117 results about "Vibration signal analysis" patented technology

The invention provides a machine vision-based heavy-load large holding tool driving wheel multi-mode damage detection method, and relates to the technical field of automatic detection, and the method comprises the steps: collecting the image, vibration and temperature data of a driving wheel, extracting the visual features of the image through a multi-scale pyramid network, and obtaining the multi-mode damage of the driving wheel; inputting a cross-modal feature fusion network in combination with eccentricity and runout features of the vibration signals and stress concentration features of the temperature data to obtain fusion features; inputting the fusion features into a multi-task detection module to identify the damage, and measuring the damage area, depth, crack growth rate and stress concentration degree based on image segmentation, a photometric stereo method, vibration signal analysis and temperature gradient analysis; and finally, in combination with the equipment operation condition and the damage evolution law, an improved evidence theory is adopted to fuse the evaluation indexes, and an evaluation report including the damage state, the development trend and the maintenance suggestion is generated.

The invention discloses a grinding machine internal part temperature anomaly detection method based on vibration signal analysis, which comprises the following steps that grinding data are collected through sensor deployment, and the sensors comprise a temperature sensor, a vibration sensor, an infrared thermal imaging sensor, a magnetic resistance current sensor and an inductance type particle sensor; carrying out preprocessing and feature extraction on the collected data; model construction and training are carried out based on data of preprocessing and feature extraction; according to the method, the abnormal condition of the temperature of the part is predicted by detecting the vibration signal of the internal part, the content of metal particles in lubricating oil is detected through the oil analysis sensor, and the abrasion degree of the bearing is judged in combination with the vibration signal. And motor current harmonic characteristics are monitored, and overload or rotor imbalance problems are identified.

The invention relates to the technical field of magnetic bias monitoring, in particular to a real-time monitoring method and system for direct-current magnetic bias of a transformer. The method comprises the following steps: acquiring a primary side current signal and an iron core vibration signal of the transformer; analyzing the zero-flux closed-loop characteristic of the primary side current signal of the transformer and performing temperature drift elimination on the primary side current signal of the transformer to generate a primary side optimization signal of the transformer; performing magnetostrictive vibration noise separation on the iron core vibration signal to generate an iron core vibration separation signal; dynamically filtering a power frequency fundamental wave of the primary side optimization signal of the transformer to extract a pure direct current component; and performing wavelet packet decomposition on the iron core vibration separation signal, and performing magnetostriction characteristic spectrum extraction on the decomposed iron core vibration separation signal to obtain an iron core magnetostriction characteristic spectrum. According to the invention, through multi-source signal fusion and material characteristic modeling, the accuracy, real-time performance and graded protection response capability of transformer DC magnetic bias monitoring are improved.

The invention discloses a rigid rod frequency ratio method axial force identification method. The method comprises the following steps: (1) according to engineering data, obtaining whether a rigid rod is a pressure rod or a pull rod, and obtaining related parameters of the rigid rod: the linear density m of the rigid rod, the length L of the rigid rod and the flexural rigidity EI of the rigid rod; (2) vibration signals of the rigid rod are collected on site through a vibration sensor, and multi-order frequency of the rigid rod is obtained through analysis; (3) selecting a corresponding method to calculate the frequency ratio of the rigid rod according to whether the rigid rod is a compression rod or a pull rod and boundary conditions, and calculating the axial force of the rigid rod through a frequency ratio method; and (4) checking the relative flexural rigidity xi of the rigid rod, and evaluating the axial force identification precision of the rigid rod. According to the method, a unified calculation method for identifying the axial force of the rigid pull rod and the pressing rod is established through a frequency ratio method, the method is easy to use and has good precision, and a practical new method is provided for identifying the axial force of the rigid rod.

The invention relates to the field of gear vibration signal analysis and fault diagnosis, in particular to a gearbox gear fault detection method which comprises the steps that gear vibration signals are collected, all initial extreme points are screened, and the product of the amplitude deviation degree of the initial extreme points and local accumulated energy serves as the amplitude sudden change degree; extracting the instantaneous frequency and the instantaneous phase of each initial extreme point, and fusing the frequency consistency and the phase consistency through a geometric averaging method to obtain a time-frequency coupling index; determining the effectiveness of the initial extreme point according to the product of the time-frequency coupling index and the amplitude abrupt change degree, screening the real extreme point according to the effectiveness, extracting the inherent rotation component of the vibration signal based on the real extreme point, combining to form a feature vector, and matching the feature vector with a fault feature library to realize the fault diagnosis of the gear. According to the method, the influence of noise interference and pseudo extreme points is suppressed, and the accuracy of fault detection is improved.

The invention relates to the technical field of liquefied petroleum gas supervision, and discloses a liquefied petroleum gas safe distribution supervision method based on artificial intelligence, which comprises the following steps: eliminating temperature fluctuation interference of a tank body by dynamically calculating saturated vapor pressure, compensating and correcting vehicle bumping influence by superposing motion disturbance, and analyzing and calculating leakage suspected degree of the tank body; combining the voiceprint spectrum characteristics, the infrared temperature gradient and the vibration signals, and analyzing to obtain leakage point coordinates of the tank body; the filling compliance is verified by analyzing the dynamic balance relation of the tank body, and the leakage risk value is calculated by using the model in combination with the leakage suspected degree and the environmental parameters to judge the leakage risk; dynamically associating high-risk action recognition with environment parameters, analyzing to obtain a behavior environment risk score, and inputting the score into a global regulation and control system; and in combination with the leakage suspected degree, the behavior environment risk score and the leakage point coordinates, constructing a phase change leakage coupling factor, synchronously regulating and controlling an alarm threshold value and decision planning, performing leakage diffusion simulation, and performing full-process response.

The invention discloses a tool fracture and fatigue intelligent detection method based on vibration signal analysis, and the method comprises the following steps: S1, installing a vibration sensor, and collecting the vibration signal of a tool in real time; s2, the collected tool vibration signals are preprocessed, and noise in the signals is removed; s3, performing time-frequency analysis on the preprocessed vibration signals, and extracting time-frequency features in the signals; s4, performing deep feature learning on the extracted time-frequency features to form deep features; s5, the depth features are classified and analyzed, and the health state of the cutter is output; s6, according to the health state optimization feature extraction and prediction result of the cutter, generating learning output; s7, evaluating the health state of the cutter in real time according to the learning output, and pushing alarm information; and S8, according to the alarm information, predicting the service life of the cutter and optimizing a cutter replacement and maintenance strategy. According to the method, short-time Fourier transform and Hough transform are combined, and the extreme learning machine is applied, so that intelligent detection on the fracture and fatigue of the cutter is realized.

The invention relates to the technical field of signal correction, in particular to a nonlinear distortion correction method for a vibration monitoring signal, and provides the following scheme: carrying out broadening and sparse reconstruction on a vibration signal through dispersion Fourier transform and compressed sensing, and extracting a high-frequency local structure; on the basis of phase-space reconstruction and a Lyapunov exponent, nonlinear distortion segments are identified; further performing orbit guiding modeling and degradation path reasoning on the orbit cluster corresponding to the segment, and selecting a reference orbit matched with the structure from the residual linear segments; and constructing an affine mapping relation between orbits and back-projecting the affine mapping relation to a time domain to generate a correction signal. According to the invention, high-precision identification and low-error correction of millisecond-level local nonlinear distortion are realized, and the stability and accuracy of vibration signal analysis are improved.

The invention relates to the technical field of rail transit monitoring, and discloses a multi-sensor fusion rail transit electric passenger car bogie dynamic performance monitoring method. The method comprises the steps that multi-source heterogeneous sensing data such as bogie vibration acceleration time sequence signals, wheel-rail contact force distribution data, bearing temperature gradient data and structural strain field data are collected; analyzing the vibration signal to extract a frequency band energy feature vector, performing spatial gridding mapping on the contact force data to generate a distribution matrix, and performing time-space alignment fusion on the two to form a primary fusion feature set; positioning a bearing temperature anomaly region through a deep convolutional network, and generating a secondary fusion feature set in combination with a structural strain concentration coefficient; and inputting the two-stage feature set into a dynamic performance evaluation model, outputting a performance degradation index set of scores of axle box bearing wear degree, framework fatigue cumulant, wheel set tread damage and the like, generating a maintenance priority sequence and triggering a preventive maintenance instruction according to the performance degradation index set, and realizing accurate monitoring and efficient operation and maintenance management and control of the dynamic performance of the bogie.

The invention discloses an on-load tap-changer fault diagnosis method based on multi-feature fusion, relates to the technical field of on-load tap-changer fault diagnosis, and is used for solving the problem that complex information in a signal cannot be comprehensively captured due to the fact that an existing vibration signal analysis method generally focuses on extraction of a single feature. Comprising data acquisition, feature extraction and fault diagnosis. According to the method, advanced feature extraction technologies of complementary ensemble empirical mode decomposition (CEEMD), quality factor adjustable wavelet transform (QFAT) and short-time Fourier transform (STFT) are combined, and fault information in a vibration signal is comprehensively extracted. By optimizing the core parameters of the OOSVM classifier and combining the predatory optimization algorithm, the global search capability and local fine search capability of the algorithm are improved, the accuracy, robustness and timeliness of fault diagnosis are further improved, and the requirements of power equipment fault diagnosis for high precision, real-time performance and adaptability are met.

The invention relates to the technical field of engine detection, and discloses an impeller imbalance fault detection method based on vibration signal analysis, which comprises the following steps: S1, collecting impeller vibration signals by arranging a plurality of three-axis acceleration sensors at key parts such as an impeller and a bearing; s2, preprocessing the collected vibration signals, including removing noise by using wavelet transform and standardizing the signals; and S3, decomposing the vibration signal into a plurality of intrinsic mode functions through empirical mode decomposition. According to the method, technologies such as multi-channel vibration signal acquisition, empirical mode decomposition and wavelet packet transformation feature extraction, multi-task learning classification and identification, generalization ability enhancement of a generative adversarial network and fault trend prediction of a long and short-term memory network are combined; the accuracy, the mode distinguishing capability and the dynamic trend prediction capability of impeller imbalance fault detection are remarkably improved, and meanwhile, the real-time performance and the intelligent early warning function of fault monitoring are achieved through edge calculation.

The invention relates to a dental drilling hardness real-time monitoring method based on vibration signal analysis and a dental hardness sensor. The dental drilling hardness real-time monitoring method comprises the following steps: acquiring a three-dimensional vibration displacement signal; after filtering and multi-dimensional feature extraction, obtaining a vibration feature vector representing the mechanical impedance of the tooth body; inputting the hardness index and the vibration displacement into a trained machine learning model, and mapping and outputting the hardness index and the vibration displacement of the current drilling area in real time; packaging with a timestamp, and serializing and storing as a traceable historical record; the system continuously presents hardness change in a dynamic waveform and a color gradient, and prompts when detecting that the hardness changes suddenly or crosses a boundary. Compared with the prior art, the method has the advantages that the three-dimensional micro-vibration signal of the drill bit can be converted into the digital fingerprint of the mechanical impedance of the tooth tissue in real time, and a visualization-reminding closed loop is driven by the hardness index flow synchronized with the timestamp, so that the drilling hardness in-situ sensing is realized.

The invention provides a fuel gas non-metal pipeline aging degree vibration detection system and method based on deep learning, and automatic evaluation of the non-metal pipeline aging degree is realized. The system comprises a vibration signal acquisition module, a vibration signal analysis module, a deep learning analysis module and an aging evaluation feedback module. The method comprises a laboratory database establishment stage and an engineering detection stage, the laboratory database establishment stage comprises a vibration signal acquisition module, a vibration signal analysis module, a deep learning analysis module and an aging evaluation feedback module, and the engineering detection stage comprises a vibration signal acquisition module and an aging evaluation feedback module. The method is high in accuracy, efficiency and non-destructive, has little influence on pipeline operation, is suitable for safety monitoring of various non-metal pipelines, and has wide application value.

The invention relates to the technical field of construction monitoring, in particular to a monitoring, prevention and control method and system for a road construction process, and the method comprises the steps: carrying out laser ranging and vibration signal analysis, extracting phase disturbance and tail section response features, recognizing a key monitoring section, carrying out time mutual dependence analysis, calibrating a risk section, and carrying out prevention and control alarm. According to the invention, through the spatial distribution fitting constructed by laser ranging and the calculation of the phase disturbance density index, the spatial resolution capability of the disturbance anomaly of the underground structure is improved, the spatial turn-back rate is analyzed in combination with the change of the included angle of the coordinate vector, the abnormal disturbance area is clustered and aggregated through the density peak value, and the accurate screening of the observation section is realized. According to the method, a tail section vibration signal collected by an underground wall acceleration sensor is introduced, a feature vector sequence is constructed according to the tail section duration and the energy residual proportion, and the vibration difference between sections is described through the Mahalanobis distance, so that the sensitive recognition capability of construction disturbance is enhanced.

The invention provides a reservoir inherent frequency downhole in-situ measurement method based on neural network filtering. The method comprises the steps that (1) pulse shock waves are generated through a continuous high-voltage pulse discharging device to induce reservoir rock mass vibration; (2) acquiring natural vibration noise signal data of the continuous high-voltage pulse discharge device during pulse discharge at the wellhead; (3) acquiring a background noise signal of the continuous high-voltage pulse discharge device in a static state in a shaft and feedback vibration signal data during pulse discharge; (4) filtering and noise reduction are carried out on the high-noise background data; (5) carrying out vibration signal analysis; (6) obtaining a Hilbert marginal spectrum, and determining a correlation coefficient between each intrinsic mode and an original vibration signal; and (7) determining the inherent frequency of the target reservoir. According to the method, the inherent frequency measurement of the target reservoir can be directly carried out underground, the problem that the underground inherent frequency measurement is difficult or the indoor inherent frequency test is inaccurate is effectively solved, and the underground in-situ efficient acquisition of the inherent frequency of the reservoir is realized.

The invention discloses a distributed optical fiber sensing performance optimization method for the vibration response of a hydraulic structure, and the method comprises the steps: firstly constructing a distributed optical fiber vibration sensing system with adjustable pulse width, and building a system performance index checking platform, so as to obtain distributed vibration signals under different system parameters; analyzing the spatial and temporal distribution characteristics of measured values, establishing a mathematical relationship model of measured value noise and spatial resolution, and quantifying the influence of the noise on the measurement precision. According to the method, distributed dynamic strain values under different spatial resolutions are calculated in combination with a simulation model of a target hydraulic structure, and optimal system parameter setting is optimized by integrating the relationship among measured value noise, measured truth values and the spatial resolutions, so that the system perception performance is improved. The reliability and engineering applicability of the distributed optical fiber vibration sensing system in hydraulic structure health monitoring can be effectively improved, high-precision and low-noise monitoring data are provided for long-term vibration monitoring of projects such as dams, bridges and tunnels, and the method has high engineering application value.

The invention provides an axle coupling vibration data generation method based on image feature coding, and belongs to the technical field of image feature coding, and the method comprises the steps: building a vibration data training set through employing an analog signal and an actual engineering signal, and dividing the vibration data training set into training subsets under different working conditions; then, the original vibration signal is preprocessed, and a time-frequency image is obtained through short-time Fourier transform; thirdly, extracting image space features by using a convolution Transform encoder, and generating image hidden space feature vectors; then, enhanced sampling is carried out on the feature vector through a diffusion probability model, and a new feature vector is generated; then, a convolution Transform decoder is used for carrying out reconstruction to generate a simulated vibration signal; and the quality of the generated signal is further improved through an end-to-end joint training optimization model. According to the method, a large amount of vibration data with real statistical characteristics can be generated, the precision and generalization ability of axle coupling vibration signal analysis are effectively improved, and the method is expected to be applied to the fields of structural health monitoring, bridge anomaly detection and intelligent defect diagnosis.

The invention discloses a rotating equipment health state evaluation method, and belongs to the technical field of equipment state intelligent analysis. According to the method, a health state analysis model is established by acquiring vibration signals in the operation process of the rotating equipment, and the health state change conditions of the rotating equipment under different operation durations of the rotating equipment are analyzed; according to the established health state analysis model, determining the size of a sliding window intercepted when the vibration signal of the rotating equipment is analyzed in the current state; determining a health assessment value of the rotating equipment in the current state according to the size of a sliding window intercepted during vibration signal analysis of the rotating equipment in the current state; the health assessment value of the rotating equipment in the current state is monitored, whether an alarm needs to be given or not is judged, and a manager overhauls the rotating equipment in advance according to reminding of an alarm signal, so that the safety risk of equipment faults is reduced, and the adaptability of the system and the accuracy of health state analysis are improved.

The present application relates to the technical field of transformer fault diagnosis, and in particular to a transformer body vibration signal analysis and fault diagnosis method, device and medium. The present application combines the kurtosis characteristics of the vibration signal, adopts a semi-soft threshold function wavelet denoising method based on the threshold selection method of the 3sigm rule, and achieves better denoising effect. Through the fault diagnosis model of the organic fusion of the two improved HHT transforms-mobilenetV2 model, combined with different feature extraction methods, it is more conducive to retaining the effective features of the vibration signal; the improved mobilenetV2 model designs a multi-scale deep convolution model, introduces a channel attention mechanism before the channel-by-channel convolution, and after multi-scale deep convolution feature extraction, a multi-source data attention mechanism is introduced; without affecting the safe and reliable operation of the transformer, the intelligent diagnosis of the vibration state fault of the transformer is realized.

The invention discloses a blasting parameter dynamic adjustment method based on vibration signal analysis in strip mine blasting, which comprises the following steps of: S1, arranging a plurality of vibration monitoring instruments on a strip mine blasting site, and acquiring vibration velocity, frequency, vibration duration and monitoring point position information of a blasting vibration signal; s2, calculating characteristic parameters such as instantaneous frequency, amplitude and phase of the signal in combination with Hilbert conversion, and decomposing the collected original vibration signal by using a signal processing algorithm; s3, constructing a blasting effect evaluation model by integrating factors such as muck pile lumpiness distribution, root conditions, dust concentration and the like; and S4, a blasting parameter dynamic adjustment strategy is formulated according to the incidence relation between the vibration signal characteristic parameters and the blasting effect. According to the method, the actual information in the blasting process can be timely and accurately obtained, a reliable basis is provided for dynamic adjustment of blasting parameters, the blasting effect is remarkably improved, the lumpiness of a muck pile is more uniform, the generation of roots is reduced, and dust pollution is reduced.

The invention discloses a mixed-flow water turbine vibration signal processing method based on adaptive enhanced EEMD (ensemble empirical mode decomposition), and relates to the technical field of vibration signal analysis and processing. A vibration signal of the mixed-flow water turbine is collected, and wavelet noise reduction is carried out; self-adaptive frequency band weighted noise is injected into the noise-reduced signal, and a noise-added signal is obtained; eMD decomposition is carried out on the signal after noise adding, a stopping condition is modified into a double-constraint mode stopping criterion in the EMD decomposition process, and an IMF component set and a residual error are obtained by using double constraints of the number of extreme points and the residual error energy change rate; and effectively screening IMF components of the IMF based on a fault sensitivity index, and introducing a compensation item into a residual error to obtain a final decomposition result as a vibration signal processing result. Background noise is effectively suppressed through frequency band weighted noise injection, and modal aliasing is effectively suppressed through a double-constraint stop criterion and fault sensitivity screening, so that fault features can be separated more accurately, and the signal-to-noise ratio can be improved.

The present application relates to the technical field of roller shutter door assembly wear evaluation, and particularly relates to a silent roller shutter door assembly wear evaluation method based on vibration signal analysis, which comprises the following steps: collecting a roller shutter door vibration signal, performing wavelet packet decomposition, reconstructing a denoising signal according to energy entropy and kurtosis product, and generating a window adjustment coefficient; calculating a slope ratio to construct a double-threshold band scaled by the adjustment coefficient to determine candidate pulse points; dividing pulse clusters according to time interval mutations, extracting amplitude Gini coefficient, interval discrete entropy and center frequency offset to fuse into a distortion factor; constructing a wear characteristic vector from the distortion factor cumulative slope and peak interval ratio, determining a wear grade through Mahalanobis distance comparison; recursively updating a covariance matrix based on a forgetting factor, and performing a remaining service interval penalty compression measurement. The present application overcomes the strong noise masking problem and guarantees the reliability of the prediction result.

The present invention relates to a vibration detection method for a circuit breaker mechanical fault, comprising the following steps: 1. magnetically adsorbing or adhering a vibration sensor to the surface of a housing at an operating mechanism of a circuit breaker under test; 2. closing the circuit breaker to excite vibration, whereupon the vibration sensor collects a vibration signal, transmits the signal to a vibration signal analysis and processing instrument, and records the vibration signal; 3. the vibration signal analysis and processing instrument obtains the magnitude of the vibration amplitude, performs a fast Fourier transform on the vibration signal within a range of 0-8000 Hz, and performs a three-layer wavelet packet decomposition on the vibration signal using a packet processing unit of an industrial computer; and 4. calculating the maximum amplitude a of the vibration, and obtaining the energy value e of each frequency band. i 5. The original data of the load vibration test of the circuit breaker during commissioning are also subjected to fast Fourier transform and three-layer wavelet packet decomposition in the range of 0-8000Hz to calculate the maximum amplitude A of the vibration and the energy E of each frequency band. i ; 6. Determine whether there is a spring fatigue loosening fault; 7. Determine whether there is a transmission mechanism jamming fault; 8. Display the fault condition.

The invention belongs to the technical field of rubber bearing wear state monitoring, and particularly relates to an axial flow pump rubber bearing wear state monitoring method based on vibration signal analysis. The method comprises the following steps: acquiring and preprocessing a signal; constructing features; dividing wear stages; and intelligent early warning is realized. The system comprises an LMS data acquisition system, a PC (Personal Computer) and an MATLAB (Matrix Laboratory) program which are connected in sequence. According to the method, multi-scale feature extraction, entropy feature construction, feature fusion and classification model construction of the vibration signals are combined, the wear states (healthy, slight wear and serious wear states) of the rubber bearing of the rubber axial flow pump are accurately monitored and recognized, and the running safety and reliability of the axial flow pump are effectively guaranteed. According to the invention, the wear state of the rubber bearing of the axial flow pump can be monitored in real time in a complex environment, and a guarantee is provided for reliable operation of the axial flow pump.

The invention discloses a yaw system active anti-vibration method and system based on barometric gradient perception, and relates to the technical field of wind power control, and the method comprises the steps: carrying out the distributed continuous barometric monitoring of a yaw system through a distributed barometric sensor, and obtaining distributed barometric gradient data; performing data conversion on first air pressure gradient data in the distributed air pressure gradient data to obtain a first vibration signal; performing feature analysis on a target vibration signal obtained by fusing the first vibration signal to obtain a target vibration absorption strategy; and activating an anti-vibration mechanism to perform active anti-vibration execution on the target anti-vibration strategy. The technical problem that the yaw system vibration caused by wind-induced disturbance cannot be effectively recognized in the prior art is solved, and the technical effect of improving the yaw system vibration recognition precision and the active vibration absorption control capability is achieved by constructing a distributed air pressure sensing and vibration signal analysis mechanism and generating and executing the active vibration absorption strategy.

The present invention discloses a method and system for actively avoiding vibrations in a yaw system based on air pressure gradient sensing, which relates to the field of wind power control technology. The method and system include: performing distributed continuous air pressure monitoring on the yaw system through a distributed air pressure sensor to obtain distributed air pressure gradient data; performing data conversion on first air pressure gradient data in the distributed air pressure gradient data to obtain a first vibration signal; performing feature analysis on a target vibration signal obtained by integrating the first vibration signal to obtain a target vibration avoidance strategy; and activating a vibration avoidance mechanism to actively avoid vibrations in accordance with the target vibration avoidance strategy. The present invention solves the technical problem that the prior art cannot effectively identify vibrations of the yaw system caused by wind-induced disturbances. By constructing a distributed air pressure sensing and vibration signal analysis mechanism, an active vibration avoidance strategy is generated and executed, achieving the technical effect of improving the vibration recognition accuracy and active vibration avoidance control capability of the yaw system.

A fault diagnosis method for on-load tap changers based on multi-feature fusion, which relates to the technical field of on-load tap changer fault diagnosis and is used to solve the problem that the existing vibration signal analysis methods usually focus on the extraction of single features and cannot comprehensively capture the complex information in the signals. It includes data acquisition, feature extraction, and fault diagnosis. The present invention combines advanced feature extraction techniques of complementary ensemble empirical mode decomposition (CEEMD), quality factor adjustable wavelet transform (QFAT), and short-time Fourier transform (STFT) to comprehensively extract fault information in vibration signals. By optimizing the core parameters of the outlier support vector machine (OOSVM) classifier and combining it with the predator optimization algorithm to enhance the global search ability and local fine search ability of the algorithm, the accuracy, robustness, and timeliness of fault diagnosis are further improved, realizing the requirements of high precision, real-time performance, and adaptability for power equipment fault diagnosis.

The invention discloses an intelligent vibration reduction regulation and control method for a diesel generating set based on vibration signal analysis. The intelligent vibration reduction regulation and control method comprises the following steps: S1, collecting multi-channel vibration signals of the diesel generating set; s2, performing preprocessing such as denoising and normalization on the vibration signal; s3, extracting time domain, frequency domain and time-frequency domain characteristic parameters; s4, optimizing the feature weight by adopting a parrot search algorithm, and constructing a recognition model to judge the operation state; s5, after abnormity is recognized, the fuel injection quantity, the injection angle, the air inlet pressure, the rotating speed and other operation control parameters are collected; s6, optimizing the control parameters through a whale optimization algorithm, and minimizing the main vibration amplitude; s7, applying the optimal parameters to a control system to execute adjustment; and S8, collecting an adjusted vibration signal for feedback, updating the model, and realizing closed-loop control and self-learning. According to the invention, intelligent vibration identification and self-adaptive regulation and control of the diesel generating set are realized, and the operation stability and the vibration reduction efficiency are improved.

The present application relates to a kind of powder packaging machine transmission component fault pre-diagnosis method based on vibration signal analysis, the present application is accurate to collect multiple source vibration signals, standardization processing of time-frequency characteristics, generate fault semantic anchor point library in combination with prior knowledge of dynamics and under the training method of multiple loss constraint convolutional neural network, realize the adaptive alignment of vibration signal hidden space feature and physical fault mechanism.The present application outputs the joint attribution time-frequency graph of specific fault type, and the coincidence degree of model attention area and theoretical fault frequency label is based on physical consistency check, significantly improve diagnostic accuracy and explainability, effectively assist equipment operation decision, the present application is by constructing the fault semantic anchor point library based on physical mechanism, and it is embedded in the hidden space optimization process of convolutional neural network in differentiable way, effectively overcome the problem that traditional data-driven model is generally present in powder packaging machine transmission chain fault diagnosis.

The present invention relates to the technical field of data processing, and specifically relates to a method and system for optimizing the running trajectory of a multi-axis industrial robot. For the original vibration signal of each axis in the multi-axis system, based on the independent component analysis method, the error vibration signal and the complexity factor of the motion posture of the multi-axis industrial robot are analyzed in combination with the vibration signal of the real motion. Further, the error vibration intensity coefficient of the error vibration signal is judged, and according to the error vibration intensity coefficient of the error vibration signal, it is fed back to the PID controller of the multi-axis industrial robot to adjust or correct its proportional gain component, and then it is fed back to the multi-axis linkage interpolation control algorithm to adjust the control of the corresponding axis, effectively correcting the influence factors of the non-linear characteristics on the running deviation of the multi-axis industrial robot, achieving a more accurate and reliable control of the running trajectory of the multi-axis industrial robot, realizing the optimization of the running trajectory of the multi-axis industrial robot, and improving the working efficiency of the multi-axis industrial robot.