1100 results about "Mechanical system" patented technology

The invention belongs to the technical field of signal processing, and particularly relates to a multi-source data fusion fault diagnosis method and system based on the DS evidence theory, and the method comprises the steps: firstly collecting the multi-source information of mechanical equipment, including vibration, image and sound data, respectively calculating the corresponding characteristic indexes, carrying out the dimension reduction processing of the indexes through employing a PCA algorithm, and carrying out the fault diagnosis of the multi-source information. And carrying out fault diagnosis by adopting a Bayesian fault diagnosis model, and carrying out fault fusion diagnosis by adopting a DS evidence theory after a diagnosis result of each sensor is obtained. The method can comprehensively consider the characteristics of various data, extract more effective fault indexes, realize more comprehensive fault identification, fuse the diagnosis results of a plurality of sensors, ensure the final diagnosis accuracy, and is suitable for fault diagnosis of various complex mechanical systems.

The invention belongs to the technical field of a computer system based on a specific calculation model, relates to a virtual and real dynamic interaction-based digital twin bearing fault diagnosis method, and aims to solve the problem that a bearing system in large rotary mechanical equipment is large in fault hidden danger and avoid serious consequences caused by faults. A generally used bearing is in a high-precision standard, so that system signal data under bearing fault operation is not easy to collect in a factory, and the unbalanced data sample distribution directly leads to finiteness of fault diagnosis in a real scene only based on a big data driving mode classification method, so that the system signal data cannot be easily collected in the factory. The method has good scene working condition self-adaption, can be applied to fault diagnosis of a bearing system in different industrial scenes, fully excavates limited industrial data information and rich physical priori knowledge, and ensures safe and reliable operation of an important rotating mechanical system through a digital twinning technology.

The invention discloses a roadway cleaning robot autonomous navigation and operation system based on multi-sensor fusion. The roadway cleaning robot autonomous navigation and operation system comprises a mechanical system, a control system and a power system. The mechanical system comprises a crawler chassis, a mechanical arm and a big arm turntable; the control system software part presentation layer comprises a user interaction and management module, the business layer comprises a task planning and decision module, and the function layer comprises a robot autonomous walking control module, a roadway deformation detection module, a mechanical arm accessory replacement control module and a mechanical arm roadway cleaning control module. The sensing layer comprises an infrared person detection module, a laser radar mapping positioning module, a mechanical arm visual positioning module and a mechanical arm state feedback calculation module; the hardware part comprises a laser radar, an IMU, a sensor and the like. Various sensor data are fused, accurate positioning, autonomous walking, mechanical arm visual positioning, roadway deformation detection, accessory automatic replacement, roadway automatic cleaning and the like of the robot are achieved, and the intelligent level and the working efficiency of the robot are improved.

The invention discloses an assembly geometric digital twin modeling and pose calculation method based on deep learning, and belongs to the technical field of precision mechanical system modeling. Comprising the following steps: firstly, selecting a small number of product key contact surfaces for point cloud measurement; secondly, performing redundancy removal, expansion and arrangement on the data by adopting an up-sampling / down-sampling and data enhancement method; then, an assembly data set is established through assembly pose parameter calculation and data enhancement; then training a neural network by using a deep learning technology, and establishing a geometric distribution error and assembly pose mapping model; according to the method, part surface geometric distribution errors and assembly pose errors in an assembly body are considered, a geometric digital twin model with complete and accurate error information is established, and a model basis is provided for product precision prediction, product assembly process optimization, product assembly visualization and product quality improvement; and the method is suitable for precision mechanical products with high precision and large-batch production requirements.

The invention discloses an intelligent image classification method based on big data, and relates to the technical field of image classification, and the method comprises the steps: image feature extraction: employing a deep learning algorithm to integrate and classify the extracted features, the method has the advantages that information of two different physical quantities can be integrated into a visual representation by constructing a vibration and temperature data fusion time-frequency diagram, and vibration data generally reflect dynamic characteristics of a mechanical system, such as running balance of a bearing, collision or friction between parts and the like; temperature data is related to energy loss, heat conduction, potential fault heating and the like, associated information of mechanical vibration and heat change in time and frequency can be captured at the same time through fusion of a time-frequency diagram, a more comprehensive view angle is provided for equipment state monitoring and fault diagnosis, and when a fault occurs, the fault diagnosis accuracy is improved. Vibration and temperature signals often change at the same time, and when a bearing has a local abrasion fault, the vibration amplitude may be increased.

The invention discloses an unmanned platform electromechanical system fault early warning method based on a Transform-BiGRU cross attention mechanism, and belongs to the technical field of fault early warning. The method comprises the following steps: acquiring parameters, and performing time sequence alignment and key feature screening; secondly, designing a parallelization spatio-temporal feature extraction framework: on one hand, capturing a long time sequence dependency relationship through a multi-layer Transform encoder, on the other hand, realizing bidirectional context modeling of a sequence by adopting a bidirectional gating circulation unit, and introducing an interpretable global attention mechanism to perform feature weight dynamic allocation on a BiGRU hidden layer state; and a cross attention feature fusion module is constructed for a Transform and BIGRU fusion problem, and cooperative characterization of a time sequence dynamic feature and a space correlation feature is realized through interactive attention calculation of a spatial-temporal feature tensor. By means of the method, advanced early warning from diesel engine lubricating oil pressure abnormity to whole ship electric power system cascade faults is achieved.

The present invention belongs to the technical field of signal processing, and particularly relates to a multi-source data fusion fault diagnosis method and system based on the DS evidence theory. The method of the present invention comprises: first, collecting multi-source information of a mechanical device, wherein the multi-source information includes vibration data, image data and sound data; respectively calculating corresponding feature indicators; then, using a PCA algorithm to perform dimensionality reduction processing on the indicators; using a Bayesian fault diagnosis model to perform fault diagnosis, so as to obtain a diagnosis result from each sensor; and then, using the DS evidence theory to perform fault fusion diagnosis. The present invention can comprehensively consider the characteristics of various types of data, extract more effective fault indicators, achieve more comprehensive fault identification, and fuse diagnosis results from a plurality of sensors, thereby ensuring the accuracy of final diagnosis. The present invention is suitable for fault diagnosis of various complex mechanical systems.

A modular combination axe head with a mechanical system that allows multiple configurations of other modular attachments; such as a sledge hammer and pick that is particularly designed for use by firefighters or military as a tactical entry tool. The axe includes a front end and a rear end. The front end has an upper section that functions as an axe blade and the lower section functions as a brush hook or. The rear section includes a pick that can be utilized for a variety of functions. By incorporating three tools in a single structure the weight that a firefighter carries is lessened. Thus, reducing fatigue and the time that is required to cut through an obstruction to reach fire victims.

The invention discloses a micro-cantilever geometric structure parameter inversion system, method and device, and belongs to the technical field of micro-electro-mechanical systems. According to the system, a dynamic model containing geometry, material and electromechanical coupling nonlinearity is constructed, a PINNs network fused with physical constraints is designed, a staged training optimization strategy is adopted, and efficient inversion of micro-cantilever geometric parameters is achieved. The core of the method is that a nonlinear kinetic equation is used as a constraint to be embedded into a neural network for training, experimental data and physical residual errors are combined to construct a composite loss function, and the problems that a traditional method neglects a nonlinear effect, depends on finite element simulation and is low in inversion precision are solved. The average relative error of inversion of the system is lower than 1%, which is obviously superior to that of a traditional method, and the method can be widely applied to MEMS device design, online detection and closed-loop control scenes.

A fluid distribution system is disclosed herein for mechanical systems, such as vehicle transmissions with forced lubrication. The fluid distribution system may comprise: a fluid distribution system, comprising: a fluid distribution device positioned in a hollow shaft comprising a coaxial passage and a plurality of radial channels, the fluid distribution device comprising: a cartridge coaxial with the hollow shaft, comprising a first circular side having a coaxial inlet opening and a second circular side having a plurality of tube openings positioned equidistantly from an axis of rotation of the hollow shaft; and a plurality of pipes, each pipe extending from one of the plurality of tube openings parallel to the axis of rotation of the hollow shaft, where a length of each pipe corresponds to axial positions of the plurality of radial channels axially spread apart along the hollow shaft.

The invention relates to the technical field of mechanical vibration and fluid excitation coupling effect analysis, in particular to a runner crown modification pressure pulsation amplitude measurement method, which comprises the following steps of: extracting pressure gradient distribution and cavitation phase change characteristic parameters of a runner surface through fluid dynamics simulation; targeted arrangement of a high-frequency pressure sensor array in a vortex core area, a boundary layer separation critical position and a modification structure mutation area is guided, and the signal acquisition precision is improved by adopting an annular encryption and axial equal-interval arrangement strategy. And establishing a dynamic error feedback mechanism of actual measurement and simulation. And verifying an abnormal frequency band proportion through frequency domain kurtosis and analyzing envelope spectrum harmonic waves, and screening a modification parameter combination with the pressure pulsation amplitude decreasing to reach the standard and stable frequency band distribution. According to the method, the problem of measurement errors caused by multi-source high-frequency noise and effective signal frequency domain aliasing is solved, a high-precision quantification basis is provided for runner modification optimization, and the method is suitable for the field of mechanical system dynamic characteristic testing.

A state signal generation unit that detects the state of a mechanical apparatus as a state signal, a condition signal generation unit that generates a condition signal by detecting the operating status of the mechanical apparatus as an operating condition, a state feature generation unit that generates state features from the state signal, a condition feature generation unit that generates condition features from the condition signal, an initial state learning unit that executes learning based on initial learning state features and outputs initial state learning results, an initial condition learning unit that executes learning based on initial learning condition features and outputs initial condition learning results, an anomaly degree calculation unit that calculates the degree of anomaly based on state learning results and detection state features, and an unknownness degree calculation unit that calculates the degree of unknownness based on condition learning results and detection condition features are included.

A diagnostic method localizes technical faults in a motion system that includes a base adapted to receive a motion stage for equipment, a machine frame resting on the floor, dampers adapted to support the base, and an active isolation system arranged between the base and the machine frame. The active isolation system and the base form a mechanical system. The active isolation system includes actuators, adapted to impart six degree-of-freedom (DOF) motion to the base in a reference frame, and inertial sensors adapted to provide a six DOF measurement of the base's motion. The method includes: i) applying a control signal for actuating or contributing to the actuation of the actuators of the active isolation system to impart a motion to the base; ii) obtaining, with the inertial sensors, a six DOF measurement of the base's motion relative to a reference point; iii) creating a measured process sensitivity matrix of the mechanical system using the six DOF measurement; and iv) determining, based on the measured process sensitivity matrix, whether all the actuators and sensors of the active isolation system are working as expected and / or whether there is a pivot point impeding the movement of the base.

Embodiments of the present disclosure may include a method of training an electro-mechanical system that includes local trainable AI, the method including training a local AI for a controller of the electro-mechanical surgical device or system with simulated data. Embodiments may also include performing a task or set of tasks or operations with the device or system as controlled, instructed, influenced, or the like by the controller having the local AI system. Embodiments may also include collecting real-life data from the step of performing. Embodiments may also include further training the local AI system and improving the AI simulated data generator with real-life data. Another embodiment relates to a system for providing a user with suggestions during social interactions. Embodiments of the invention may extend across many types of devices and systems, control systems, sensors, types of AI, and so forth, and combinations of the foregoing.

The invention provides a rotating mechanical system performance evaluation and optimization method and system based on system negentropy conversion rate, and the method comprises the following steps: carrying out the numerical simulation of a flow field of a rotating mechanical system through a computational fluid mechanics method, and obtaining the parameters of the flow field; calculating a viscous dissipation entropy yield, a turbulent dissipation entropy yield and a wall entropy yield in the fluid domain based on the flow field parameters; the total entropy yield of the system is obtained through the volume fraction and the wall area fraction of the whole fluid domain; converting the input power of the rotating machinery into an equivalent negentropy flow, and calculating the negentropy conversion rate of the system for evaluating the effective utilization degree of the input power; and evaluating the performance of the rotating machinery system based on the negentropy conversion rate, and identifying a key area of energy loss. According to the method, the negentropy conversion rate result is applied to structural design optimization, running state diagnosis and energy efficiency adjustment of the rotating machinery, and online calculation and dynamic visualization can be realized in combination with real-time monitoring data.

The invention discloses a milling chatter diagnosis method based on multi-step differential entropy, and belongs to the technical field of mechanical system fault diagnos.The method comprises the operation steps that the type and the installation position of a sensor are selected, and chatter diagnosis parameters are determined; acquiring synchronous vibration data X; the difference step number mu is set to be equal to 1, the embedded dimension is m, the time delay is tau, and the following calculation is repeated until the difference step number mu is larger than the maximum difference step number mu T; reconstructing a phase space P [mu] of the synchronous vibration data X; processing the phase space P [mu] according to a difference rule to obtain a phase space P '[mu]; calculating the cosine similarity between each two adjacent rows of data in the phase space P 'mu; the interval [-1, 1] is divided into epsilon intervals at equal intervals, probability distribution of the obtained cosine similarity in each interval is counted, and mu step difference entropy is calculated; making the difference step number mu = mu + 1 and the embedded dimension m = m0 + (mu-1), and returning to continue to calculate the mu step difference entropy; determining a flutter type according to a flutter diagnosis criterion; the method can effectively overcome the inherent defects of traditional entropy, remarkably improve the accuracy, timeliness and reliability of flutter diagnosis, provide solid technical support for flutter research and recognition in the machining process, practically improve the machining efficiency, reduce the influence of flutter on the machining quality and reduce the production cost. Besides, the method is highly in line with the trend of intelligent development of the manufacturing industry, has wide application prospects, and is expected to promote technical innovation and progress of the machining industry.

The invention discloses a bearing fault detection method based on an auto-encoder reconstruction error. The method belongs to the technical field of mechanical system fault detection, and comprises the following operation steps: selecting a sensor type, determining an installation position and setting related parameters; acquiring one-dimensional synchronous vibration data of k groups of bearings under normal working conditions, and respectively preprocessing the one-dimensional synchronous vibration data; training and testing an auto-encoder model by using the preprocessed data; obtaining one-dimensional synchronous vibration data of the real-time working condition of the bearing, and performing preprocessing; reconstructing the preprocessed real-time working condition data by using the trained auto-encoder model, and calculating a reconstruction error E; judging whether a fault occurs or not; according to the bearing fault detection method based on the self-encoder reconstruction error, the bearing fault can be effectively and reliably detected; the invention belongs to an artificial intelligence algorithm, and needs less manual expert knowledge; when the self-encoder model is trained, only the data in the healthy state of the bearing is used, and the problem of insufficient data volume during model training is solved.

The invention provides a multi-pump zero-displacement hydraulic agile control system and a control method thereof. The system is composed of a hydraulic system, a mechanical system and an electric control system. According to the control method, a multi-pump zero-displacement switching mechanism and a deep reinforcement learning strategy based on an Actor-Critic architecture are fused, the effective oil supply amount is adjusted by dynamically starting and stopping part of hydraulic pumps, a servo motor rotating speed instruction is output in real time in combination with a neural network, and the strong coupling dynamic relation between sliding block displacement and the angle of a swing rod is coordinated. The system switches part of the pumps to a bypass state when approaching balance so as to reduce energy consumption, and starts all the pumps to guarantee recovery capacity when greatly deviating. According to the technical scheme, on the premise of not depending on an accurate mathematical model, agile, stable and energy-saving control over the inverted pendulum type under-actuated system can be achieved.

The invention provides a transmission system gear fault diagnosis method based on incremental learning, and the method comprises the steps: firstly collecting transmission system monitoring data through a sensor, constructing an incremental data set, and dividing the incremental data set into fault diagnosis tasks of different stages; constructing an initial model, and training the model based on the initial fault data; in the test stage, a generalized entropy index is defined to judge whether the fault is a new fault; when new fault type data appears, a small number of samples are randomly selected from the old type data and fused with the new type data to construct a training set, and in order to avoid forgetting of the model to the old type fault data, knowledge distillation is carried out on attention weight and old type prediction output during training; and when the generalized entropy index continues to judge that an unknown fault occurs, repeating the network training step to update the model, and finally realizing incremental learning of a fault mode. According to the method, continuous updating of the fault diagnosis model can be realized for streaming data, the state monitoring capability of the transmission system is improved, and a guarantee is provided for the service safety of a mechanical system.

The invention relates to the related technical field of fault prediction and health management, in particular to a cutting machine fault monitoring method and system fusing vibration and current signals, and the method comprises the steps: obtaining equipment feature parameters, determining a first fault sensitive feature set and a second fault sensitive feature set, and carrying out the correlation verification through combining historical fault data with a tool replacement log, and correcting the deviation of the fault monitoring evaluation function to obtain a fault monitoring evaluation result, and generating a fault monitoring report. The technical problems that the coupling operation state of a mechanical system and an electrical system cannot be completely represented only by depending on a single vibration or current signal, a dominant fault mode is difficult to accurately recognize when a cutting machine has multi-source degradation factors, and fault false alarm and fault missing alarm are caused are solved, first and second fault sensitive feature sets are determined, the fault type recognition precision is enhanced, and the working efficiency is improved. The evaluation result is verified and corrected through historical data and a cutter log, the deviation from the real state of the equipment is reduced, and the technical effects of timeliness and accuracy of operation state sensing and fault early warning of the cutting machine are guaranteed.

The invention relates to the technical field of optical-mechanical system glue application, and discloses a system and a method for testing glue curing shrinkage stability of an optical-mechanical system of a laser machine. The system comprises a pressing and fixing device which comprises a spring, a pull rod, a needle gauge, a pressing piece and a fixing block, and position switching of the needle gauge between the inner surface and the outer surface of the fixing block is achieved by rotating the pull rod head corresponding to the pull rod; the glue curing unit comprises a UV curing device, a precise temperature control heating platform and a moisture curing environment generator; the light source unit comprises a laser light source and a stable support structure with a clearance fit hoop; the optical machine gluing unit comprises a mirror base and a reflecting mirror; the pressing and fixing device is rigidly connected with the optical platform through the aluminum profile portal frame; the data acquisition unit comprises a CCD (charge coupled device) sensor, a three-dimensional adjustable fixing device and an analysis module, and the analysis module generates a multi-dimensional stability index by resolving the time sequence data of the coordinates of the reflected light spots acquired by the CCD sensor in real time so as to complete the glue curing shrinkage stability test.

The invention discloses a mechanical system structure health monitoring method and device based on hierarchical symbol transfer entropy, and relates to the technical field of data processing, and the method comprises the steps: collecting the vibration data of a plurality of channels of an electromechanical system in different health states; calculating a hierarchical symbol transfer entropy of each sub-channel vibration signal in the plurality of channel vibration data; performing superposition calculation on the level symbol transfer entropy of each level of each sub-channel vibration signal to obtain a second-order tensor feature corresponding to each sub-channel vibration signal; superposing the second-order tensor features corresponding to the vibration signals of each sub-channel to obtain second-order tensor feature matrixes corresponding to the vibration data of the multiple channels; training the initial two-dimensional extreme learning machine based on the second-order tensor feature matrix belonging to the training data set to obtain a trained two-dimensional extreme learning machine; the health state of the electromechanical system is monitored based on the two-dimensional extreme learning machine, and the accuracy of monitoring the health state of the electromechanical system is improved.

The present invention provides a low-cost, non-invasive device (100) for measuring flow and leakage detection of fluids within closed conduits. The invention utilizes affordable a Micro-Electro Mechanical System (MEMS) based Inertial Measurement Units (IMUs) sensor (102) in the device to non-invasively measure fluid flow and leakage of fluids which exhibit turbulence or flow-induced-vibrations within a conduit. This device fin directly estimates flow rate or velocity based on the concept of Reynolds number in fluid dynamics. Further, this device is easy to install on pipes of all dimensions, requires minimal calibration, has no moving parts in contact with the fluid, can be integrated with Geographic Information System (GIS) software, and possesses advanced metering infrastructure to support remote reading and automated bill generation.

Transportation systems have artificial intelligence including neural networks for recognition and classification of objects and behavior including natural language processing and computer vision systems. The transportation systems involve sets of complex chemical processes, mechanical systems, and interactions with behaviors of operators. System-level interactions and behaviors are classified, predicted and optimized using neural networks and other artificial intelligence systems through selective deployment, as well as hybrids and combinations of the artificial intelligence systems, neural networks, expert systems, cognitive systems, genetic algorithms and deep learning.

The invention relates to a nonlinear damping element based on a Modelica language, and relates to the field of mechanical system modeling and simulation. According to the technical scheme, a novel damping element is designed based on the Modelica language, corresponding damping coefficients can be defined according to different movement speeds of the element, and the limitation of the prior art is effectively broken through; specifically, according to the technical scheme, the element types of the Modelica mechanical library are enriched, the blank of nonlinear damping elements in the library is filled up, and the application range of the library is expanded; moreover, simulation requirements of a nonlinear damping system can be accurately matched, so that the modeling process better fits nonlinear characteristics of a damping coefficient in an actual working condition; besides, free switching between non-linear damping and linear damping can be flexibly realized depending on a speed-damping force two-dimensional number table, the adaptability of the element to different modeling scenes is greatly enhanced, and powerful support is provided for precise modeling of a mechanical system based on Modelica.

The present application discloses a winding and unwinding control method, a winding and unwinding control apparatus, a control device, and a storage medium. The method can be applied to a control device in a mechanical system, and the mechanical system further comprises a winding and unwinding motor. The method comprises: acquiring the real-time winding diameter of a winding and unwinding motor; on the basis of the real-time winding diameter and a preset expected linear velocity, determining a target angular velocity of the winding and unwinding motor; and on the basis of the target angular velocity, controlling the winding and unwinding motor to operate. By means of the solution of the present application, the linear velocity of winding and unwinding can be kept stable, so that synchronization of winding and unwinding is guaranteed, thereby reducing the situation of shutdown of a winding and unwinding machine.

The invention discloses a fault analysis method, device and equipment for a rotary drilling rig and a medium, and belongs to the technical field of engineering machinery technologies. The fault analysis method for the rotary drilling rig comprises the following steps: acquiring equipment parameters of the rotary drilling rig, and respectively establishing corresponding mathematical models for a plurality of functional systems of the rotary drilling rig according to the equipment parameters; wherein the functional system comprises a mechanical system, a hydraulic system, a control system and a power system; establishing a model library by utilizing the mathematical model; the simulation models corresponding to all the function systems are connected based on the model library, and a digital prototype of the rotary drilling rig is obtained; the digital prototype is controlled to operate according to multiple fault working conditions, and the component energy loss of the digital prototype under each fault working condition is obtained; and establishing a mapping relationship between the energy loss of the component and the fault type of the fault working condition, and performing fault analysis on the rotary drilling rig according to the mapping relationship. The fault analysis precision and efficiency of the rotary drilling rig can be improved.

Provided is a process that includes obtaining data indicative of state of a dynamic mechanical system and an environment of the dynamic mechanical system, the data comprising a plurality of channels of data from a plurality of different sensors including a plurality of cameras and other sensors indicative of state of actuators of the dynamic mechanical system; forming a training set from the obtained data by segmenting the data by time and grouping segments from the different channels by time to form units of training data that span different channels among the plurality of channels; training a metric learning model to encode inputs corresponding to the plurality of channels as vectors in an embedding space with self-supervised learning based on the training set; and using the trained metric learning model to control the dynamic mechanical system or another dynamic mechanical system.

A mechanical system includes a curved beam and a motor coupled to the curved beam. The curved beam is configured to buckle at two different locations along the positive and negative portions of its load / displacement curve, corresponding to opposite and equal sense bending directions. The motor is configured to impart a flapping motion to the curved beam.

The invention discloses a boat hanging frame mechanical system fault diagnosis method and system, a program product and a computer readable storage medium. The method comprises the following steps: acquiring vibration data of a boat hanging frame mechanical system in a normal-temperature environment and a low-temperature environment; performing noise reduction processing on mechanical vibration data in a normal-temperature environment and a low-temperature environment by a multi-domain fusion self-adaptive fault feature enhancement noise reduction method; dividing the denoised data into a training set, a verification set and a test set; training set data is transmitted to a fault diagnosis model, and normal temperature environment mechanical vibration data features after noise reduction and low temperature environment mechanical vibration data features after noise reduction are extracted; respectively aligning the extracted normal-temperature environment mechanical vibration data characteristics and the extracted low-temperature environment mechanical vibration data characteristics on a plurality of scales by using a multi-layer category perception multi-core adaptive maximum mean difference method; data features are transmitted into a classifier for fault classification, model parameters with the best performance on a verification set are taken, and the effect and performance of the model are checked on a test set.