39 results about "Negative pressure wave" patented technology

The invention discloses a petroleum pipeline abnormity early warning and monitoring method and system based on a multi-source data fusion attention mechanism and a petroleum pipeline abnormity early warning technology, and aims to realize early-stage tiny leakage abnormity detection of a petroleum pipeline through a multi-source data fusion technology and a deep learning technology. According to the technical key points, historical data of pipeline operation states are collected from sound wave, temperature, negative pressure wave and vibration sensors, and a data set is constructed; obtaining a time sequence segment code for algorithm training; constructing a fusion attention module for algorithm training; on the basis of the obtained multi-source data, an attention Transform model is fused, and an adversarial learning method is adopted for training so as to predict the future operation state of the petroleum pipeline; training a petroleum pipeline early warning model by using Gaussian distribution with learnable scale parameters; the trained multi-source data is fused with an attention Transform model to serve as a petroleum pipeline abnormity early warning model, future operation state abnormal behaviors are recognized, and the failure probability is obtained. According to the method, the multi-source data analysis technology and the deep learning technology are fused, and the innovative Gaussian kernel scaling parameters and the failure probability calculation method are combined, so that the accuracy and reliability of early warning of the abnormity of the petroleum pipeline are effectively improved, and the method particularly has important application value in the aspect of micro leakage detection.

The invention discloses an underground pipeline intelligent leakage detection method and device, and relates to the field of intelligent leakage detection.The method comprises the steps that a high-frequency pressure sequence of a target pipe section is collected, and based on the Boltzmann superposition principle, the high-frequency pressure sequence is used for constructing a neural lag operator network by taking the environment temperature as a heat rheological regulation factor; taking the full historical stress-strain memory field tensor as input, carrying out non-local memory characteristic and hysteresis loop analysis, carrying out point-by-point differential processing on a high-frequency pressure sequence and the intrinsic nonlinear hysteresis response signal of the pipe, stripping non-stationary background fluctuation, avoiding modulation interference of material nonlinearity on a fluid signal, and obtaining a non-linear hysteresis response signal of the pipe. The method comprises the following steps: extracting the abnormal residual error of the fidelity fluid dynamics, constructing a spatio-temporal evolution Poincare section, inputting the spatio-temporal evolution Poincare section into a convolutional neural network, judging the real leakage negative pressure wave and the random drift of the sensor through the morphological difference of attractors on the spatio-temporal evolution Poincare section, and eliminating the periodic false alarm caused by the rheological property of the material.

The invention relates to the field of high-speed railway tunnel aerodynamics, in particular to a high-speed railway tunnel transient pressure wave efficient generator based on a harmonic synthesis method, which comprises a transient pressure wave generation module, a data pre-processing module, a core processing module, a transient pressure wave output module and a target experiment model, the transient pressure wave generation module generates a positive pressure waveform and a negative pressure waveform; the data pre-processing module is used for calculating characteristic data of field test pressure waves; the core processing module adopts a harmonic synthesis method to reconstruct pressure wave data, and sends pressure wave time history data to the transient pressure wave output module to generate positive pressure waves and negative pressure waves; and the transient pressure wave output module is connected with the core processing module and is used for outputting the positive pressure wave and the negative pressure wave to a target test model. The generator can efficiently simulate pressure waves under various driving conditions, and time-frequency information of actual pressure waves is reserved, so that the test efficiency and precision are improved.

The invention relates to the field of high-speed railway tunnel aerodynamics, in particular to a tunnel train-induced pressure wave refined generation device based on short-time Fourier transform, a positive and negative pressure generation module is used for generating positive pressure waves and negative pressure waves generated when a high-speed train passes through a tunnel; the central control module is connected with the positive and negative pressure generation module, and the central control module is used for generating pressure waves with the same frequency as the tunnel field transient pressure waves and controlling the generation frequency, intensity and duration of the pressure waves through a short-time Fourier transform program; and the pressure wave output module is connected with the central control module and is used for outputting the generated positive pressure wave and negative pressure wave to a target test model. The system has the advantages of being high in automation degree, high in adaptability and wide in practicability, accurate simulation can be conducted according to different tunnel structures and working conditions, and scientific support is provided for tunnel design and train operation safety.

The invention discloses a method and system for positioning gas pipeline leakage by combining an XGBoost algorithm and negative pressure waves, and belongs to the field of natural gas pipeline safety guarantee. The method comprises the following steps: 1) reading signals such as pressure and flow of each valve chamber and station yard through an SCADA system, and preprocessing the signals; 2) inputting the time sequence signal into a pre-trained XGBoost model, and predicting a leakage position and an aperture; 3) calculating the flow and pressure of the leakage point; 4) determining upstream and downstream on-way pressure drop of the leakage point; (5) the pipeline is segmented, and the gas flow rate and the pressure wave propagation speed are calculated segment by segment; and 6) calculating to obtain the signal time difference of the front and back stations, and further accurately positioning the position of the leakage point. Meanwhile, the device is provided with a leakage positioning system and can be used for detecting the pipeline leakage condition in real time and quickly positioning leakage points. Compared with a traditional method, the method has the advantages of accurate positioning, clear process, high operability and the like, and is suitable for being applied by technicians in the field.

The invention belongs to the technical field of pipeline leakage monitoring, and particularly relates to an oil and gas long-distance transmission pipeline leakage alarm system which is composed of a detection sensor, a data acquisition and transmission device, an analysis and calculation module, an upper monitoring system and an alarm system. The detection sensor is mainly used for monitoring the pipeline pressure, negative pressure waves generated when leakage occurs can cause jumping of the pipeline pressure, at the moment, the data acquisition and transmission device records pressure jumping information, and the accurate occurrence time is recorded through the Beidou timing device. According to the method, a leakage point is collected through a 4G wireless network or a special communication optical fiber, the collected information is transmitted to an analysis and calculation module through a 4G wireless network or a special communication optical fiber, the analysis and calculation module calculates the position of the leakage point through negative pressure wave time difference between the upstream point and the downstream point of the leakage point, the information is displayed on an upper monitoring system, and an alarm is triggered at the same time. Related persons in charge are notified to process in the forms of sound, light, short messages and the like.

The invention relates to a water pipe leakage detection method based on a distributed fiber bragg grating sensing network, and belongs to the technical field of cross application of optical fiber sensors and artificial intelligence. In the process of the method, firstly, a three-dimensional fluid dynamic simulation model of the water conveying pipeline system is established, and training data are generated; secondly, the fiber grating sensors are laid along the pipeline in a distributed mode to form a real-time monitoring network which is used for collecting negative pressure wave signals generated by leakage; then, aiming at a signal noise problem, innovatively adopting a variation mode decomposition method (VMD) combining a genetic algorithm (GA) and a correlation coefficient (CC) to carry out intelligent noise reduction and reconstruction on the signal; then, dimension reduction is carried out on the reconstructed signal by using a compressed sensing (CS) technology so as to reduce the calculation complexity; then, the signals after dimension reduction are converted into logarithmic spectrograms, the logarithmic spectrograms are input into a deep residual neural network (ResNet) for feature extraction, and then the signals are accurately classified into noise, normal, slight leakage, moderate leakage and serious leakage through a two-dimensional convolutional neural network (2D CNN); and finally, according to a classification result and the position of the sensor, calculating and positioning a leakage point by using a negative pressure wave method. According to the method, by fusing advanced signal processing and deep learning technologies, the accuracy, robustness and intelligent level of leakage detection are remarkably improved, and effective monitoring of the health state of the water conveying pipeline is achieved.

A physics-informed data-driven oil and gas pipeline network fault diagnosis method and system for type-imbalanced data scenarios, relating to the field of oil and gas pipeline network fault diagnosis, and aiming at solving the overfitting problem of current intelligent fault diagnosis models during processing of oil field type-imbalanced data sets, and reducing the risks of false alarms and missed alarms. Main steps are as follows: deeply understanding propagation and attenuation mechanisms of negative pressure waves when oil and gas move in a pipeline network, and establishing a negative pressure wave attenuation physical model reflecting the operating state of the pipeline network; on the basis of a long short-term memory network, constructing a deep generative adversarial model suitable for processing time series data; designing a reasonable series-parallel mechanism to fuse the physical model and a data-driven model, so as to construct a hybrid generative adversarial model; using the trained hybrid generative adversarial model to generate pipeline network fault data, and balancing an original training set; and training an intelligent fault diagnosis model to achieve pipeline fault type identification. The present invention considers both the prior knowledge from the physical model and the learning capability of the data-driven model and integrates same, and compared with simple data-driven models, uses the prior knowledge of the pipeline network contained in the physical model to reduce a parameter space search domain, thus reducing the number of estimated parameters, improving the interpretability and generalization performance of a deep generation model, and improving the physical rationality and feature distinguishability of generated fault data. Therefore, the present invention effectively overcomes the negative impact of type-imbalanced data sets on the performance of diagnosis models, further improving the accuracy of intelligent fault diagnosis of pipelines.

The application discloses a kind of methods and systems for locating gas pipeline leakage combined with XGBoost algorithm and negative pressure wave, belong to natural gas pipeline safety guarantee field.The method includes the following steps: 1) by SCADA system reads each valve chamber and station pressure, flow and other signals, and it is preprocessed;2) time series signal is input into pre-trained XGBoost model, and the leakage position and aperture are predicted;3) the flow and pressure of leakage point are calculated;4) determine the pressure drop along the way of upstream and downstream of leakage point;5) the pipeline is segmented and the gas flow rate and pressure wave propagation velocity are calculated segment by segment;6) the signal time difference of front and rear station is calculated, and the position of leakage point is further accurately positioned.The application also has a leakage positioning system, which can be used for real-time detection of pipeline leakage, and quickly locates the leakage point.Compared with the traditional method, the application has the advantages of accurate positioning, clear process and strong operability, and is suitable for application by the person skilled in the art.

This invention discloses a method and system for monitoring leaks in urban low-pressure gas pipelines based on negative pressure waves, comprising: S1, acquiring the medium-pressure signal of the medium-pressure pipeline upstream of the pressure regulating box and the low-pressure signal of the low-pressure pipeline downstream of the pressure regulating box; S2, performing discrete wavelet transform on the medium-pressure signal and the low-pressure signal; S3, if the low-pressure signal shows a sudden pressure drop, it is determined that a low-pressure gas leak has occurred under no-gas-use conditions; otherwise, proceed to step S4; S4, if the maximum fluctuation amplitude of the detail signal after two splits of the medium-pressure signal exceeds a set value or the low-pressure signal shows low-frequency changes, it is determined that a low-pressure gas leak has occurred under gas-use conditions. This invention, based on negative pressure waves, only requires collecting and analyzing the internal pressure of the pipeline to achieve real-time online monitoring. Compared with traditional manual inspections, it saves manpower, is highly accurate, safe, and convenient, and has a short response time and high processing efficiency in leak situations.

The invention discloses a pipeline leakage identification system and method and a storage medium, the system comprises a plurality of data acquisition modules, a remote data transmission module and a cloud server, the plurality of data acquisition modules are respectively and correspondingly arranged on a plurality of detection points of a pipeline, and each data acquisition module comprises a pressure sensor and a flowmeter; the sensors are respectively used for collecting negative pressure wave signals of leakage points and flow signals of corresponding detection points; the remote data transmission module is used for remotely controlling the running state of each data acquisition module and controlling to upload the acquired data in real time; and the cloud server is used for determining the time difference of the negative pressure wave signal of the leakage point reaching each pressure sensor based on the received acquisition data, determining the position of the leakage point according to the time difference and the pre-calibrated negative pressure wave signal propagation speed, and verifying the position of the leakage point through the change of the flow signal. According to the method, the pipeline leakage identification accuracy and efficiency can be improved, and accurate positioning of leakage points can be realized.

This invention relates to the field of analysis and early warning, and discloses an IoT-based regional water supply network leakage analysis and early warning system. The system includes a partitioning module, a fusion analysis module, and an early warning module. The system collects data through sensor arrays deployed in the network partitions. The fusion analysis module executes a negative pressure wave localization algorithm and an acoustic correlation analysis algorithm in parallel, generating first and second localization results respectively. By calculating the Euclidean distance between the two results and comparing it with a preset tolerance, it determines whether to output localization information or trigger model reverse verification. During verification, a dynamic hydraulic model takes real-time data as input, iteratively fits and outputs a virtual leak location, and outputs the final result after a second distance verification. The early warning module generates tiered early warning information based on the analysis results and displays it visually. This invention, by combining multi-algorithm fusion verification with hydraulic model reverse verification, significantly improves the accuracy and reliability of leakage localization and effectively reduces the false alarm rate.

The application discloses a long-distance water delivery pipeline online water hammer tracing method based on negative pressure waves, comprising the following steps: obtaining a pipeline pressure signal of a long-distance water delivery pipeline; preprocessing the pipeline pressure signal of each key node; smoothing filtering the preprocessed signal through an S-G filter; taking the window length, polynomial order and derivative order of the filter as adjustable parameters; adaptively searching and adjusting the adjustable parameter combination; obtaining a filtering result; constructing a comprehensive score function based on the filtering result; screening an optimal parameter combination according to the comprehensive score function; performing difference operation and continuous same sign judgment on the filtering signal of the optimal parameter combination; obtaining a negative pressure wave arrival time; and obtaining a water hammer tracing result according to the negative pressure wave arrival time. The method utilizes adaptive filtering, continuous same sign criterion and the collaborative calculation of a multi-sensor pair, reduces the influence caused by the abnormality of individual sensors or local errors, and improves the real-time performance and reliability of water hammer tracing in long-distance water delivery projects.

The invention discloses a method for monitoring leakage of a fluid pipe network based on the shortest path planning, comprising the following steps: establishing a propagation model of a negative pressure wave in a single straight pipe; mapping key points of the pipe, obtaining the longitude and latitude coordinates of several points, making a pipe network distribution data table, establishing an adjacency matrix according to the connection relationship between different points, and making a pipe network diagram in MATLAB; taking points at fixed intervals in all pipe sections of the pipe network, and subdividing the entire pipe network into several discrete points; solving the shortest path from each discrete point to each deployed sensor, and then calculating in turn the arrival time of the negative pressure wave signal detected by each deployed pressure sensor when a leak occurs at each discrete point. The arrival time is formed into a data column, and a time delay standard library is established therefrom. When a leak actually occurs at a certain position, the arrival time of the negative pressure wave measured by each sensor is compared with the time delay standard library, and the leak point coordinates are screened.

A system (3) utilizing internal and external online, real-time direct leak detection based on applying fiber optic sensors (4) to detect leak generated acoustic waves (6) or negative pressure wave inside the pipeline (5) and utilizing the same fiber optic sensing cable (9) or a different fiber optic sensing cable (14) to detect leak outside of the pipeline. This approach encompasses two different fiber optic technologies to detect leaks both internally and externally and involves quasi¬ distributed and / or distributed sensing techniques. This system will detect leaks internally or intrinsically by sensing the leak generated acoustic signals propagated inside tubulars, pipes, pipelines, flow channels, or any pressurized system, as well as externally or extrinsically by detecting leak released fluid or leak event associated phenomena outside of the leaking systems respectively. These two technologies may detect leaks separately or jointly.

The invention belongs to the technical field of AI fusion positioning methods, and particularly relates to a tiny negative pressure wave-AI fusion positioning method for a long-distance heat supply pipe network, which comprises the following steps: dividing a plurality of logic straight pipe sections along a main line of the long-distance heat supply pipe network by taking a heat source, a tee joint, an elbow and a valve as nodes, and arranging a pair of high-frequency pressure sensors at each of two ends and adjacent nodes of each straight pipe section; a high-frequency pressure sensor outputs a current signal, the current signal is converted into a 1-5V voltage signal through a precision resistor, the 1-5V voltage signal is accessed to a synchronous data acquisition card of an edge AI node to form distributed closed-loop data acquisition, the acquired voltage signal is denoised by adopting a db6 wavelet five-layer soft threshold value, a time difference delta t of a leakage wave reaching an upstream sensor and a downstream sensor is extracted through a cross-correlation function, and the time difference delta t of the leakage wave reaching the upstream sensor and the downstream sensor is calculated. Elbow reflection secondary peak identification is introduced to eliminate reflection wave interference, and a formula is adopted to calculate the real-time wave velocity to carry out wave velocity real-time correction.

A kind of oil pipeline abnormal early warning monitoring method and system based on multi-source data fusion attention mechanism, oil pipeline abnormal early warning technology, to realize the early small leakage anomaly detection of oil pipeline by multi-source data fusion technology and deep learning technology.Technical points: collect pipeline operation state historical data from acoustic wave, temperature, negative pressure wave, vibration sensor, construct data set;Get time series segmentation code for algorithm training;Build fusion attention module for algorithm training;Based on the obtained multi-source data fusion attention Transformer model, adopt the training method of adversarial learning to predict the future operation state of oil pipeline;Use the Gaussian distribution with learnable scale parameter to train the oil pipeline early warning model;Use the trained multi-source data fusion attention Transformer model as the oil pipeline abnormal early warning model to identify the abnormal behavior of future operation state and obtain the failure probability.The present application effectively improves the accuracy and reliability of oil pipeline abnormal early warning by combining multi-source data analysis technology and deep learning technology, and using innovative Gaussian kernel scaling parameter and failure probability calculation method, which has important application value in small leakage detection.

The invention belongs to the technical field of leakage monitoring of long-distance hot water transmission pipe networks, and particularly relates to a method for quickly correcting the wave velocity of negative pressure waves of a long-distance hot water transmission pipe network based on temperature and pressure table look-up, which comprises the following steps of: S1, deploying a pressure sensing assembly and a data acquisition unit on a specified pipe section of the long-distance hot water transmission pipe network, the pressure sensing assembly is used for capturing negative pressure wave signals in the pipe network, and the data acquisition unit is used for acquiring temperature parameters and pressure parameters in the operation process of the pipe network. Through the flow method of deploying the sensing and collecting assembly, constructing the associated data table, interpolating and correcting the wave velocity in real time and calculating the leakage position, the industrial pain point that high precision depends on high cost and low cost and cannot be corrected in real time in the field of leakage monitoring of the long-distance hot water transmission pipe network is fundamentally solved, and the leakage monitoring accuracy of the long-distance hot water transmission pipe network is improved. And wave velocity correction is realized only through the incidence relation of the temperature, the pressure and the wave velocity without depending on complex prior parameters such as pipe network topology and friction resistance coefficients, so that the technical scheme is greatly simplified.

The invention provides a product oil skid-mounted station oil storage tank leakage prevention system, which relates to the technical field of oil storage tank leakage prevention and comprises a distributed negative pressure wave sensing network, a central signal processing unit and a human-computer interaction interface. The sensing network is composed of a plurality of sensors arranged on the outer wall of the storage tank and key nodes of a pipeline, the central processing unit collects background signals to establish a steady-state pressure base line, and abnormal conditions conforming to negative pressure wave characteristics are recognized by dynamically comparing real-time signals; calculating the spatial position of the leakage point by adopting a time difference of arrival algorithm based on the time difference of the signals arriving at different sensors; and multi-stage alarm is carried out according to the ratio of the signal amplitude to the baseline noise. Millisecond-level response and accurate positioning of tiny leakage are achieved, an emergency cut-off system can be linked, and the problems of hysteresis and positioning fuzziness of traditional liquid level monitoring are effectively solved.

The invention discloses a pipeline leakage real-time monitoring system, method and equipment and a medium, and belongs to the technical field of pipeline monitoring. The system comprises a double-pressure wave sensor module used for collecting pressure wave signals of a pipeline, and the pressure wave signals comprise transient negative pressure waves in the initial leakage stage of the pipeline and steady-state pressure waves in the continuous leakage stage of the pipeline; the signal processing module is used for processing the transient negative pressure waves and the steady-state pressure waves and extracting leakage parameter characteristics through time-frequency analysis; the data processing module is used for calculating the position of a leakage point by utilizing a time delay estimation method according to the leakage parameter characteristics; and the alarm communication module is used for sending out a leakage alarm signal when leakage is confirmed, and uploading the leakage point position and the monitoring data to a cloud end. The technical problem that pipeline leakage monitoring and positioning are not accurate is solved.

The invention discloses a negative-pressure-wave-based online water hammer tracing method for a long-distance water conveying pipeline, and the method comprises the steps: obtaining a pipeline pressure signal of the long-distance water conveying pipeline, carrying out the preprocessing of the pipeline pressure signal of each key node, carrying out the smooth filtering of the preprocessed signal through an S-G filter, and obtaining a pipeline pressure signal of the long-distance water conveying pipeline; and taking the window length, the polynomial order and the derivative order of the filter as adjustable parameters, performing adaptive search and adjustment on an adjustable parameter combination to obtain a filtering result, constructing a comprehensive scoring function based on the filtering result, and screening an optimal parameter combination according to the comprehensive scoring function to obtain a filtering result. And performing differential operation and continuous same-sign judgment on the filtering signal of the optimal parameter combination to obtain a negative pressure wave arrival time, and obtaining a water hammer traceability result according to the negative pressure wave arrival time. According to the method, by means of cooperative calculation of adaptive filtering, continuous same-sign criteria and multiple sensor pairs, the influence caused by abnormity or local errors of individual sensors is reduced, and the real-time performance and reliability of water hammer tracing in the long-distance water conveyance project are improved.

A system utilizing internal and external online, real-time direct leak detection based on applying fiber optic sensors to detect leak generated acoustic waves or negative pressure wave inside the pipeline and utilizing the same fiber optic sensing cable or a different fiber optic sensing cable to detect leak outside of the pipeline. This approach encompasses two different fiber optic technologies to detect leaks both internally and externally and involves quasi-distributed and / or distributed sensing techniques. This system will detect leaks internally or intrinsically by sensing the leak generated acoustic signals propagated inside tubulars, pipes, pipelines, flow channels, or any pressurized system, as well as externally or extrinsically by detecting leak released fluid or leak event associated phenomena outside of the leaking systems respectively. These two technologies may detect leaks separately or jointly.

The invention relates to the field of analysis and early warning, and discloses a regional water supply network leakage analysis and early warning system based on the Internet of Things, which comprises a partition module, a fusion analysis module and an early warning module. The system collects data through a sensor array arranged in a pipe network partition, a fusion analysis module executes a negative pressure wave positioning algorithm and an acoustic correlation analysis algorithm in parallel, and a first positioning result and a second positioning result are generated respectively; and judging whether to output positioning information or trigger model reverse verification by calculating the Euclidean distance between the two and comparing the Euclidean distance with a preset tolerance. And during verification, the dynamic hydraulic model takes real-time data as input, outputs a virtual leakage point position through iterative fitting, and outputs a final result after secondary distance verification. And the early warning module generates graded early warning information according to the analysis result and visually displays the graded early warning information. According to the method, multi-algorithm fusion verification and hydraulic model reverse verification are combined, so that the precision and reliability of leakage positioning are remarkably improved, and the false alarm rate is effectively reduced.

The invention discloses a multi-mode sensing fusion pipeline micro-leakage accurate positioning system and method, and relates to the field of pipeline safety monitoring. The system comprises a multi-mode sensing array, an edge positioning host and a cloud diagnosis platform. The multi-modal sensing array collects acoustic and negative pressure wave modal sensing signals, after the edge positioning host preprocesses the signals, the multi-modal abnormal probability judgment module fuses depth time-frequency features and statistical waveform features, and the self-adaptive space-time filtering and leakage point positioning module adopts a particle filtering algorithm based on an attention mechanism. The above information is fused for iterative convergence, optimal leakage point position estimation is obtained, a leakage event comprehensive research and judgment module outputs a positioning result in combination with a real-time working condition and a historical model, a cloud diagnosis platform gathers data for optimization, and an intelligent closed loop is formed. According to the invention, high-sensitivity, high-reliability and high-precision positioning of pipeline micro-leakage is realized.

A digital-analog dual-driven oil and gas pipeline network fault diagnosis method and system for unbalanced data scenarios, involving the field of oil and gas pipeline network fault diagnosis, aims to solve the overfitting problem faced by the current intelligent fault diagnosis model when processing oil field unbalanced data sets, and reduce the risk of false positives and false negatives. The main steps are as follows: deeply understand the propagation and attenuation mechanism of negative pressure waves when oil and gas move in the pipeline network, and establish a negative pressure wave attenuation physical model that reflects the operation status of the pipeline network; build a deep generative adversarial model suitable for processing time series data based on long short-term memory networks; design a reasonable series-parallel mechanism fusion physical model and data-driven model to build a hybrid generative adversarial model; use the trained hybrid generative adversarial model to generate pipeline network fault data and balance the original training set; train the intelligent fault diagnosis model to realize pipeline fault type identification. The present invention simultaneously considers and integrates the prior knowledge from the physical model and the learning ability of the data-driven model. Compared with the pure data-driven model, the pipe network prior knowledge contained in the physical model can narrow the parameter space search domain, reduce the amount of estimated parameters, improve the interpretability and generalization performance of the deep generative model, and at the same time improve the physical rationality and feature distinguishability of the generated fault data, thereby effectively overcoming the negative impact of class imbalanced data sets on the performance of the diagnostic model, and further improving the accuracy of pipeline intelligent fault diagnosis.

The invention discloses an intelligent monitoring method for freezing pipe leakage in freezing method construction, and the method comprises the steps: collecting a negative pressure wave signal generated near a freezing pipe leakage point and the position and size information of the freezing pipe leakage point corresponding to the negative pressure wave signal through an indoor test; performing de-noising processing and feature extraction on the acquired negative pressure wave signal, and constructing and training an LSTM model; during on-site freezing method construction, pressure sensors are installed at the two ends of a freezing pipe; a negative pressure wave signal of the freezing pipe is monitored through a pressure sensor, denoising processing and feature extraction are conducted on the monitoring signal, the trained LSTM model is imported in real time, freezing pipe state information is output, and real-time monitoring of leakage of the freezing pipe is achieved; and according to a monitoring result, by integrating multi-factor analysis and consideration, proper construction treatment measures are taken for the fracture of the freezing pipe. The method can help a project to monitor the state of the freezing pipe in real time, quickly discover the position and size of a leakage point, improve the tunneling operation efficiency and guarantee the tunnel construction safety.

The invention provides a cleaning device for aviation monocrystalline silicon material part production, and relates to the technical field of monocrystalline silicon cleaning devices.The cleaning device comprises an ultrasonic cleaning machine and a protective cover, a driving motor is arranged at the bottom of the ultrasonic cleaning machine, and a main driving rod is fixedly installed at the end of an output shaft of the driving motor; the main driving rod is sleeved with a silicon wafer driving disc and a silicon wafer bearing disc in a sliding mode, the silicon wafer bearing disc is located above the silicon wafer driving disc, at least two flow guide spiral pipes are fixedly installed in the silicon wafer driving disc, the lower end of each flow guide spiral pipe protrudes out of the lower portion of the silicon wafer driving disc, and energy is transmitted through cleaning liquid. Liquid molecules are forced to vibrate to generate periodic positive and negative pressure waves, when the negative pressure waves exceed the tensile strength of liquid, cavitation bubbles are formed, the surfaces of parts are scoured through movement of a main body, remaining bubbles are guided through a flow guide groove in a flow guide spiral pipe, the cavitation bubbles flow directionally, scouring on the surfaces of the parts is enhanced, and the cleaning uniformity is improved.

The present disclosure integrates several methodologies for the detection and location of leaks in pipelines that transport hydrocarbons in liquid or gaseous phase. Detection is achieved through the management and processing of the data obtained by various measuring devices installed at strategic points on the pipeline infrastructure, this system does not alter neither structure nor mechanics of pipe and in case of a leak or flow caused by unauthorized actions, sensor system collect information on the physical variables of the fluid. This system may be made up of acoustic monitoring techniques, detection of negative pressure waves, as well as methods of mass and energy balance, integrated by multiple sensors, which are managed and controlled by electronic systems that process the information to determine the point of occurrence of a leak in digital maps. The system operates autonomously through the integration of solar cells for power supply and has satellite communication systems between the inspection sites and a monitoring center. It also can be remotely linked with SCADA systems (Supervisory Control and Data Acquisition) in real time. Additionally, it also has the technological integration of a motion sensor and a video and / or infrared camera together with a satellite modem whereby it sends images and video to the monitoring center, at the time of an unauthorized intrusion occurred on the pipeline in the area protected by a remote pipeline monitoring terminal (RPMT).

The invention relates to the technical field of microfluid precision control, and discloses a micro-droplet generation method based on a three-section type air pressure waveform, which comprises the following steps: a microcontroller sends a time sequence instruction to drive a pneumatic assembly to act, and stages of constant high-pressure pushing, rapid pressure relief and oscillation negative pressure pullback are sequentially executed in a fluid pipeline; in the pushing stage, the stable-pressure gas is used for driving liquid to extend to form a liquid column; in the pressure relief stage, the micro valve is reset to be matched with the front-end one-way pressure relief valve to quickly discharge residual high pressure; in the pull-back stage, a Helmholtz oscillation system formed by a main gas circuit pipeline and a valve port is used for inducing aerodynamic resonance, and reverse negative pressure waves are generated and directly act on the root of a liquid column to promote physical fracture of the liquid column. Through the synergistic effect of an active excitation negative pressure pull-back mechanism and a front-end rapid pressure relief strategy, trailing caused by fluid viscosity is effectively overcome, satellite liquid drops are eliminated, the problem of pressure lag is solved, and high-precision lossless generation of picoliter-to-nanoliter micro-droplets is achieved.

The utility model discloses a gas pipeline leakage negative pressure wave positioning detection device, and relates to the technical field of gas pipeline leakage negative pressure wave positioning detection. The gas pipeline comprises a gas pipeline body and a pressure sensor, branch pipes are symmetrically and fixedly arranged at the two ends of the gas pipeline body, a mounting plate is fixedly sleeved at the upper ends of the branch pipes, a hand valve is fixedly sleeved on the branch pipes below the mounting plate, and a sealing gasket is fixedly connected with the top of the mounting plate. According to the utility model, the pressure sensor is tightly pressed on the mounting plate, the mounting rack is abutted against the sealing gasket, and the blocking arm is clamped into the blocking groove arranged on the fixing bolt under the elastic force action of the metal elastic sheet, so that the pressure sensor can be quickly mounted and fixed at the upper end of the branch pipe, and the pressure sensor can be quickly fixed at the upper end of the branch pipe by shifting the blocking arm. And the blocking arm is moved out of the blocking groove, so that the pressure sensor can be quickly dismounted, and the pressure sensor can be more conveniently mounted and dismounted.