108 results about "Waveform analysis" patented technology

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.

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.

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 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 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 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 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.

The utility model provides a GJB289A bus network state detection device in an external field environment. The GJB289A bus network state detection device comprises a communication module, a waveform analysis module and an impedance analyzer, the communication module is in communication connection with the waveform analysis module and the to-be-tested bus, performs communication test on the to-be-tested bus, and records the communication state of the to-be-tested bus; the waveform analysis module is in communication connection with the bus to be tested to realize signal waveform test; the impedance analyzer is in communication connection with a to-be-tested cable corresponding to the to-be-tested bus, and carries out an attenuation test on the to-be-tested cable. By integrating the communication module, the waveform analysis module and the impedance analyzer, a GJB289A bus network can be tested to ensure the reliability and stability of communication. And the waveform analysis module and the impedance analyzer can test the signal waveform on the bus to be tested and analyze the impedance, so that the test requirements are met.

The invention relates to the field of power monitoring and management, in particular to a power distribution room equipment internet of things monitoring system. The system comprises a distributed sensing node network, an edge computing gateway, a data center platform and a multi-stage early warning module, the distributed sensing node network comprises a temperature and humidity sensor, a Hall effect current sensor and a voltage sampling circuit, and a signal processing unit is arranged in the distributed sensing node network; the edge computing gateway is connected with the distributed sensing nodes through a communication bus; the data center platform constructs a digital twin model of the power distribution equipment, stores time sequence data and configures a waveform analysis engine; the multi-stage early warning module is configured with a three-stage threshold interval, establishes a communication link with the linkage control device, and is configured with an alarm signal conversion unit. According to the invention, the technical effects of effective monitoring of the distribution room, processing of sensing signals, dynamic rendering display, linkage instruction generation, waveform analysis and diagnosis, and the communication bus having a protocol conversion function are achieved.

The utility model discloses a pulse insulation and voltage resistance testing device based on pulse waveform width. The pulse insulation and voltage resistance testing device comprises a pulse generator, a tested object, a voltage differential circuit, a high-speed AD conversion circuit and a pulse storage judgment circuit. A pulse signal output by the pulse generator is collected by the voltage differential circuit, is converted into a digital signal by the high-speed AD conversion circuit, and is transmitted to the pulse storage and judgment circuit for waveform analysis. And the microcontroller calculates the maximum value and the waveform width of the pulse waveform according to a preset algorithm, and compares the maximum value and the waveform width with a standard waveform so as to judge whether the detected product is broken down or not. Different from a traditional current-dependent judgment method, weak insulation breakdown can be accurately identified by measuring the width of the pulse waveform, and the influence of capacitive or inductive load on a judgment result is avoided. The device has the advantages of high precision, high efficiency and strong stability, can be widely applied to pulse insulation and voltage resistance tests of electrical equipment, and provides reliable electrical safety guarantee.

The invention relates to the technical field of big data processing, in particular to a high-voltage switch fault diagnosis system based on non-intrusive acoustic sensing characteristics. The system comprises a signal acquisition module used for acquiring a plurality of vibration signals of a target switch device and a plurality of vibration components of each vibration signal; the energy analysis module is used for analyzing the energy distribution of each vibration component; the waveform analysis module is used for analyzing the waveform of each vibration component; the de-noising module is used for carrying out wavelet transform de-noising on each vibration component, the signal reconstruction module is used for reconstructing the de-noised vibration components, the number of decomposition layers during de-noising is determined according to an energy analysis result, and a de-noising threshold value is determined according to a waveform analysis result. According to the method, the noise interference degree of each vibration component and the fault feature containing probability of each vibration component can be determined, adaptive noise reduction processing is carried out accordingly, and the information indicating the equipment fault impact feature can be fully reserved while the environmental noise can be effectively suppressed.

The invention discloses a broadcast equipment noise management and control system in the technical field of broadcast, the system can be carried on broadcast equipment with a processor for use, the system comprises a processing center, and the processing center comprises an audio collection unit, an audio processing unit and an audio processing unit, the audio conversion unit is used for converting audio signals of the mobile phone and comprises a conversion module; the audio analysis unit is used for analyzing audios and sound waves and comprises a waveform checking module, a waveform analysis module and a phase recognition module; and the noise processing unit is used for processing and reducing the audio noise. The audio noise reduction and the volume self-adjustment are carried out on the basis of the cross-correlation algorithm and the cosine similarity algorithm by acquiring the played audio and the environmental noise data in real time, so that the noise reduction operation of the broadcasting equipment is realized, the method can be applied to various broadcasting equipment, the audio noise during broadcasting is reduced, and the broadcasting effect is improved.

An electrical signal measurement device (2A) is provided with: a signal conversion unit (30) for intensity-modulating measurement light on the basis of a time-waveform electrical signal to be measured, and outputting the signal as an optical signal corresponding to the electrical signal; a waveform measurement unit (40) that measures the time waveform of the optical signal; and a waveform analysis unit (52) that finds the time waveform of the electrical signal on the basis of the measurement result of the optical signal. The waveform measurement unit (40) includes a streak tube (42) including a photoelectric surface to which an optical signal is input, a scanning electrode, and a fluorescent surface, an imaging element (44) that captures a fluorescent image generated on the fluorescent surface and outputs a waveform image, and an input optical system (41) that inputs the optical signal to the photoelectric surface, and the waveform analysis unit (52) obtains a temporal waveform of an electric signal on the basis of the waveform image. The input optical system (41) inputs the optical signal into the photoelectric surface as a linear optical image extending in a direction intersecting the scanning direction. Therefore, the electric signal measuring device and the measuring method can measure the time waveform of the electric signal at high speed.

The invention discloses a nuclear radiation personal dose intelligent acquisition and monitoring system, particularly relates to the field of nuclear radiation dose monitoring, and is used for solving the problems that the existing dose monitoring is rough in radiation signal analysis, lagged in exposure risk identification and difficult to carry out prospective evaluation under the conditions of a complex radiation field and a dynamic body position. According to the system, fine waveform analysis is carried out on original pulse signals collected by an individual detection unit, scattering characteristics and energy deposition are dynamically corrected in combination with individual real-time body position data, and an intermediate energy deposition spectrum reflecting interaction between radiation and human tissue is constructed; and the exposure situation in a future monitoring time window is predicted based on the evolution of spectrum characteristics, so that intelligent assessment and early warning of personal radiation exposure risks are realized, the real-time performance and reliability of nuclear radiation protection are improved, and the method is suitable for nuclear emergency disposal and high-risk operation scenes and supports continuous monitoring and rapid deployment application.

The invention discloses a mass seismic waveform data analysis multi-agent autonomous evolution method and system, and belongs to the technical field of seismic data automatic analysis and large model and multi-agent cooperation. The method comprises the following steps: firstly, designing a structured formula library and an abstract semantic vector library, calling a multi-agent collaborative execution graph in combination with user requirements, carrying out analysis based on the structured formula library and the abstract semantic vector library, generating a corresponding analysis report text, and after interaction is finished, according to a calculated weighted comprehensive score, carrying out multi-agent collaborative execution on the multi-agent collaborative execution graph; and performing versioning updating on the multi-agent collaborative execution graph. According to the method, high-parallel collaboration, knowledge enhancement and self-evolution scheduling of large-scale waveform analysis tasks are realized, manual dependence is reduced, and response efficiency and expandability of complex earthquake scenes are improved.

A hemodynamic monitor with nociception prediction and detection is disclosed. A hemodynamic monitoring system monitors arterial pressure of a patient and provides a warning of a nociceptive event of the patient to medical personnel. The system includes a hemodynamic sensor that generates hemodynamic data representative of a waveform of arterial pressure of the patient. A hardware processor in the system is configured to execute nociception detection software code to perform waveform analysis of the hemodynamic data to determine a plurality of signal measurements. Detection input features are extracted by the processor from the plurality of signal measurements indicative of a nociceptive event of the patient. A nociception score representative of a probability of the nociceptive event of the patient is determined from the detection input features. A sensory alarm is invoked in response to the nociception score satisfying a predetermined detection criterion.

This invention primarily relates to the field of embroidery machine technology. It discloses an adaptive spindle control method, device, equipment, and medium for an embroidery machine. The method includes: acquiring the spindle angle of the embroidery machine; when the spindle angle is within a preset presser foot working angle range, collecting acceleration signals during the contact process between the presser foot and the fabric via an acceleration sensor at the end of the presser foot drive rod; performing time-domain waveform analysis on the acceleration signals to extract characteristic parameters representing the fabric thickness and hardness, including at least the peak width and rebound ratio of the acceleration signal waveform; determining the impedance index of the current fabric based on the peak width and rebound ratio; and adjusting the torque limit and speed of the spindle motor of the embroidery machine to match the characteristics of the current fabric before the next stitch begins, based on the impedance index. This application can significantly reduce the incidence of faults such as broken needles, broken threads, and jamming, improving embroidery efficiency and finished product quality.

The invention relates to thermal effect simulation in electrical discharge machining, in particular to a simulation method for a temperature field and a thermal stress field in electrical discharge machining. The method comprises the steps of collecting data through discharge machining experiment parameters, analyzing a single pulse discharge waveform, carrying out material microstructure characterization through a scanning electron microscope and an electron back scattering diffraction technology, establishing a physical model comprising an electrode, a workpiece and a plasma channel, carrying out model simplification, and setting a heat conduction model and a heat source model. Electric and magnetic field boundary conditions and heat conduction and fluid heat transfer conditions of a discharge channel are set, thermodynamic coupling analysis is carried out in ABAQUS software, the distribution condition of a transient heat effect and a stress field in discharge machining is simulated, numerical solution is carried out in combination with experimental data, and therefore the change condition of the temperature of the surface of a workpiece and the change condition of the thermal stress field in the machining process are obtained. Through the method, the distribution of thermal stress can be accurately predicted, and a theoretical basis is provided for optimizing a processing technology and reducing generation of microcracks.

The invention discloses a lightning arrester resistive current measurement method and system, and relates to the technical field of lightning arrester monitoring, and the method comprises the steps: obtaining a current signal and an environment temperature of a lightning arrester, carrying out the signal preprocessing of the current signal, obtaining a corresponding target total current signal, carrying out the Fourier transform of the target total current signal, obtaining corresponding harmonic current data, and carrying out the calculation of the harmonic current data. And performing phase correction on the harmonic current data based on a preset reference zero point moment to obtain a plurality of corrected phases, and performing resistive current decoupling processing according to each corrected phase, the environment temperature and the harmonic current data to determine a corresponding resistive current. The technical problems that a traditional lightning arrester resistive current measurement method mainly comprises a fundamental wave method, a capacitive current compensation method and a waveform analysis method, but voltage signals need to participate in calculation, the test difficulty is large, the safety risk is high, and the reliability of resistive current measurement is reduced are solved.

This invention relates to the field of music education technology, specifically to a musical instrument playing training system and method based on real-time pitch monitoring. The system includes a musical instrument acoustic waveform analysis module, a tuning frequency tracking module, a pitch deviation analysis module, a muscle memory reshaping guidance module, and a musical instrument mastery blind spot positioning module. In this invention, by sensing the vibration envelope and reconstructing the energy distribution of the overtone series, the core musical tone components are accurately extracted from a complex acoustic environment, improving the accuracy of fundamental frequency identification and enhancing the ability to capture microscopic pitch drift. This effectively solves the problem of poor anti-interference in traditional detection methods, transforming pitch deviation into a concrete physiological cause analysis, optimizing the depth of performance mechanism analysis and enhancing the physical correction directionality. It achieves precise integration of correction actions and performance rhythm, ensuring the scientific nature of muscle memory reshaping and significantly reducing functional pitch deviation. By tracking the rate of movement deformation and tracing the source of technical bottlenecks, it optimizes musical instrument mastery and improves the level of intelligent music training.

The present application relates to the technical field of intelligent operation and maintenance, in particular to a rail transit low-voltage device adaptive testing and predictive diagnosis method and system. By applying electrical excitation and mechanical vibration stress to the device to be tested and collecting multi-dimensional time series data, then using a waveform analysis model to extract deep features to identify early faults, using a sequence prediction model to analyze the performance degradation trend, and based on this, dynamically generating an adaptive optimal test path through a reinforcement learning model. Finally, with the help of a domain knowledge graph, correlation reasoning is performed to generate a comprehensive diagnostic report containing fault root causes and maintenance recommendations. The present application deeply integrates artificial intelligence into the testing process, realizes the transition from passive measurement to active cognition, significantly improves the depth, efficiency and intelligence level of testing, and provides forward-looking and accurate protection for the reliability of rail transit low-voltage devices.

The invention discloses a medical gas system vortex air compressor fault prediction method and system and a medium, and relates to the field of air compressor fault prediction, in the method, multi-dimensional data such as a pressure pulsation frequency spectrum, a vibration envelope spectrum, a current waveform and a temperature change rate are obtained, and fault diagnosis is achieved from multiple dimensions of machinery, electricity and performance; vortex plate and bearing faults are identified through harmonic analysis of a pressure pulsation spectrum and vibration envelope spectrum characteristic frequency, electrical faults are analyzed and judged in combination with current waveforms, and the accuracy of fault diagnosis is improved. A performance abnormity evaluation system is established by introducing a compressed air flow ratio and a pressure-current correlation coefficient, and meanwhile, the system pollution risk is monitored in combination with a temperature change rate, a dew point and an oil vapor concentration, so that comprehensive monitoring of an equipment state and gas quality is realized; in addition, the deep learning model is adopted to comprehensively analyze and predict various fault features, potential faults are found in time, early warning signals are generated, and the operation reliability of the medical gas system is improved.

A method embodiment includes generating, via a sensor of a computing device, a signal representing vibrations originating from a blood vessel of a subject and decomposing the signal into one or more first intrinsic oscillatory modes and one or more second intrinsic oscillatory modes. The one or more first intrinsic oscillatory modes have respective oscillation frequencies that are less than respective oscillation frequencies of the one or more second intrinsic oscillatory modes. The method includes obtaining an intensity spectrum of the one or more first intrinsic oscillatory modes over a range of frequencies and using the obtained intensity spectrum to determine a blood volume status of the subject. Another method embodiment includes using the one or more second intrinsic oscillatory modes to determine one or more mechanical properties of the blood vessel or tissue adjacent to the blood vessel.