time-domain data and representing them in persistence diagrams and vectorized Betti sequences. The method further includes the extraction of fault-related features from the obtained topological features of the data, which can be distinctively associated with not only fault type but also fault severity level. Further, the method includes use of machine learning models to extract fault-related features from TDA, for the prediction of motor eccentricity fault level, even for data from new eccentricity levels that are not seen in the training data.

Abstract: The present disclosure provides a system and a method for controlling a motion of a robot from a starting point to a target point within a bounded space with a floorplan including one or multiple obstacles. The method includes solving for an electric potential in a bounded virtual space formed by scaling the floorplan of the bounded space with the one or multiple obstacles and applying charge to at least one bound of the bounded virtual space while treating the scaled obstacles as metallic surfaces with a constant potential value, wherein the electric potential provides multiple equipotential curves within the bounded virtual space. The method further includes selecting an equipotential curve with a potential value different from a potential value of an obstacle equipotential curve, determining a motion path based on the selected equipotential curve, and controlling the motion of the robot based on the determined motion path.

Abstract: A non-intrusive load monitoring method based on V-I trajectory and neural network includes: collecting the household voltage, current and active power data in real time; determining whether there is a switching event and whether the load operating state has reached a steady state through the change of the active power; obtaining the voltage, current and power data of the load, converting the V-I trajectory into RGB color image containing the phase difference between the voltage and current, power and other information. After obtaining the RGB color image, performing normalization processing and performing load monitoring through pre-trained convolutional neural network. The present disclosure fully extracts the steady-state feature of the load through the convolutional neural network, and the neural network model can directly run on an embedded device, and does not need to rely on the computing support of a server.

Abstract: A motor assembly comprising a mover assembly and a stator. The mover assembly comprising a plurality of mover assembly units, each unit comprising: a non-ferromagnetic core with a profile of an outer surface having at least two flat sections lying on intersecting planes; at least two ferromagnetic laminated structures arranged on the at least two flat sections of the non-ferromagnetic core, each ferromagnetic laminated structure includes a stack of ferromagnetic plates, each plate is covered with a non-conductive coating. Each ferromagnetic laminated structure is arranged on a corresponding flat section of the at least two flat sections of the non-ferromagnetic core such that side of each ferromagnetic plate is adjacent to the non-ferromagnetic core thereby making the ferromagnetic plates of the at least two ferromagnetic laminated structures lie on intersecting planes.

Abstract: The present disclosure provides a system and a method for detecting a fault of an operation of a synchronous motor. The method includes collecting an electrical input and measurements associated with the operation of the synchronous motor caused by the electrical input. The method further includes determining sequences of points defining a mutual position between a stator and a rotor of the synchronous motor that results in magnetostatic determined as a weighted summation over real-space basis functions parameterized on pairs of adjoint points in the determined sequence of points and weighted with a surface charge density between corresponding adjoint points, such that the resulted magnetostatic explains the measurements of the operation of the synchronous motor given the electrical input. Further, the method includes determining the fault of the operation of the synchronous motor based on the mutual position between the stator and the rotor of the synchronous motor.

Abstract: An electrical machine that is an outer runner motor having a rotor mounted rotatably about a machine axis, with the rotor rotatively attached to a shaft. A stator assembly having a stator core with a non-ferromagnetic material. An axial-flux yoke with an inner wall rigidly attached on an outer surface of a first edge wall of the stator core, or a radial-flux yoke with a continuous inner wall rigidly attached on a continuous outer wall of the stator core. Wherein the axial-flux yoke or the radial-flux yoke, include laminated sheets and slots. Windings can be positioned in the slots of either the axial-flux stator yoke or the radial-flux stator yoke. Wherein the stator core and the shaft include a spline coupling feature adapted to selectively couple and mate the stator core to the shaft.

Abstract: A motor comprising a shaft, an array of stator assemblies rigidly attached to the shaft, each stator assembly includes a stator yoke having a toroid shape fixed around the shaft and having a number of slots at radial and axis directional faces with windings within the slots of the stator yoke, and a rotor assembly rotatively attached to the shaft to enclose the array of stator assemblies, the rotor assembly has a rotor drum with sections, each section embraces one stator assembly, each section has two axial-flux permanent magnet arrays attached on axial-directional inner surfaces of the section and has one radial-flux permanent magnet array attached on a radial-directional inner surface of the section furthest from the shaft, wherein the axial-flux and the radial-flux permanent magnet arrays with the number of pole pairs equals the number of the stator slots plus or minus the number of stator winding pole pair.