1456 results about "Vibration based" patented technology

A method for diagnosing rotor rub-impact in a rotating machinery based on vibration signal deconstruction and frequency modulation characteristic anti-noise enhancement, including: converting, based on two-stage integration transformation and high-pass filtering, vibratory acceleration signals from a rotating machinery device into vibratory displacement signals; performing targeted extraction on a rotating frequency component therein based on an improved variational mode decomposition method; calculating and estimating instantaneous fluctuation characteristics of a fundamental frequency of the extracted rotating frequency component using a quadrature-derivative-based normalized Hilbert transform to obtain an instantaneous frequency; inputting a calculated instantaneous frequency sequence into an optimum stochastic resonance system for anti-noise enhancement of intrawave frequency modulation characteristics; and processing FFT on an instantaneous frequency of intrawave frequency modulation characteristics after anti-noise enhancement, and diagnosing and identifying a rotor rub-impact fault of the rotating machinery based on a distribution characteristic of harmonic amplitudes related to a rotor rotating frequency.

The invention provides a thermal power plant thermal instrument fault diagnosis method and system based on vibration analysis, and relates to the technical field of fault diagnosis, and the method comprises the steps: collecting and processing a vibration signal of a thermal instrument, extracting an optimized vibration feature vector, constructing a hybrid neural network model, and training the hybrid neural network model to obtain an optimized fault diagnosis model; performing fault diagnosis on the vibration feature vector, and generating a fault type, possibility and confidence; and performing fault risk assessment and case reasoning, and generating a fault reason analysis report and a maintenance suggestion.

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 provides an earth surface anomaly monitoring data fusion system based on vibration signal processing, and relates to the technical field of earthquake precursor monitoring, and the system comprises a data collection module which is used for carrying out the real-time monitoring of the earth surface through a distributed multi-parameter sensor network, and obtaining the time sequence monitoring data related to the earthquake precursor; the signal processing module is used for processing the time sequence monitoring data by adopting a two-parameter joint optimization algorithm and outputting a separation signal component containing precursor information; the data fusion and state inversion module is used for carrying out fusion processing on the separated signal components by adopting a dual-time scale analysis framework and outputting a multi-scale precursor state estimation result and an uncertainty quantitative index; and the monitoring and early warning module is used for outputting earthquake precursor grading early warning information through a three-layer anomaly recognition architecture and probabilistic risk assessment. According to the invention, high-precision separation and identification of earthquake precursor related signals can be realized, and the false alarm phenomenon is reduced through an intelligent early warning mechanism.

The invention relates to the technical field of geological survey and measurement, in particular to a rock mass anomaly detection method based on vibration and visual data multi-modal fusion, which comprises the following steps: firstly, respectively acquiring vibration data and image data, and then performing data feature extraction and fusion through a deep learning model; specifically, a combined model of a long short-term memory network LSTM and a convolutional neural network CNN is used to extract time sequence features and frequency information of vibration data, a Swinin-T model is used to extract spatial features of image data, and then a shared Transform encoder and a cross attention mechanism are used to realize multi-modal data alignment and fusion. And inputting the fused features into an anomaly detection module for training and tuning, and finally deploying the trained model to edge equipment for anomaly detection of the rock mass. According to the invention, through multi-modal data fusion, abnormity can be accurately identified in various complex environments, so that the probability of false alarm and missing alarm is reduced, and meanwhile, by combining with an edge computing architecture, field deployment and real-time monitoring are facilitated, and the method is suitable for various application scenes.

The invention provides a pump equipment state monitoring and fault diagnosis method based on artificial intelligence, and relates to the technical field of data processing, and the method comprises the steps: obtaining a vibration signal of a target type of pump equipment based on a preset vibration sensor, and marking the vibration signal; extracting features of the vibration signal based on a preset dual-channel feature extraction model; iteratively training a preset basic fault diagnosis model based on the characteristics of the vibration signal until a preset training completion condition is reached; binding a preset number of fault diagnosis models to construct a pump equipment state reasoning model; acquiring an operation vibration signal of the pump equipment of the target category, inputting the operation vibration signal into the pump equipment state reasoning model, and outputting a fault category; through time-frequency dual-channel fusion and multi-scale perception, the fault identification precision is improved; the rationality and interpretability of the result are enhanced by using physical prior constraints; and through model integration optimization, the classification stability and reliability in a complex scene are improved.

The invention relates to the technical field of agricultural robot motion control, in particular to a mechanical arm posture stable adjusting method based on vibration feedback, which comprises the following steps: S1, collecting a vibration signal of an end effector of a mechanical arm in real time; s2, frequency domain analysis and amplitude-phase decomposition are conducted on the vibration signals, and vibration characteristic parameters of the mechanical arm in the X / Y / Z axis direction are extracted; s3, generating a multi-axis vibration vector in combination with the terrain inclination angle data; s4, attitude compensation parameters of all the joints are calculated; s5, a mechanical arm joint angle adjusting instruction is generated according to the posture compensation parameters; and S6, circularly executing the steps S1 to S5 until the vibration amplitude is lower than a preset threshold value. According to the method, the multi-axis vibration characteristics are extracted in real time, the joint compensation angle is calculated through the anti-offset and anti-overturning matrix, rapid and stable attitude control of the mechanical arm in the high-frequency disturbance environment is achieved, and therefore the tail end positioning precision and the overall operation reliability are remarkably improved.

The invention relates to the technical field of signal analysis and processing, in particular to an underground flow switch control method based on vibration signal analysis. The method comprises the steps that 1, vibration signals of a pump are collected through a three-axis vibration sensor, and acceleration time domain signals in the three-axis direction are obtained; performing multi-dimensional vibration spectrum component decoupling on the acceleration time-domain signal to obtain a time-frequency characteristic matrix of three-axis vibration; 2, calculating a Reynolds stress tensor based on the turbulence intensity; calculating a flow state transition criterion value according to the turbulence intensity and the Reynolds stress tensor, and establishing a flow estimation model; 3, according to the flow estimation model, dynamic response of underground flow switch control is generated; and according to the dynamic response, a self-adaptive control law for controlling the underground flow switch is generated by using a self-adaptive control method. According to the invention, the limitation of a traditional pressure sensing and flow sensing technology in a complex underground flow state is effectively overcome, and the real-time performance, the accuracy and the environmental adaptability of flow control are remarkably improved.

The invention relates to the field of industrial pipeline systems, in particular to a displacement analysis evaluation and compensation method for optimizing pipeline thermal expansion. The method comprises the following steps: collecting multi-temperature thermal state snapshots to form a thermal state change sequence; determining a thermal expansion constraint boundary according to the thermal state change sequence, and forming boundary condition data; fixed points and free points of the pipeline system are identified according to the boundary condition data, and a degree-of-freedom distribution diagram is constructed; based on the degree-of-freedom distribution diagram, analyzing a displacement transfer relation of each pipe section, and generating displacement transfer chain data; identifying a thermal expansion concentrated release area through displacement transfer chain data, and performing node resonance detection to form vibration risk assessment data; and designing a thermal stress digestion path based on vibration risk assessment data, and setting flexible constraints to form a balanced stress network. The thermal expansion displacement transmission path is tracked in real time through displacement transmission chain analysis, the resonance frequency is synchronously recognized in combination with vibration mode detection, and dynamic association of the thermal expansion process and the vibration mode is achieved.

The invention discloses a fatigue load response analysis method for a non-uniform beam of a ship, and particularly relates to the technical field of fatigue load response analys.The vibration coupling degree between the non-uniform beam and other parts of the ship is judged by obtaining the inherent frequency of the non-uniform beam and the other parts of the ship and calculating the vibration coupling degree coefficient; the method comprises the following steps of: acquiring load time-history data of a non-uniform beam, generating a dynamic load spectrum of the non-uniform beam, effectively capturing real load conditions of a ship under various working conditions, and calculating a fatigue damage coefficient of the non-uniform beam through local stress-strain analysis of the non-uniform beam so as to obtain a dynamic load spectrum of the non-uniform beam. A self-adaptive fatigue damage model is constructed based on the vibration coupling degree coefficient and the fatigue damage coefficient, the fatigue damage value of the non-uniform beam is dynamically corrected, the fatigue damage evolution process of the non-uniform beam under different working conditions is reflected through self-adaptive adjustment, and the dynamic property and accuracy of fatigue damage evaluation are improved.

The invention relates to the technical field of machine tool fault diagnosis and maintenance, and discloses a machine tool fault predictive maintenance method based on vibration analysis, which comprises the following steps: collecting vibration, temperature and acoustic emission signals and machine tool working condition parameters through a multi-modal sensor, extracting multi-domain features after preprocessing the vibration signals, and combining the working condition parameters through feature fusion and dimension reduction to obtain a machine tool fault predictive maintenance result. And establishing a fault classification model by using transfer learning, and performing hierarchical optimization. Model parameters are updated in real time based on an online learning mechanism, a fault early warning agent model is constructed to predict fault probability distribution, a dynamic threshold strategy is designed to avoid false report and missing report, and finally, related models and strategies are integrated to edge computing equipment. According to the method, multi-source data are integrated, multiple advanced algorithms are applied, machine tool faults can be accurately predicted, real-time monitoring and maintenance decision output are achieved, the machine tool operation reliability is improved, and the maintenance cost is reduced.

The invention relates to the technical field of engineering mechanical structure health monitoring, and discloses a carry-scraper structural member crack real-time detection system and method based on vibration characteristics, and the carry-scraper structural member crack real-time detection system based on vibration characteristics comprises a sensing acquisition module which is used for acquiring structural member vibration response and obtaining an original signal; the signal processing module is used for carrying out wavelet denoising on the original signal and extracting effective vibration data; the feature extraction and dynamic calibration module is used for extracting multi-domain features and performing dynamic calibration in combination with working conditions; the crack recognition and intelligent diagnosis module is used for inputting an intelligent model to recognize a crack state; and the crack positioning and alarm feedback module is used for positioning cracks and giving an alarm in combination with the diagnosis result. According to the invention, the multi-modal sensor array is constructed, piezoelectric and MEMS sensors are cooperatively arranged, a magnetic snap-in type installation structure is combined, broadband response signals are stably collected under different working conditions, and then front-end signal optimization is completed in cooperation with a multi-scale wavelet denoising and standardization mechanism.

The invention discloses a method for predicting blast furnace TRT equipment maintenance time based on an LSTM model, and the method comprises the steps: constructing a sliding window adaptive algorithm based on vibration signal change characteristics, and dynamically adjusting the size of an input data sliding window of the LSTM model according to the fluctuation amplitude and change rate of a vibration signal; adopting an adaptive sliding window to obtain TRT equipment training data, and training the LSTM model; applying the trained LSTM model to the vibration signals acquired in real time, predicting the vibration trend of the TRT equipment in the future n days at the current point, and outputting a predicted value of the vibration signals in a future period of time; and according to the prediction result of the vibration signal, in combination with a preset threshold value of shutdown maintenance, judging the time when the TRT equipment reaches the threshold value, and further obtaining the remaining days of the TRT equipment away from the maintenance. According to the method, the future development trend of the TRT equipment vibration signal is accurately predicted, the maintenance plan is reasonably arranged, and the TRT equipment operation reliability and production efficiency are improved.

The invention provides a subway train traction energy efficiency optimization data transmission control method and system. The method comprises the following steps: acquiring numerical values of vibration parameters when a traction motor of a subway train runs, and segmenting the numerical values of the vibration parameters according to preset time granularity; calculating a comprehensive weight index of each time sequence unit based on the vibration amplitude, the frequency distribution and the phase angle parameter, and generating a dynamic code rate hierarchical allocation strategy in combination with the real-time state of the network transmission bandwidth and a control instruction reservation mechanism; and establishing a multi-dimensional parameter association model based on the numerical value of the vibration parameter, a dynamic code rate hierarchical allocation strategy, the real-time power state of each power electronic device, and the torque output instruction and energy recovery threshold feedback parameter, and dynamically adjusting a data transmission rule through the multi-dimensional parameter association model. According to the technical scheme provided by the invention, the accuracy of subway train traction energy efficiency optimization data transmission control is improved.

According to the cooling tower early fault early warning method based on vibration state monitoring, vibration signals and working condition labels of key parts of the cooling tower are synchronously collected through multiple channels, and data quality is improved through preprocessing operation such as band-pass filtering and normalization; time-frequency features are extracted in a multi-scale mode through self-adaptive variational mode decomposition and wavelet packet transformation, signal complexity is quantized through energy entropy, and weak fault detection capacity is enhanced; the obtained features are input into a deep belief network after being subjected to principal component analysis dimensionality reduction, and automatic classification and recognition of the equipment operation state are achieved; dynamic early warning grade adaptation is carried out according to an identification result in combination with a working condition label, the environmental adaptability and stability of early warning are effectively improved, the method further has the functions of early warning sample recording and periodic model iterative optimization, and the fault identification precision and robustness in a complex noise environment are remarkably improved.

The invention relates to the technical field of electrical variable measurement, in particular to a power equipment system real-time dynamic monitoring method and system based on the Internet of Things. The method comprises the following steps: acquiring different data types; forming vibration sequences for the vibration signals, and obtaining fitting curve functions of all the vibration sequences; determining a trend sequence based on the variation coefficient of the vibration sequence; combining the trend sequence and the vibration sequence with a fitting curve function to determine a vibration trend coefficient; clustering the period based on the vibration trend coefficient, respectively obtaining a local change trend and an overall change trend based on cluster data and all data, and obtaining an equipment disturbance coefficient based on the local change trend and the overall change trend; and determining a prediction function based on the equipment disturbance coefficient, and obtaining a corrected vibration signal in combination with the vibration trend coefficient difference of adjacent periods, thereby completing dynamic monitoring. According to the invention, the monitoring accuracy of the power equipment fault is enhanced.

The invention discloses a method for testing dynamic comprehensive data of an electric cylinder in real time, and relates to the technical field of mechatronics test.The method comprises the steps that vibration signals, temperature data and electromagnetic interference data of the electric cylinder are collected and preprocessed, and a multi-modal data stream is generated; defining a spline basis function based on the vibration signal, and extracting a transient feature tensor of the vibration signal by using the spline basis function; constructing a transient environment state transition model based on the multi-modal data stream, inputting the transient characteristic tensor, the temperature data and the electromagnetic interference data into the transient environment state transition model, and generating a displacement compensation amount and a hidden state sequence; and constructing a digital simulation model, inputting the displacement compensation amount into the digital simulation model, and generating multi-physics field coupling simulation displacement. By constructing the spline basis function and the transient environment state transition model, the problem of vibration signal feature distortion is solved, and the extraction precision and the time resolution of the transient impact features are remarkably improved.

The invention discloses a fresh concrete vibration apparent quality identification method based on a convolutional neural network, and relates to the technical field of concrete apparent quality identification, and the method comprises the steps: obtaining vibration track data, vibration frequency parameters and a concrete surface texture image of vibration equipment in an operation process, and generating an original data set; performing model training optimization based on the initial convolutional neural network model to obtain a depth feature extraction network adaptive to the vibration process; analyzing time sequence detection data in the vibration process, extracting vibration energy density distribution characteristics, aggregate distribution uniformity indexes and slurry wrapping degree parameters, and constructing a vibration quality correlation characteristic matrix; in the depth feature extraction network, the vibration quality correlation feature matrix and vibration sensing data collected in real time are fused, and a quality evaluation system is established; according to the quality evaluation system, the vibration operation process is detected, and a visual evaluation result is obtained; the fresh concrete vibration apparent quality identification method provided by the invention is more intelligent and accurate.

The present application discloses a method for calculating the risk of failure of a component on the basis of vibration load simulation, comprising: inputting a determined PSD spectrum load into predetermined target software, so as to convert the PSD spectrum load into initial time domain information; executing a signal repetition operation, obtaining target time domain information, and calculating fatigue damage information; converting the information into target frequency domain data, and generating compressed spectrum data; and inputting the data into a vibration simulation model, obtaining a model output result, and determining a risk of failure evaluation result of a target component.

The invention discloses a two-phase cold plate heat dissipation control method and system based on vibration and flow coordinated regulation and control, and relates to the technical field of liquid cooling heat dissipation. The two-phase cold plate heat dissipation control method and system based on vibration and flow collaborative regulation and control comprises the steps that S1, cold plate collaborative heat dissipation data is collected in real time, and the cold plate collaborative heat dissipation data is preprocessed; s2, the heat dissipation degradation degree of the two-phase cold plate is evaluated, heat dissipation state classification judgment is executed, and a regulation and control instruction is issued; s3, executing the control instruction, evaluating the regulation and control response state of the cooling path, judging the regulation and control effect, and triggering a time-sharing coordinated regulation and control strategy; and S4, whether heat dissipation of the cooling path fails or not is judged, the thermal degradation trend of the two-phase cold plate is evaluated, and a liquid path switching strategy is executed. The problems that in an existing cold plate liquid cooling heat dissipation technology, oil films are generated by cooling liquid due to temperature changes, the oil films are prone to being attached to the inner walls of micro-pore channels, flowing is blocked, the heat exchange efficiency is reduced, and local overheating and unstable equipment performance are further caused are solved.

The invention provides a physiological function monitoring method and system based on multiband laser, a terminal and a medium, and particularly relates to the technical field of optical and physiological monitoring, and the scheme comprises the following steps: based on a preset space-time mapping model, using a camera to collect an out-of-focus speckle image sequence of a target monitoring object under the irradiation of two laser lamps within a preset time period; and based on the out-of-focus speckle image sequence, obtaining a vibration signal of the target monitoring object corresponding to the laser of each wave band so as to obtain a target physiological parameter monitoring value. According to the scheme, the multi-spectrum and out-of-focus speckle imaging technology is combined, non-contact precise tracing of the motion of the target monitoring object is achieved through the space-time coupling of the dual-band light spot flow caused by the single-point motion of the target monitoring object, the precision of the target physiological parameter monitoring value calculated based on the vibration signal is improved, and the accuracy of the target physiological parameter monitoring value is improved. And meanwhile, the tracing flexibility is improved, and the robustness of the monitoring model is effectively enhanced.

The invention proposes an optical cable burial depth detection method based on a distributed optical fiber sensing technology, and relates to the technical field of optical fiber sensing, and the method comprises the steps: collecting the burial state change data of an optical cable, and the burial state change data comprise temperature data and vibration data; constructing a spatio-temporal data matrix corresponding to the temperature data, carrying out data processing on the spatio-temporal data matrix according to a sliding window algorithm, obtaining spatial temperature distribution data, selecting a plurality of reference features of the spatial temperature distribution data in any time period to construct a feature matrix, and sending the feature matrix into an optical cable temperature-burial depth relation model, obtaining an optical cable prediction depth corresponding to the temperature data; constructing a ground noise environment model based on the vibration data, obtaining a noise field distribution characteristic function corresponding to the ground noise environment model based on the ground noise environment model, performing Fourier transform on the noise field distribution characteristic function to obtain a noise correlation function, and obtaining a ground environment parameter according to the ground environment parameter and the noise correlation function. Calculating an optical cable prediction depth corresponding to the vibration data; and calculating the burying depth of the optical cable based on the optical cable prediction depth corresponding to the temperature data, the optical cable prediction depth corresponding to the vibration data and a depth prediction algorithm. According to the invention, the accuracy of predicting the embedding depth of the optical cable can be improved.

The invention relates to the technical field of combustion equipment state monitoring, in particular to a combustion state monitoring method and system based on vibration and noise fusion analysis, and the method comprises the steps: collecting a combustion signal in real time through a vibration sensor and a broadband noise sensor, extracting a characteristic frequency band through noise reduction and filtering, and obtaining a combustion state; fPGA hardware-level clock synchronization is adopted to realize time alignment of bimodal signals, a vibration-noise joint feature matrix is constructed and multidimensional coupling is carried out, a combustion state feature vector is generated through a mixed deep learning model, a combustion state is determined based on a bimodal confidence weighting decision, and a linkage control instruction is generated during interruption. The system comprises a signal acquisition synchronization unit, a feature fusion and modeling unit, a state decision and instruction generation unit and a safety linkage execution unit, executes a method and activates a standby safety system, solves the problems of asynchronous bimodal data and insufficient monitoring precision, and improves the combustion state monitoring accuracy and safety response efficiency.

The invention provides a motor magnetic steel sheet defect detection method based on vibration analysis, and relates to the technical field of motor fault diagnosis, and the method comprises the steps: carrying out the vibration feature extraction of a plurality of vibration data sets, inputting a plurality of vibration feature sets into a defect feature recognition library, carrying out the similar traversal comparison, and outputting a plurality of initial defect features; and collecting temperature distribution characteristics and sound wave characteristics of the target motor in a predetermined period, carrying out credible probability analysis on the plurality of initial defect characteristics, and outputting the initial defect characteristic with the maximum probability as a motor magnetic steel sheet defect detection result. According to the method and the device, the technical problems that a traditional motor magnetic steel sheet defect detection method cannot quickly and accurately identify the defect characteristics of the motor magnetic steel sheet during the operation of the motor, and the defect detection timeliness and accuracy are insufficient can be solved; and the efficiency, the accuracy and the reliability of defect detection of the magnetic steel sheet of the motor can be remarkably improved.

The invention discloses a bionic TENG sensor unit for an artificial throat and a preparation method thereof, and relates to the field of artificial throats, and the sensor unit comprises a gradient micro-dome friction layer, a strain amplification layer, an electrode layer and a back cavity substrate layer. The gradient micro-dome array realizes gradual contact through gradient change of height and diameter; the strain amplification layer converts vertical vibration into horizontal strain by using a cantilever beam-sliding block structure; the back cavity array selectively amplifies vibration at a specific frequency based on the Helmholtz resonance principle. The invention further provides a corresponding preparation method. The preparation method comprises the steps of back cavity forming, electrode deposition, microstructure manufacturing, surface treatment and the like. The sensor has the advantages of high sensitivity, good frequency selectivity and strong durability, and is particularly suitable for detecting weak laryngeal vibration signals of laryngeal patients.

The invention relates to the technical field of vibration test, and discloses a micro-seismic environment simulation vibration test board and a test method thereof, which realize high-fidelity simulation of multiple types of micro-seismic scenes through a multi-source composite signal generation and waveform reconstruction technology. The dynamic adjustment capability of the control strategy is improved by using disturbance compensation and an adaptive control mechanism, real-time analysis and early warning feedback are performed on the response state in the test process based on the vibration characteristic evaluation result, and an auxiliary decision basis is provided for the vibration control strategy. Besides, a hierarchical closed-loop regulation and control mechanism is logically formed based on local output regulation among multiple axes and a global control strategy, and closed-loop linkage of the control strategy, execution response, state evaluation and control correction is realized, so that the overall regulation and control precision, response sensitivity and test precision are remarkably improved; accurate simulation and stable control of a three-dimensional vibration field are realized.

The invention relates to a temperature vibration cross-linking coupling compensation method and system for a pressure scanning valve and a medium, and the method obtains an output pressure value of any channel of the pressure scanning valve after cross-linking coupling compensation at any moment, and comprises the steps: obtaining a pressure measurement value of the current channel of the pressure scanning valve at the current moment, and taking the pressure measurement value as a first pressure value; obtaining an influence coefficient of the pressure sensor temperature of the current channel at the current moment on zero drift as a temperature compensation influence coefficient; acquiring the vibration amplitude of the current channel at the current moment, and acquiring a vibration compensation coefficient based on the vibration amplitude; and calculating a second pressure value based on the first pressure value, the temperature compensation influence coefficient and the vibration compensation influence coefficient, and taking the second pressure value as an output pressure value of any channel at any moment. The two-dimensional comprehensive compensation of temperature and vibration can be realized, the interaction of the factors can be reflected more accurately, and a more comprehensive compensation solution is provided, so that the accuracy of the obtained pressure value is improved.

The invention relates to the technical field of rolling technology and equipment detection, in particular to a method, a system and equipment for detecting deviation of a roll gap of a rolling mill, and the method comprises the steps: constructing a vibration detection center line parallel to a roll axis of a working roll according to a to-be-processed copper material, and according to the vibration detection center line, roller vibration characteristics are acquired by taking each working roller as a unit, a vibration characteristic matrix is generated according to the machining time of the corresponding working roller, and vibration deviation comparison is performed on the vibration characteristic matrix corresponding to each working roller by establishing a reference vibration deviation model, so that a roller gap deviation detection result is obtained. The problem that weak vibration changes under high-speed and high-load working conditions are difficult to capture in roll gap deviation is effectively solved, a reference vibration deviation model is established through dynamic monitoring and analysis based on vibration signals, efficient and accurate roll gap deviation detection is achieved, and the stability of the production process is guaranteed.

The invention provides a municipal bridge damage defect detection method and a municipal bridge damage defect detection system, which are used for solving the problem that the quality and the accuracy of acquired data are difficult to guarantee due to the fact that vibration signals are easily interfered by environmental noise, such as vehicle load, wind load and the like. A bridge key part vibration signal data set is obtained, and a final damage area range is determined based on the vibration signal data set; whether the bridge is damaged or not is judged based on recognition of the vibration signals, so that the signal-to-noise ratio of monitoring data and the damage positioning precision are improved, a more accurate and reliable decision basis is provided for a bridge management department, and finally maintenance and reinforcement work of the bridge is effectively guided.

The invention relates to the technical field of equipment fault detection, in particular to an equipment fault detection method based on a vibration acoustic method and an inspection system. The method comprises a data acquisition step, a preprocessing step, a grading step, a first judgment step, a fault determination step, a vibration intervention step, a second judgment step, a risk early warning step and a normal detection step. The inspection system comprises a data acquisition module, a preprocessing module, a grading module, a first judgment module, a fault determination module, a vibration intervention module, a second judgment module, a risk early warning module and a normal detection module. The method has the effect of improving the accuracy of a fault detection result.