186 results about "Waveform analysis" patented technology

The invention relates to the technical field of industrial equipment diagnosis, in particular to an AI-driven industrial equipment fault analysis method and system, and solves the problems of insufficient multi-source data fusion, weak working condition adaptability and inaccurate fault positioning in the prior art. According to the method, high-frequency time sequence signals are synchronously collected through a vibration sensor, a temperature sensor and the like, working condition parameters are marked, a dynamic confidence coefficient weight is generated based on a working condition matching historical fault library, and cross-modal features are extracted through a space-time convolutional network after multi-source data are fused. A fault mode separation module is used for decoupling the fault contribution degree of mechanical transmission, electric power and heat dissipation systems, precise diagnosis is achieved in combination with frequency domain fingerprints, time domain waveform analysis and cross decision verification, finally, the remaining life is predicted through a long-short-term memory network and a Weibull model, and the system integrates multi-source sensing, dynamic fusion and intelligent diagnosis units. The fault early warning accuracy and the equipment maintenance scientificity under complex working conditions are improved, and a solution is provided for intelligent diagnosis of industrial equipment.

The invention relates to a system and a method for testing the conductivity of an engine wire harness, and relates to the field of intelligent detection, a programmable switch matrix is formed by acquiring a wire harness layout diagram and arranging matrix relays, and after logic self-inspection is executed, an optimal conduction path is searched based on a hierarchical partition model and a DFS algorithm; according to the method, the path image sets of the partitions are overlapped and mapped to identify abnormal wire harness sections, and finally, abnormity is determined through analog signal injection and waveform analysis and physical repair guidance is performed, so that the technical problems of low test efficiency, poor flexibility and difficulty in adapting to complex variable wire harness layout conductivity test are solved, and the purposes of improving the test flexibility and efficiency and reducing the test cost are achieved. And a fault point can be accurately positioned.

The invention relates to the technical field of ultra-far offshore data transmission, and discloses an ultra-far offshore distance real-time data stable transmission method and system. The method comprises the following steps: acquiring target transmission link data, and extracting dynamic fluctuation characteristics containing signal strength distribution information and a delay jitter relationship from the target transmission link data; calling the trained channel estimation network to carry out signal positioning processing on transmission link data, and generating a signal transmission path set containing a channel type identifier and phase code mapping relation; based on the set, calling a waveform analysis network to perform multi-modal feature fusion on the signal intensity distribution information to obtain a fusion result containing time-frequency domain features and modulation mode features; and inputting a fusion result into a transmission strategy model to perform dynamic path selection, and outputting a real-time data transmission element containing a control instruction generated based on a signal transmission sequence and a phase self-calibration parameter. According to the method, stable transmission of real-time data under the ultra-far offshore distance can be realized.

The invention discloses a steam turbine safe operation health assessment method and system, and belongs to the technical field of steam turbine monitoring. The method comprises the following steps: acquiring running data of a steam turbine in real time through a multi-source sensor network deployed on key components of the steam turbine; the method comprises the following steps: preprocessing original operation data of a steam turbine by adopting edge computing equipment to generate a time domain waveform analysis diagram or a time sequence analysis diagram; transmitting the preprocessed structured feature data to an operation and maintenance management platform, inputting a fault diagnosis model for analysis, pre-training through a historical fault case library, and aligning feature distribution of the historical case library and current equipment data through a domain adaptive algorithm in transfer learning; and inputting a diagnosis result into a self-adaptive dynamic evaluation model based on a reinforcement learning engine, dynamically adjusting a model weight and an alarm threshold according to real-time working condition parameters, outputting a health score and a maintenance decision, and forming a closed-loop process of perception-edge processing-cloud diagnosis-dynamic optimization-decision output.

The invention relates to the technical field of submarine cable fault positioning, in particular to a submarine cable fault positioning system based on underwater beacons. The method comprises the steps that an underwater acoustic beacon array unit monitors and collects submarine cable fault sound wave signals in real time based on underwater acoustic beacons; the fault signal processing analysis unit extracts submarine cable fault sound wave signal parameters based on a non-point source space sound source waveform analysis model, and generates multi-parameter fault sound source feature vectors; the sound source positioning analysis algorithm unit performs three-dimensional inversion positioning based on the multi-parameter fault sound source feature vector and the arrival time difference of the submarine cable fault sound wave signal to the underwater acoustic beacon, and obtains the spatial position information of the submarine cable fault point; and the data communication alarm management unit sends the spatial position information of the submarine cable fault point to a shore-based operation and maintenance system. The submarine cable fault positioning method is used for realizing a submarine cable fault positioning technology for quickly and accurately positioning and identifying spatial extension characteristics in a complex submarine environment.

The invention provides an operation optimization method and system based on electric power data conjoint analysis, and the method comprises the steps: obtaining synchronous sampling data and power grid topological parameters of a wide-area electric power system; performing equipment state evaluation, broadband resonance traceability and harmonic responsibility quantification on the synchronous sampling data and the power grid topological parameters by using a conjoint analysis engine, and generating a power grid control strategy; and carrying out operation optimization on the power equipment in the wide-area power system based on the power grid control strategy. According to the method, conjoint analysis is carried out on equipment state evaluation, broadband resonance traceability and harmonic responsibility quantification of the wide-area power system, traditional static spectrum analysis is upgraded to dynamic behavior perception, the power grid analysis depth and accuracy can be improved, the high-frequency harmonic analysis requirement can be met through broadband resonance traceability, and the method is suitable for large-scale popularization and application. Intelligent analysis can be realized for different application scenes, the waveform analysis capability and the frequency response range are improved, and the analysis effect is improved.

A system comprises a signal generator circuit configured to generate a modulated radio frequency (RF) signal based on a predefined waveform. An output signal path is connected to the signal generator circuit and is connectable to a device under test. A first signal path is connected to the signal generator circuit so as to receive a reference signal corresponding to the modulated RF signal. A second signal path is connectable to the device under test so as to receive a measurement signal from the device under test. A analysis signal path is connected to the first and second signal paths for merging the reference signal and the measurement signal into an analysis signal. A signal analysis circuit is configured to separate the reference signal from the measurement signal for analyzing the analysis signal based on the predefined waveform.

The invention discloses a rectifier bridge arm fault identification method and system circuit based on multi-characteristic current waveform analysis, and relates to the technical field of rectifier bridge arm fault identification, and the method comprises the steps: collecting a current signal of an AC input side of a rectifier bridge through a current sensor, and obtaining an original current time domain waveform after filtering conditioning; performing period division on the original current time domain waveform based on a voltage synchronization signal, and outputting a positive half-cycle conduction interval and a negative half-cycle conduction interval in each power frequency period; in each power frequency period, performing square integral operation on the current waveforms of the positive half-cycle conduction interval and the negative half-cycle conduction interval to obtain a positive half-cycle energy value and a negative half-cycle energy value, and calculating an energy unbalance degree; and windowing fast Fourier transform is carried out on the original current time domain waveform, amplitudes and phases of third, fifth and seventh characteristic harmonics are extracted to form a harmonic characteristic vector, the vector is matched with a pre-stored bridge arm fault harmonic fingerprint template, and the matching degree of each fault type is obtained.

A waveform analytical device 4 which analyzes a target waveform which is a chromatogram or an optical spectrum includes a waveform division unit 54 configured to divide the target waveform into a plurality of partial waveforms, a determination unit 55 configured to determine whether each of the plurality of partial waveforms of the target waveform is a peak portion using a learned model created by machine learning using a plurality of sets of a plurality of partial waveforms created by dividing a reference waveform having a peak portion whose position is known, and a classification unit 56 configured to classify the target waveform into a peak region where the peak portion continues and a non-peak region other than the peak region based on a determination result from the determination unit.

The invention discloses a multispectral and hyperspectral laser radar weak multi-echo signal extraction method. According to the method, a B-spline model is adopted to establish models for transmitted pulses and received echoes. Compared with a traditional Gaussian model and a B-spline model, the method has local control characteristics, is more suitable for modeling and analysis of complex waveforms, improves the adaptability to diversified signals, remarkably reduces the probability that noise is misjudged as echo signals by conducting preliminary screening and positioning on received echoes of all channels, and improves the accuracy of noise detection. Therefore, error accumulation is effectively reduced. The process not only improves the accuracy of signal identification, but also greatly reduces the complexity of subsequent data processing, reduces the calculation burden of the system, and lays a solid foundation for refined waveform analysis.

The invention discloses an electric energy quality comprehensive monitoring method and system based on real-time waveform analysis. The method comprises the following steps: S1, acquiring an analog voltage signal acquired by a mutual inductor; s2, preprocessing to obtain a voltage waveform data segment; s3, performing multi-dimensional feature extraction to obtain a voltage waveform multi-dimensional feature vector; s4, extracting a voltage RMS effective value, a total harmonic distortion rate and wavelet packet energy; s5, inputting the voltage RMS effective value, the total harmonic distortion rate and the wavelet packet energy into a grading event pre-detection model to obtain an event feature subset coding vector; and S6, enabling the event feature subset coding vector to pass through an intelligent event fine classification model to obtain an electric energy event accurate classification result. According to the electric energy quality comprehensive monitoring method and system based on real-time waveform analysis disclosed by the invention, the accuracy and efficiency of electric energy quality event monitoring are greatly improved.

In general, in one aspect, embodiments relate to a method and / or system that includes disposing a cement bond logging tool into a wellbore. The method and / or system comprising: a cement bond logging tool comprising: two or more transmitters configured to transmit an acoustic wave from cement bond logging tool; and an array of receivers configured to receive a refracted waveform; and an information handling system configured to process the refracted waveform from the array of receivers into a cement bond property log.

The invention relates to the technical field of safety monitoring, and particularly discloses a storage tank safety monitoring method based on the Internet of Things, and the method comprises the steps: synchronously collecting a high-frequency leakage acoustic emission signal and a reference environment noise signal through a piezoelectric acoustic emission sensor disposed on the outer wall of a storage tank and an industrial microphone disposed in a storage tank region; after time delay alignment is carried out on a reference environment noise signal and a high-frequency leakage acoustic emission signal, self-adaptive filtering processing is carried out, so that environment noise interference in the high-frequency leakage acoustic emission signal is filtered out. And then, further introducing a deep learning algorithm to carry out time domain waveform analysis and adaptive content perception enhancement processing on the denoised high-frequency leakage acoustic emission signal so as to enhance key acoustic emission characteristics in the signal, thereby realizing intelligent identification of the storage tank leakage on the basis. According to the method, the accuracy and sensitivity of storage tank leakage monitoring can be effectively improved, the influence of noise interference on an industrial site on storage tank leakage identification is overcome, and early warning of tiny leakage of the storage tank is achieved.

The invention provides a frequency converter embedded current waveform analysis method and system and a medium, and relates to the technical field of current waveform analysis, and the method comprises the steps: obtaining a sampling circuit configured in a current path of a frequency converter; calculating the required total gain based on the original output voltage signal of the sampling resistor and the required input voltage of the analog-to-digital converter; the signal quality of the original output voltage signal is evaluated, the required total gain is dynamically distributed to an instrument amplifier and a programmable gain amplifier, the original output voltage signal is amplified and then passes through an analog-to-digital converter, and current waveform data is obtained for current anomaly analysis; when the signal quality is lower than a first threshold value, the gain share of the instrument amplifier is increased preferentially; when the signal quality is higher than a second threshold, the gain share of the programmable gain amplifier is increased preferentially. According to the invention, the technical problem of low accuracy of current waveform analysis in the prior art can be solved, and the technical effect of improving the accuracy of waveform analysis is achieved.

The invention discloses a motor noise detection method and system based on waveform analysis, and relates to the related field of motor fault diagnosis, and the method comprises the steps: carrying out the time domain collection of an original noise signal during the operation of a motor, obtaining an original waveform signal, carrying out the Fourier transform of the original waveform signal, extracting the spectrum characteristics, and obtaining the spectrum energy distribution; performing threshold segmentation on the frequency spectrum energy distribution, screening out motors which meet a preset quality standard but have preset abnormal indications, and obtaining critical qualified motors; extracting an original waveform signal of the critically qualified motor, and extracting a transient impact component from the original waveform signal to obtain a transient feature; and matching the transient feature with an early fault mode in a pre-constructed fault feature library, and outputting a potential fault detection result. The technical problem that the current qualified motor with potential faults cannot be identified in the existing motor noise detection is solved, and the technical effect of accurately identifying the qualified motor with the potential faults is achieved.

The invention discloses a fluid pipeline system diaphragm valve fault self-adaptive identification method, relates to the technical field of diaphragm valves, and solves the problems that signal characteristics are not subjected to fine analysis and are easily interfered by fluctuation of normal working conditions. The method comprises the following steps: acquiring a to-be-determined wave band, comparing the to-be-determined wave band with a preset standard waveform, identifying a to-be-verified amplitude associated with a corresponding processing process based on a corresponding movement comparison processing process, carrying out abnormity determination on a diaphragm valve based on a specific numerical value of the to-be-verified amplitude, and confirming an abnormal diaphragm valve. The specific accuracy of amplitude verification in the subsequent movement process is facilitated, the abnormal influence caused by signal fluctuation can be effectively reduced, and the specific accuracy in the valve abnormality determination process is improved.

The invention discloses an unmanned aerial vehicle cluster collaborative formation control method and system. The method comprises the following steps: acquiring multi-source sensor data in real time, fusing the multi-source sensor data by using an extended Kalman filtering algorithm, and performing state estimation when part of sensor data is missing to obtain state information; based on the state information, interference is detected by monitoring related parameters of communication signals and sensor data, the interference type is identified through spectrum and waveform analysis, and the intensity is evaluated; determining interfered unmanned aerial vehicles and adjacent unmanned aerial vehicles according to a communication neighborhood, performing local formation adjustment on the interfered unmanned aerial vehicles and the adjacent unmanned aerial vehicles by using a distributed control algorithm, and estimating a speed optimization algorithm of the adjacent unmanned aerial vehicles by using Kalman filtering under incomplete information to realize unmanned aerial vehicle cluster collaborative formation control; and establishing an early warning model based on the distance and the speed, and realizing collision avoidance by adopting an optimized artificial potential field method. The anti-interference capability of the unmanned aerial vehicle cluster is improved, the collision probability is reduced, and the formation stability is enhanced.

The invention discloses an intelligent critical case early warning analysis system based on multi-source clinical data fusion, belongs to the technical field of image processing, and can more accurately carry out respiration monitoring and early warning. Comprising a respiratory movement analysis module used for generating a respiratory movement amplitude change curve according to thoracic and abdominal movement video data; the breathing machine waveform analysis module is used for generating waveform characteristic data containing abnormal time slices according to the breathing machine waveform data; the blood oxygen signal processing module is used for generating a blood oxygen change trend vector according to the blood oxygen saturation degree signal data; the multi-modal fusion module is used for generating a multi-dimensional physiological feature vector under a synchronous timestamp according to the breathing movement amplitude change curve, the waveform feature data and the blood oxygen change trend vector; the risk assessment module is used for generating a breathing compensation state score according to the multi-dimensional physiological feature vector; and the alarm triggering module is used for triggering a grading alarm signal according to the deviation degree of the breathing compensation state score and the baseline range.

The invention discloses a processing method and system for a ship intelligent navigation scene, and relates to the technical field of ship intelligent navigation, and the method comprises the following steps: carrying out the continuous waveform analysis of an energy return path of a laser ranging link based on the sudden increase of the density of seawater suspended particles caused by the frontier of a storm, constructing a particle disturbance recognition zone, and carrying out the recognition of the particle disturbance; generating a disturbance identifier carrying an energy rebound mode characteristic according to an analysis result; and expanding the ranging pulse to a time domain by using the disturbance identifier, performing pulse form reconstruction on an energy abnormal section of the ranging pulse, so that the ranging pulse still keeps a stable main peak structure under the influence of energy rebound, and outputting a stable main peak waveform after pulse form reconstruction. According to the invention, through constructing a disturbance identification and form reconstruction mechanism, continuous restoration and smooth output of the distance measurement signals are realized, and the stability and anti-interference performance of distance sensing are improved; and through approaching artifact weakening and gradual change operation control, excessive operation is prevented, and stable course and avoidance safety of the ship in a storm environment are ensured.

The utility model discloses a shield tunnel segment wall back grouting real-time detection device which is arranged at the shield tail of a shield tunneling machine, two construction platforms are symmetrically arranged in the shield tunneling machine, and the shield tunnel segment wall back grouting real-time detection device comprises an annular track which is movably installed between the two construction platforms in the tunneling direction of the shield tunneling machine. The circular track has an arc consistent with the radian of the outer wall of the duct piece; the sound wave detector is arranged on the outer side of the annular track in a reciprocating motion mode along the annular track, and the height of the sound wave detector can be adjusted in the radial direction of the duct piece; according to the grouting plumpness detection device, the grouting plumpness on a propagation path is reflected by utilizing the propagation speed of the sound waves in different media and the frequency change of the waves, the grouting plumpness can be detected in real time along with tunneling of a shield, and the grouting plumpness can be accurately detected. And a certain popularization value is achieved.

The invention relates to a distributed secondary cable checking device and method based on characteristic waveform analysis, and belongs to the field of electric power engineering site construction. Comprising a secondary cable, a host, an aviation plug connecting line and a distributed terminal. All wire cores of a secondary cable to be checked are connected with a distributed terminal through aviation plug connecting wires, then the distributed terminal is grounded through a grounding wire, then the distributed terminal is started, at the moment, the distributed terminal starts to inject different characteristic voltage signals into all the wire cores, and each wire core can be regarded as a steady-state alternating-current voltage source to the ground; a wire checking person holds a host at the other end of the cable to sequentially contact each wire core to extract waveform characteristics for wire checking, and the characteristic waveform forms a loop through the sequence of the distributed terminal, the cable core, the host, the human body and the ground; the multi-cable core checking device can quickly realize single-person multi-cable accurate core checking, and solves the problems that the operation is tedious, the efficiency is extremely low, the manpower is wasted, the result cannot be guaranteed, and the difficulty of multi-cable core checking is greatly improved.

This invention provides an amplifier with improved bias tracking performance based on a DSP chip. It includes a file acquisition module that directly acquires the audio file to be played via an interface. The audio file is pre-input into the DSP for standard waveform analysis, which speeds up processing and reduces latency during actual playback. During actual playback, the actual played energy is compared with the pre-calculated energy to determine if it matches the pre-designed output. If it matches, the pre-designed output is used directly; otherwise, it is further input into a model for processing, ensuring operational stability and safety.

The application discloses a sound source accurate positioning method based on an acoustic sensor array, and comprises the following steps: step one, inputting an audio segment and capturing the audio segment through an acoustic sensor; step two, making an audio image, that is, making a waveform image of the input audio; step three, acquiring a characteristic waveform, that is, cutting the waveform image to intercept a sound waveform that changes; step four, waveform analysis, that is, analyzing the intercepted waveform image and the input audio segment to obtain characteristic information contained in the audio; and step five, characteristic information analysis, that is, analyzing the information characteristic obtained in step four. In the implementation of the scheme, the characteristic in the audio segment is analyzed, the characteristic value is anchored, and then the array sound source is analyzed through the characteristic value, so that the characteristic value can be quickly and accurately positioned, the comparison range is reduced, the comparison judgment speed is accelerated, and the result accuracy is improved.

The invention relates to the technical field of modern electric power systems, and particularly discloses an electric power high-voltage data monitoring and waveform interactive analysis system, which comprises a data receiving layer, an event driving layer, a business processing layer and a front-end rendering layer, the data receiving layer is used for receiving the power high-voltage waveform data in real time and transmitting the power high-voltage waveform data to the event driving layer; the event driving layer is used for dispatching the electric power high-voltage waveform data and transmitting the dispatched electric power high-voltage waveform data to the business processing layer; the business processing layer is used for processing the scheduled power high-voltage waveform data and transmitting the processed power high-voltage waveform data to the front-end rendering layer and the time sequence database; and the front-end rendering layer is used for carrying out rendering optimization on the processed electric power high-voltage waveform data and returning a touch instruction to the business processing layer. According to the invention, the accuracy and real-time performance of power high-voltage monitoring can be greatly improved, and a convenient and powerful waveform analysis tool can be provided for power engineers.

The invention discloses a GIS isolation switch fault diagnosis method and system based on multiple parameters, and solves the problems that the workload is large, the detection precision is low, and the opening and closing states of a GIS isolation switch cannot be accurately judged when the opening and closing states of the isolation switch are detected in the prior art. Converting the current data into a current time waveform; the method comprises the following steps: acquiring and processing an angular displacement signal when a GIS isolating switch is switched on and off, converting the angular displacement signal into stroke data, and establishing a switching on and off action curve according to the stroke data; and according to the opening and closing action curve and the current time waveform, analyzing the continuous process of each action stage of opening and closing of the contact of the GIS isolating switch, and judging whether the opening and closing of the GIS isolating switch are normal or not. The state of the GIS disconnecting switch is judged by detecting the angular velocity of the GIS disconnecting switch and the motor current, the fault type of the GIS disconnecting switch can be conveniently and effectively determined, and whether the GIS disconnecting switch is switched on and switched off in place or not is judged.

The present invention relates to a conductivity testing system and method for an engine wiring harness, and relates to the field of intelligent detection. By obtaining a wiring harness layout diagram, laying out matrix relays to form a programmable switch matrix, and performing a logic self-test, the system searches for an optimal conduction path based on a hierarchical partitioning model and a DFS algorithm, and overlaps and maps the path atlases of each partition to identify abnormal wiring harness segments. Finally, the system determines the abnormality through analog signal injection and waveform analysis, and provides physical repair guidance. The system solves the technical problems of low test efficiency, poor flexibility, and difficulty in adapting to the conductivity test of complex and changeable wiring harness layouts, and achieves the technical effects of improving test flexibility and efficiency and accurately locating fault points.

The invention relates to a low-strain detection waveform analysis method fused with time-frequency feature extraction, and the method comprises the steps: carrying out the segmentation processing of a collected low-strain waveform signal on a time axis, detecting the energy change rate and starting time of each time segment, screening out signal segments which are remarkably different from background noise, and forming a local feature signal set; analyzing the occurrence interval, duration and energy change trend of each characteristic signal, identifying related signals belonging to the same potential damage process, and generating a time association chain; based on the time association chain and the multi-band features, performing consistency analysis on the performance of each feature under different time-frequency dimensions, retaining signals which are stable in both time and frequency, and rejecting accidentally prominent isolated anomalies in a single domain to obtain a reliable feature set of low-strain detection; and tracking the change trend of the reliable features in time and frequency, judging the occurrence and development of low-strain damage according to the evolution rules of the features in different stages, and outputting a detection result.

The application relates to the technical field of intelligent regulation and control of an archive storage environment, and discloses an intelligent control system for the temperature and humidity of an archive room. The system comprises an environment monitoring network component, an internal detection engine component, a fusion computing center component and a decision generator component. The environment monitoring network component collects environment data by using a sensor array and generates a high-precision environment state atlas by applying a data assimilation technique. The internal detection engine component reconstructs an internal non-uniform humidity field of archives based on risk identification in the atlas by using a waveform analysis algorithm. The fusion computing center component aligns the time and space attributes of the two, and outputs a comprehensive confidence by using an evidence reasoning method. The decision generator component generates an adaptive control strategy by using a heuristic optimization algorithm according to the confidence. The application realizes all-round accurate perception and intelligent regulation and control from the macro environment of the archive room to the micro environment of the archive entity.

The application provides a converter valve area short-circuit fault positioning method, device, equipment and medium, the method comprises the following steps: dividing the converter valve fault into eight typical valve area short-circuit faults; RTDS simulation analysis, according to the different converter protection action results, dividing into three types of protection actions, giving the typical fault characteristic quantity of the valve difference protection action for positioning the valve area fault and the eight typical valve area short-circuit quick criterion; according to whether the actual converter protection action occurs in the rectifying station or the inverting station, confirming the valve area short-circuit fault occurring station; according to the converter protection action event of the monitoring background, confirming the converter protection action type and the valve area fault converter bridge; calling the built-in fault recording of the DC control protection, analyzing the converter protection action waveform, analyzing the fault characteristics of the current waveform of the valve side of the converter bridge and the outlet current waveform of the DC side, positioning the valve area short-circuit fault point according to the typical fault characteristic quantity of the valve difference protection action and the eight typical valve area short-circuit quick criterion; and has the advantages of fast positioning speed and simple analysis method.