20 results about "Order tracking" patented technology

The invention relates to the technical field of equipment testing, and discloses a motor operation state vibration noise combined diagnosis generation method, which comprises the following steps: when a motor operates, performing bimodal data extraction on operation state data to obtain a vibration signal and a noise signal; performing order tracking analysis on the vibration signal to obtain an electric vibration characteristic order component; calculating a time domain energy envelope line, and performing synchronous time domain segmentation on the noise signal to obtain noise signal slices; filtering non-related components of the vibration characteristic order component in the noise signal slice, and extracting a vibration synchronous noise component of the vibration characteristic order component synchronous with the current rotation period of the motor according to a filtered result; constructing a sound-vibration fusion spectrum; analyzing the distribution rule of the sound and vibration intensity in the sound and vibration fusion spectrum, identifying a correlation mutation point, and diagnosing a specific operation state fault based on the distribution mode of the correlation mutation point; according to the invention, the efficiency of vibration noise combined diagnosis generation of the motor operation state can be improved.

The invention relates to a permanent magnet synchronous motor improved fractional order sliding mode active-disturbance-rejection control method based on an improved fractional order super-spiral sliding mode observer, and the method specifically comprises the steps: firstly, building a mathematical model of a PMSM in an alpha-beta and d-q coordinate system, designing an improved fractional order super-spiral sliding mode disturbance observer, and estimating the unknown disturbance in a system in real time; and finally, optimizing a steepest function and designing a fractional order tracking differentiator in combination with a fractional order calculus principle. A nonlinear state error feedback law is constructed, nonlinear calculation is carried out on the proportion, differential and integral of an error signal, and the anti-interference capability, the response speed and the control precision of the controller are improved; and combining a fractional order tracking differentiator, a fractional order state observer and a nonlinear feedback control law, and designing a fractional order sliding mode active-disturbance-rejection controller. According to the method, system buffeting is effectively weakened while the convergence speed is increased, and the robustness of the controller under load disturbance is effectively improved.

The invention discloses a coal-fired power plant belt conveyor driving gear box bearing fault diagnosis method, which relates to the field of fault diagnosis and evaluation, and is used for extracting gear box bearing fault features based on an order tracking analysis method of tachometer-free instantaneous rotation frequency estimation. The method specifically comprises the following steps: S1, obtaining an instantaneous rotation frequency ridge line through the time frequency distribution of a vibration signal, carrying out the rotation frequency estimation of a rotating shaft, and improving the precision of the instantaneous rotation frequency ridge line through an improved multi-source ridge line fusion algorithm; and S2, extracting bearing fault feature order information by using a calculation order tracking COT algorithm, and adaptively processing a bearing fault diagnosis problem of the driving gearbox of the belt conveyor. According to the bearing fault diagnosis method for the rubber belt conveyor driving gearbox of the coal-fired power plant, the bearing inner ring fault is successfully diagnosed, and the feasibility of a bearing fault diagnosis strategy for the rubber belt conveyor driving gearbox is verified.

The invention discloses a variable-speed working condition rolling bearing fault diagnosis method based on improved wavelet packet transformation and envelope order tracking, and the method specifically comprises the steps: carrying out the equal-angle resampling of a vibration signal according to the phase of a rotating shaft, and carrying out the wavelet packet transformation of an angle domain vibration signal through an improved wavelet packet transformation algorithm, and finally, fault diagnosis is carried out according to the transformed envelope order spectrum of the wavelet packet coefficient of each node. According to the method, time domain signals are converted into angle domain signals through equal-angle resampling, frequency spectrum fuzziness caused by rotating speed changes is eliminated, and the angle domain signals are divided to different order bands through the excellent frequency domain decomposition capacity of an improved wavelet packet transformation algorithm so that sub-signals with the high signal-to-noise ratio can be obtained. And finally, solving the envelope spectrum of the sub-signal to further highlight the fault characteristics. According to the method, the problem of difficulty in fault diagnosis of the variable-speed working condition bearing is solved, meanwhile, the process of determining a high-frequency resonance frequency band by means of a kurtosis graph and extracting a high-frequency resonance signal through band-pass filtering is avoided, and the diagnosis method is simple, efficient and high in accuracy.

The invention discloses a high-precision identification method and system for a noise source of an asynchronous motor, and relates to the technical field of asynchronous motor detection, and the method comprises the following steps: constructing a finite element model of a to-be-analyzed asynchronous motor, dividing the surface into a plurality of detection sub-regions, and carrying out the operation simulation of the finite element motor, analyzing vibration speed and sound pressure signal data of each sub-region, synchronously acquiring actual signals, calculating relative differences with simulation data, evaluating detection priorities by combining historical fault times, forming a detection sequence according to the priorities, and sequentially performing sound-vibration coherence analysis and coherence function calculation to obtain a sound-vibration coherence detection result; according to the method, noise frequency bands are classified into structural noise and air noise, order tracking analysis is performed on the structural noise obtained through classification, an amplitude stability index and an amplitude change gradient are calculated, automatic classification and identification of electromagnetic noise and mechanical noise are realized based on a preset threshold value, and the efficiency and accuracy of fault processing are remarkably improved.

The invention relates to a digital intelligent trajectory prediction method and system based on logistics transportation order tracking. The method comprises the steps of collecting multi-source transportation data including vehicle GPS data, AI telephone interaction data, ETC passing record data and freight station transfer operation node data; performing space-time alignment, data cleaning and map matching based on road network information on the multi-source transportation data; utilizing the processed multi-source data to generate a continuous digital intelligent track through an interpolation algorithm and a path deduction method; calculating the latest predicted position of the vehicle through a prediction algorithm model based on the digital intelligent track in combination with real-time traffic information and a historical driving mode; residual paths, real-time road conditions, weather factors and driver behavior modes are comprehensively considered, and the predicted arrival time is calculated through a dynamic ETA model. According to the invention, the GPS signal blind area problem is effectively solved, the whole-course blind-area-free tracking and high-precision arrival time prediction are realized, and the transparency and intelligent management level of logistics transportation are greatly improved.

The present application relates to a method for detecting loosening of ceramic dewatering elements for papermaking based on vibration spectrum analysis, aiming to solve the problems of difficult early identification of loosening and weak anti-working condition interference ability of ceramic dewatering elements in the running of the paper machine wire section. The scheme synchronously collects vibration signals and multi-dimensional working condition parameters, generates a structured frequency spectrum through time-frequency analysis and power spectrum density processing, and then fuses the knowledge base to match the reference structure template, realizing the topological edit distance quantization of the measured and reference frequency spectrum. Combined with the fuzzy rule engine, the different loosening degrees are dynamically weighted and inferred, and the positioning accuracy is improved through graph structure evolution trajectory analysis and high-resolution order tracking. The technology has high robustness and early loosening identification ability, which is beneficial to improve the intelligentization and intelligent decision level of the health monitoring of the clean production line ceramic elements.

The invention provides a rotary machine vibration signal angular domain sampling method, and relates to the technical field of rotary machine state monitoring and fault diagnosis. The method comprises the following steps: establishing a synchronous data acquisition channel of a rotary encoder and a vibration sensor through a common clock source; establishing a vibration sensor data first-in first-out cache aligned with an encoder channel timestamp on the basis of the synchronous data acquisition channel; angular domain sampling data screening triggered by the edge of the rotary encoder is realized on the basis of vibration sensor data oversampling; and finally, sorting the screened vibration sensor data according to instantaneous angle values to form angular domain sampling vibration signals. According to the method, angular domain equal-angle interval sampling is realized from a data acquisition source, instantaneous rotating speed calculation and time domain signal interpolation are not needed, the problem of signal distortion caused by an interpolation algorithm in a traditional order tracking technology is effectively avoided, the fidelity and analysis efficiency of angular domain vibration signals are remarkably improved, and the method is suitable for large-scale popularization and application. And a reliable data basis is provided for accurate fault diagnosis of the rotating machinery.

This invention discloses a method and system for monitoring equipment status based on vibration signals, relating to the field of mechanical equipment life prediction technology. The invention simultaneously collects vibration and temperature signals; employs adaptive variational mode decomposition to extract degradation-sensitive features; estimates instantaneous rotational speed based on the vibration signal itself, converting it into angular domain signals through order tracking; constructs an overcomplete dictionary for sparse decomposition, automatically determining the first prediction time based on the sparse energy ratio; fuses multimodal features to construct normalized health indicators; uses a temporal fusion prediction network to predict remaining life and generate graded maintenance recommendations; and continuously optimizes the model through incremental learning. This invention, through the synergy of adaptive decomposition, rotational speed-free order tracking, angular domain sparse decomposition, and lightweight networks, achieves accurate remaining life prediction without the need for speed sensors and a large number of fault samples, significantly improving early fault warning capabilities and the reliability of predictive maintenance decisions under varying load conditions.

The invention relates to the technical field of fan bearing fault diagnosis, in particular to a fan bearing fault online detection method and system, which adopts a weighted combination envelope spectrum technology, quantifies the correlation between fault features and resonance frequency bands through two-dimensional spectrum coherence, and adaptively distributes weights in combination with a frequency domain signal-to-noise ratio. The fault information of multiple resonance frequency bands is aggregated and enhanced, the attenuation influence in the signal transmission process is effectively counteracted, and the problem that signal attenuation distortion is caused due to the fact that enclosure magnetic attraction installation is needed under the limitation of the fan structure size is solved. An order tracking and constant angle increment sampling technology is introduced, a corresponding relation between a vibration signal and a bearing rotation angle is established, a time domain signal is converted into an angular domain signal, a fault characteristic frequency and a rotation speed are decoupled, and it is ensured that theoretical fault frequencies of parts such as an inner ring, an outer ring and a roller are accurately matched with a detection result. Compared with fixed frequency domain analysis of a traditional acceleration enveloping method, the method has the advantage that misjudgment caused by frequency deviation is effectively avoided.

The present application relates to the technical field of fan bearing fault diagnosis, in particular to a fan bearing fault online detection method and system, adopting a weighted combination envelope spectrum technology, quantifying the correlation of fault characteristics and resonance frequency bands through two-dimensional spectrum coherence, combining the frequency domain signal-to-noise ratio to adaptively assign weights, aggregating and enhancing the fault information of multiple resonance frequency bands, effectively offsetting the attenuation effect in the signal transmission process, solving the problem of signal attenuation distortion caused by magnetic attraction installation through the machine shell under the limitation of fan structure size. The order tracking and constant angle increment sampling technology is introduced, the corresponding relationship between the vibration signal and the bearing rotation angle is established, the time domain signal is converted into the angle domain signal, the fault characteristic frequency and the rotating speed are decoupled, the theoretical fault frequency of the inner ring, the outer ring, the roller and other components is accurately matched with the detection result, the detection adaptability under variable working conditions is greatly improved, compared with the fixed frequency domain analysis of the traditional acceleration envelope method, the misjudgment caused by frequency offset is effectively avoided.

Systems and methods that use a trained multi-tenant model are disclosed herein. In an embodiment, a method of using a trained multi-tenant model in a workspace includes receiving a first video feed including a workstation and a second video feed including a completed item station, building a worker utilization inference result queue from a first asynchronous inference thread that runs a human pose estimation model and a clothing color extraction model using sequential frames from the first video feed, building an order tracking inference result queue from a second asynchronous inference thread that runs an object detection model and an item tracking model using sequential frames from the second video feed, and merging the worker utilization inference result queue and the order tracking inference result queue to generate a real-time indication of activity at one or more workstations shown in the first video feed or the second video feed.

The application discloses a multi-source time-frequency ridge extraction method based on synchronous extraction transformation, and comprises the following steps: acquiring a gearbox time-domain vibration signal; performing variational modal decomposition filtering on the time-domain vibration signal to obtain a characteristic modal component time-domain vibration signal; performing synchronous extraction transformation time-frequency analysis on the characteristic modal component time-domain vibration signal; extracting a rotating shaft rotating frequency and a gear meshing frequency time-frequency ridge according to a local peak value search algorithm; optimizing the time-frequency ridge based on a multi-source ridge fusion method to extract a rotating frequency curve; and tracking the order of the characteristic modal component time-domain vibration signal based on the rotating frequency curve to obtain a rotating shaft fault characteristic order from an angular domain order spectrum. The method improves the frequency mutation condition and has high IF fusion precision, obtains the fault characteristic order from the order spectrum of the signal after order tracking, improves the frequency ambiguity phenomenon, and optimizes the bearing fault characteristic extraction effect.

The invention discloses a fractional order active disturbance rejection control method for a fractional order chaotic system, and the method comprises the following steps: constructing a mathematical model of a fractional order Lorenz chaotic system according to a fractional order calculus theory; designing a fractional order tracking differentiator in order to accurately extract fractional order differential information of a system state variable; designing a fractional order expansion state observer for observing the total disturbance of the fractional order chaotic system; comparing the output of the fractional order expansion state observer with the output of the fractional order tracking differentiator to obtain an error signal, and designing a fractional order PID controller; and designing a fractional order active disturbance rejection controller to realize stable control of the fractional order Lorenz system. The fractional order active-disturbance-rejection control method for the fractional order chaotic system effectively makes up for the defects of the existing control technology, and remarkably improves the control precision, the anti-interference capability and the chaos phenomenon suppression capability of the fractional order chaotic system.

The invention discloses a rotating part circumferential matrix characterization method and system based on rotating speed super-resolution correction, and relates to the field of rotating machinery fault diagnosis, and the method comprises the steps: collecting an original vibration signal of a rotating part, carrying out the synchronous compression wavelet transformation, extracting a rotating frequency ridge line, and obtaining a preliminary estimation rotating frequency sequence; performing equal-angle resampling on the original vibration signal through the preliminary estimation frequency conversion sequence by using a keyless phase order tracking technology, so that the equal-time interval signal is converted into an equal-angle resampling signal; performing pulse ridge line extraction on a circumferential matrix formed after equal-angle resampling by using a sparse pulse ridge point dynamic planning tracking algorithm; correcting the sub-pixel-level super-resolution of the initial rotating speed to obtain a corrected rotating speed signal; and carrying out secondary angular domain resampling by using the corrected rotating speed signal to generate a circumferential characterization map capable of characterizing fault space-time distribution characteristics. According to the method, the rotating speed is finely corrected, and the interpretability and the identification degree of two-dimensional representation can be improved.

The invention discloses a wheel assembly comprehensive quality intelligent detection method based on vibration analysis, and the method comprises the steps: arranging a high-precision vibration sensor and a photoelectric encoder near a hub and a bearing seat, and synchronously collecting multi-channel vibration signals and rotating speed information; de-noising is carried out by adopting wavelet transform or EMD, equal-angle resampling is realized based on order tracking, and the problem of frequency spectrum fuzziness under the variable speed working condition is eliminated; aiming at quality dimensions such as dynamic balance, bearing state, bolt looseness and tire uniformity, extracting targeted characteristics such as 1 * / 2 * rotation frequency amplitude, fault characteristic frequency envelope spectrum, kurtosis and impact pulse factor; training a multi-task intelligent diagnosis model in combination with the labeled data set, synchronously outputting defect types, severity and 0-100 score comprehensive quality scores, and introducing a confidence coefficient mechanism to trigger manual reinspection; the linkage production line realizes automatic rejection of unqualified products, forms a'detection-judgment-disposal 'closed loop, and remarkably improves the detection efficiency and the quality control level.

A bondless phase-order tracking method based on deep time-frequency refinement includes: 1) constructing several non-stationary simulated vibration signals; 2) constructing an ideal time-frequency binary mask; 3) constructing a multi-scale one-dimensional complex-valued convolution module to perform generalized time-frequency transformation on the non-stationary simulated vibration signals to obtain an initial two-dimensional time-frequency representation; 4) constructing a deep time-frequency refinement model, using the initial two-dimensional time-frequency representation as input and the ideal time-frequency binary mask as supervision labels to construct a dataset; 5) inputting the initial two-dimensional time-frequency representation into the deep time-frequency refinement model to obtain prediction data, and back-optimizing the deep time-frequency refinement model based on a loss function until a trained deep time-frequency refinement model is obtained; 6) acquiring one-dimensional vibration signals during the actual operation of rotating machinery and inputting them into the trained deep time-frequency refinement model to obtain a refined time-frequency representation, thereby obtaining the order tracking result. This invention provides a more stable and efficient signal analysis method.

The embodiment of the invention discloses a multi-microgrid interconnection system event triggering load frequency control method for communication time delay. A specific embodiment of the method comprises the following steps: constructing a time-lag-containing multi-microgrid interconnection system model according to a time-lag-containing multi-microgrid interconnection system; discrete nonlinear transformation is carried out on the multi-microgrid interconnection system model containing the time delay so as to generate a multi-microgrid nonlinear equation containing the time delay; performing dynamic linearization on the time-delay-containing multi-microgrid nonlinear equation to obtain a time-delay-containing multi-microgrid linear equation; according to the multi-microgrid linear equation containing time delay and a preset second-order tracking differentiator, generating a trigger error; and in response to determining that the triggering error meets the preset event triggering mechanism, determining a time-varying parameter and a control input quantity corresponding to the triggering error based on a preset data driving load frequency control algorithm. According to the embodiment, automatic control of the power system is achieved, time lag and disturbance can be effectively processed, the communication frequency is reduced, frequency deviation convergence is fast, and fluctuation is small.

The invention relates to the technical field of strategy optimization, in particular to an assembly tool health degree evaluation method based on tightening data, which comprises the following steps: acquiring multi-source data such as torque, angle and current in a fastening process, identifying a process mode switching point by using a reinforcement learning sampling model, and realizing adaptive adjustment of sampling frequency; performing order tracking processing on the current sequence, calculating mutual information in combination with vibration features, and extracting residual feature vectors; constructing the multi-dimensional features into a symmetric positive definite matrix manifold, projecting the symmetric positive definite matrix manifold to a Riemannian tangent space, eliminating global distribution offset caused by environmental fluctuation through manifold alignment operation, and obtaining an alignment feature vector; and inputting the alignment vector and the environment observation value into a reinforcement learning evaluation model, outputting a correction coefficient according to the geodesic distance to execute error compensation, and outputting a health level result. According to the method, through multi-dimensional calibration and physical constraint, high-confidence robust grading of the tool health state under the heterogeneous material fastening working condition is achieved.

The application provides a signal processing method, a rotating equipment monitoring method, device, equipment and medium, wherein the signal processing method comprises: obtaining vibration data in a running state of a rotating equipment, the vibration data comprising a time domain signal and a revolution phase sequence; performing equiangular phase inverse mapping on the revolution phase sequence to obtain a target time sequence corresponding to a uniform angle; resampling the time domain signal according to the target time sequence by using a limited support band-limited reconstruction kernel to obtain an angular domain signal; performing angular domain spectrum estimation on the angular domain signal to obtain an angular domain spectrum; and extracting a target order amplitude based on the angular domain spectrum. Through equiangular phase inverse mapping, the requirement of angular domain uniform sampling is converted into sampling at specified target time points in the time domain. Under the premise of controlling the amount of calculation by using a limited support, resampling is performed by using a band-limited reconstruction kernel to obtain an angular domain signal with high fidelity in sampling structure and amplitude, thereby ensuring high precision and high robustness of order tracking results.