28 results about "Electrochemical noise" patented technology

The invention relates to the technical field of equipment fault prediction, in particular to a coal mine equipment fault prediction system and method based on big data, and the method comprises the following steps: collecting the thickness of a biological membrane on the surface of equipment, synchronously collecting the vibration acceleration of the equipment and the environmental hydrogen sulfide concentration, and constructing a corrosion multi-mode data set; performing electrochemical noise separation on the corrosion multi-modal data set, and constructing a corrosion activity fingerprint spectrum; decoupling a coupling effect of mechanical vibration and microbial corrosion, and generating a corrosion-fatigue damage correlation map; and constructing a dual-channel life prediction model based on the corrosion-fatigue damage correlation map, and setting an early warning mechanism. Microbial corrosion characteristic current is accurately extracted through wavelet packet decomposition and wavelet entropy analysis, a corrosion-vibration heterogeneous graph is established by adopting a graph attention network, corrosion-induced structural vibration enhancement effect analysis is realized, and compared with a traditional prediction method based on a single signal, the method has the advantages that the method is simple and convenient to operate. And the problems of limited local corrosion influence range, unstable vibration response and the like are effectively solved.

The embodiment of the invention provides a method, a system and equipment for predicting the corrosion rate of a water wall tube of a boiler and a storage medium. The method comprises the following steps: acquiring corrosion related parameters of the water wall tube, including electrochemical noise, surface strain, medium components and temperature and humidity; performing space-time alignment and fusion processing on the corrosion related parameters to obtain a corrosion state feature matrix; inputting the corrosion state characteristic matrix into a pre-established physical characteristic extraction model, and dynamically predicting the corrosion rate of the water wall tube by adopting a space-time dual-channel network; wherein the physical feature extraction model is pre-established through a physical information neural network, and the space-time dual-channel network comprises a long short-term memory network, a graph convolutional network and a sliding window. According to the embodiment of the invention, various factors influencing corrosion are comprehensively considered, the physical information neural network is introduced to carry out deep autonomous learning, finally, a reliable corrosion rate prediction condition is provided, and dynamic, lasting, intelligent and flexible corrosion state monitoring and prediction of the boiler water wall tube are realized.

The invention relates to the technical field of material analysis, in particular to a multi-mode collaborative corrosion state monitoring method and system. Through a self-supervised contrast learning method of acoustic emission and electrochemical noise dual-mode signals and in combination with an innovative network architecture of Transformer and a multi-layer perceptron, feature alignment of acoustic emission and electrochemical noise signals is realized, the problem of dependence on a large amount of labeled data in corrosion monitoring of chlor-alkali production equipment is overcome, and a corrosion state on-line monitoring model is formed. Under a self-supervised contrast learning framework, the model can utilize unlabeled acoustic emission and electrochemical noise data to carry out autonomous learning, and the demand for expensive and scarce labeled resources is reduced. Only a small amount of manual annotation acoustic emission data is needed for fine tuning training, and online monitoring of the corrosion state can be achieved. Meanwhile, the on-site rapid and non-destructive corrosion state monitoring analysis of the chlor-alkali production equipment can be completed only through acoustic emission data, and the corrosion state monitoring technology is promoted to develop towards the more intelligent and automatic direction.

The invention belongs to the technical field of material performance monitoring, and particularly relates to a sensor and a method for monitoring the corrosion behavior of metal in chloride molten salt, and the sensor and the method are used for monitoring the corrosion characteristics of the metal in a chloride molten salt environment for solar photo-thermal power generation. The corrosion rate and corrosion behavior are monitored in multiple dimensions, data are mutually verified, the reliability of the result is ensured, the temperature control module is cooled through real-time air, it is ensured that the temperature of the probe head is consistent with the actual working condition temperature, and the monitoring accuracy is further improved. In addition, the electrochemical noise signal fluctuation characteristics can help to preliminarily distinguish local corrosion and uniform corrosion behaviors, and deep analysis of the corrosion behaviors is realized. The method can also be widely used for corrosion monitoring and early warning of a photo-thermal power station using various heat storage media except chloride molten salt, and solving the problem of molten salt corrosion has indispensable strategic significance for guaranteeing the equipment stability, reducing the operation cost and improving the international competitiveness.

The present application relates to the technical field of material analysis, in particular to a multi-modal cooperative corrosion state monitoring method and system. Through a self-supervised contrast learning method of acoustic emission and electrochemical noise dual-mode signals, and combined with the innovative network architecture of Transformer and multilayer perception, the acoustic emission and electrochemical noise signal features are aligned, the problem of relying on a large amount of labeled data in the corrosion monitoring of chlor-alkali production equipment is overcome, and an online corrosion state monitoring model is formed. Under the self-supervised contrast learning framework, the model can use unlabeled acoustic emission and electrochemical noise data pairs for self-learning, reducing the demand for expensive and scarce labeled resources. Only a small amount of artificially labeled acoustic emission data is needed for fine-tuning training, and online corrosion state monitoring can be realized. At the same time, the present application can complete the on-site rapid and non-destructive corrosion state monitoring analysis of chlor-alkali production equipment only with acoustic emission data, promoting the development of corrosion state monitoring technology in the direction of more intelligent and automatic.

The present application relates to the field of galvanized steel corrosion rate monitoring and protection, and provides a galvanized steel corrosion rate calculation method and system based on electrochemical noise data. The method comprises: obtaining a potential signal distinguishing the galvanized steel, using wavelet decomposition and segmentation processing, dividing high-frequency noise signals, medium-frequency noise signals and low-frequency noise signals according to the high and low of different resolution frequencies, and determining the total energy of the high-frequency noise, the total energy of the medium-frequency noise and the total energy of the low-frequency noise; calculating the absolute value of the energy difference between the total energy of the medium-frequency noise and the total energy of the low-frequency noise, if the absolute value of the energy difference is less than a first threshold value, the corrosion rate of the galvanized layer is a determined value, if the absolute value of the energy difference is greater than a second threshold value, the corrosion rate of the galvanized layer is: a=k*|E m -E l |+b; wherein a represents the corrosion rate of the galvanized layer, E m represents the total energy of the medium-frequency noise, E l represents the total energy of the low-frequency noise, and k and b are real numbers.

The invention discloses a heat treatment temperature field associated 304 austenitic stainless steel corrosion resistance monitoring system, which relates to the technical field of metal material heat treatment monitoring, and comprises a temperature field monitoring network, an electrochemical noise monitoring module and a data fusion and intelligent analysis center, the temperature field monitoring network monitors temperature field distribution of a heat treatment workpiece in real time and generates temperature field data, the electrochemical noise monitoring module collects electrochemical noise signals of the workpiece in real time and generates electrochemical noise data, and the data fusion and intelligent analysis center performs time synchronization and space registration to form synchronous data after receiving the two types of data. Temperature field features and electrochemical noise features are extracted, the corrosion risk probability is calculated through an intelligent judgment model, a corrosion risk early warning signal is generated when the probability exceeds a preset threshold value, the system establishes real-time correlation between the temperature field and corrosion resistance, and early recognition and accurate positioning of the corrosion risk are achieved.

The application discloses a stainless steel weld corrosion life prediction method and system based on multi-parameter coupling, and the method comprises the following steps: collecting corrosion potential sequence, electrochemical impedance spectrum data and electrochemical noise data of a weld; extracting charge transfer resistance and double-layer capacitance from the impedance spectrum data, extracting noise resistance and localization factor from the noise data, extracting corrosion potential drift rate from the corrosion potential sequence, and combining into a multi-dimensional state observation vector; constructing a state space model describing the evolution of the corrosion state of the weld, using a nonlinear Wiener process to describe corrosion damage accumulation, and establishing a mapping relationship between the observation vector and the corrosion state variable; using a particle filtering algorithm to recursively update the posterior probability distribution of the corrosion state; extrapolating and predicting the time when the corrosion state reaches a preset failure threshold according to the posterior distribution, and obtaining a residual service life prediction value and a confidence interval. The multi-dimensional electrochemical parameters are fused, and the accurate prediction and uncertainty quantification of the weld corrosion life are realized.

This invention belongs to the field of primary lithium battery technology, specifically relating to a surface modification method for the lithium alloy anode of a primary lithium battery. It employs trifluoromethane-silver trioxide modification of the Li-B alloy to form a surface layer containing metallic silver, LiF, and sulfides. This method constructs a surface layer with high stability and high lithium-ion conductivity on the lithium alloy surface, effectively protecting the metallic lithium, reducing surface impedance, and eliminating electrochemical noise, thereby extending the storage life of the primary lithium battery and improving its electrochemical performance. The method is simple, practical, and efficient, with strong application prospects.

The invention belongs to the field of electrochemical testing, and particularly relates to an electrochemical testing device suitable for a single-double working electrode system, which comprises a tank body, a working plate, a pressing plate, a working electrode communicating hole, a groove and a pressing bolt, an electrolyte cavity is arranged in the cell body, two sides of the cell body are respectively connected with a working plate, the working plates are connected with a pressing plate through bolts and nuts, the center of each working plate is provided with a working electrode communicating hole, and the center of each pressing plate is provided with a pressing bolt; the electrolyte cavity can contain various types of electrolyte solutions, the working electrode is pressed and fixed through the pressing plate, and the distance between the working plate and the pressing plate can be adjusted through the four sets of adjusting bolts and the adjusting nuts so as to be suitable for the working electrodes of various shapes and sizes. The device is simple and stable in structure, good in sealing performance, simple and convenient in electrode clamping, and suitable for various electrochemical tests such as alternating current impedance spectroscopy, linear polarization scanning and electrochemical noise of metal, film and coating systems.

The application discloses an electrochemical noise corrosion state identification and early warning method and system, and belongs to the technical field of corrosion monitoring and early warning. The method collects the potential noise signal and the current noise signal of the target component through an electrochemical workstation and calculates the noise resistance, adopts db4 wavelet for multi-layer wavelet decomposition and threshold denoising processing, extracts the kurtosis feature, the skewness feature and the wavelet coefficient feature, and constructs a corrosion state identification and early warning data set after feature screening; a fusion model including an LSTM time sequence feature extraction layer and an Attention attention weight distribution layer is constructed for training, and a corrosion state identification model is obtained; the real-time collected and processed feature parameters are input into the model for identification, the proportion of a specific corrosion state in a preset time period is counted, and a blue, orange or red graded early warning is triggered. The application realizes high-precision identification and accurate graded early warning of the corrosion state, the identification accuracy is above 85%, and reliable technical support is provided for industrial component corrosion protection.

The invention relates to a subway tunnel reinforced concrete structure chloride ion concentration detection system, aims to solve the destructive problem of a traditional detection method and realize lossless and real-time monitoring, and comprises an electrochemical noise signal data acquisition unit, a data processing unit and a chloride ion concentration prediction unit, according to the method, electrochemical noise data of a reinforced concrete simulation test block is collected, a segmented polynomial fitting method is used for removing direct current drift, key electrochemical parameters are extracted, a prediction model is constructed in combination with an XGBoost algorithm, the chloride ion concentration is accurately predicted on the basis of electrochemical noise signal characteristics, and early warning of the corrosion risk of a subway tunnel structure is achieved. According to the system, electrochemical noise analysis, wavelet transform and machine learning technologies are creatively integrated, and the prediction accuracy and efficiency are improved.

The disclosed technology is an electrochemical noise-based (ECN) corrosion monitoring tool (i.e., sensing device and data interpretation algorithms) for corrosion under insulation (CUI) applications. An electrochemical noise-based device for monitoring corrosion under insulation including a sensing device and a data interpretation algorithm.

The invention provides a real-time monitoring method and device for amorphous characteristics of an airborne system under a dynamic load, and belongs to the technical field of structural health monitoring. The device is used for monitoring crystallization transformation caused by flight load of an amorphous alloy test piece through a controllable stress-corrosion simulation electrolysis system after off-site sampling of the amorphous alloy test piece subjected to dynamic load in an airborne environment. The device adopts a single-cavity structure; a sample serves as one side wall of a cavity to form a wall type working electrode, and the back surface is sealed and supported by a solid back plate, a flange and an O-shaped ring; a reference electrode, a counter electrode, a thermocouple and a magnetic stirrer are arranged in the cavity. By adjusting temperature, solution components and external static stress, an airborne environment can be simulated, and high-precision measurement of corrosion current, interface resistance and electrochemical noise of a test piece is realized, so that the stability of an amorphous structure and the degradation degree of corrosion resistance of the amorphous structure after dynamic loading of an airborne system are judged. According to the scheme, operation is easy and convenient, the structure is compact, repeatability is good, and rapid off-position evaluation and airborne component quality tracing are facilitated.

The invention relates to the technical field of seawater cooling analysis, in particular to a seawater cooling comprehensive analysis system based on multi-sensor monitoring, which comprises a multi-source sensing array module for synchronously outputting a pipe wall temperature gradient, an axial vibration spectrum, an electrochemical noise signal and a real-time water quality parameter; the dirt-corrosion coupling module is used for calculating a dynamic dirt thermal resistance value based on the energy attenuation characteristics of the pipe wall temperature gradient and the axial vibration spectrum, analyzing the current peak density of the electrochemical noise signal, and generating a corrosion heat transfer influence factor in combination with the dynamic dirt thermal resistance value; the self-adaptive calibration engine module is used for compensating and calibrating the electrochemical noise signal according to the real-time water quality parameter and generating a multi-domain early warning threshold value; and the intelligent decision module is used for comparing the corrosion heat transfer influence factor with a multi-domain early warning threshold value and outputting an energy efficiency optimization instruction set. Quantitative indexes of corrosion heat transfer influence factors are formed, and the perception sensitivity and engineering interpretability of the corrosion-heat exchange degradation process are improved.

The present disclosure provides a method, apparatus, device, and storage medium for monitoring stress corrosion cracking, belonging to the technical field of metal material failure monitoring. The method includes: acquiring an electrochemical noise signal and an acoustic emission signal of a tubing steel specimen in the downhole environment of an oil displacement well; monitoring pitting events and microcrack initiation events in the first stage of stress corrosion cracking of the tubing steel specimen according to the electrochemical noise signal; and monitoring crack propagation events and crack tearing events in the second stage of stress corrosion cracking of the tubing steel specimen according to the acoustic emission signal. It is possible to monitor corrosion cracking events in the whole process of SCC of the tubing steel specimen, thereby improving the accuracy of SCC monitoring.

The invention discloses a method and system for realizing electrochemical noise signal pattern recognition, and the method comprises the steps: converting a target convolutional neural network model into an electrochemical noise pattern recognition model according to a time sequence of a historical electrochemical noise signal and a corresponding pattern, the target convolutional neural network model is obtained by adding a preprocessing module in front of a first convolutional layer in the basic neural network model, and the preprocessing module is composed of a plurality of maximum pooling layers; in the actual prediction process, according to electrochemical noise signals collected in real time, the electrochemical noise recognition model is used for obtaining the mode recognition result of the noise signals.

The invention discloses a corrosion detection method and device for non-corrosion sample training based on an electrochemical noise signal. The method comprises the following steps: firstly, collecting the electrochemical noise signal; secondly, constructing a data set; constructing a corrosion detection algorithm model again and training; the corrosion detection algorithm model comprises a feature extractor, a self-supervised contrast learning module, a reconstruction loss module, a deep support data description module and an outlier exposure supervision module; a space-time attention dynamic learning network is arranged in the feature extractor based on a Transform architecture; performing first-stage training on the feature extractor through a self-supervised contrast learning module and a reconstruction loss module, and performing second-stage training on the feature extractor through a deep support data description module and an outlier exposure supervision module; a matrix form soft Brownian migration algorithm is arranged in the outlier exposure supervision module and is used for generating an outlier sample for the outlier exposure supervision module; using the training set to train the corrosion detection algorithm model; and finally evaluating the corrosion detection capability.

The invention provides a metal corrosion measuring device, which comprises a first electrode module, a second electrode module and a processing module, and is characterized in that the processing module is used for processing a first electric signal of a first pair of comb-shaped interdigital electrodes and a second electric signal of a second pair of comb-shaped interdigital electrodes; to determine an electrochemical noise measurement and / or an electrochemical impedance spectroscopy measurement of metal corrosion; the processing module is also used for processing the third electric signal of the inert counter electrode, the fourth electric signal of the reference electrode and the fifth electric signal of the surface of the object to be measured so as to determine the electrochemical impedance spectroscopy measurement result of metal corrosion. The measuring device is simple in structure and easy to miniaturize and integrate, and the accuracy of a measuring result is improved. The measuring device can also adapt to different test scenes, and the accuracy and flexibility of the measuring device are further improved.

The invention discloses a corrosion monitoring and early warning method, which comprises a corrosion probe device and a signal acquisition processor, integrates two technologies of electrochemical noise monitoring and acoustic emission monitoring at the same time, and integrates probe monitoring and body monitoring at the same time. Mutual verification of electrochemical noise and acoustic emission characteristic signals is adopted, probe monitoring and equipment monitoring are carried out at the same time, the probe and equipment monitoring are combined to jointly judge the corrosion behavior generated by the monitoring equipment / pipeline, and the accuracy of monitoring and early warning is improved. In addition, stress, a gradual change gap, a welding joint and dissimilar metal are flexibly introduced into the monitoring probe aiming at various possible corrosion, various corrosion types such as stress corrosion, pitting corrosion, crevice corrosion and galvanic corrosion can be considered at the same time, and preliminary judgment of the corrosion types is given while early warning of the corrosion state is carried out.

The invention discloses an electrochemical noise experiment method based on a field experiment environment. The method comprises the following steps: 1, experiment preparation: S1, preparing a field sensor and a tool; s2, installing acquisition equipment; and S3, a component experiment data acquisition system. The problems that an existing electrochemical noise experiment method is single, and detection cannot be carried out in different environments are solved, noise detection is carried out on an electrode by utilizing solutions with different concentrations, detection data are transmitted to an upper computer through a voltage sensor, experiment data are recorded by utilizing electrochemical noise system software in a computer, and the detection accuracy is improved. The cRIO acquisition module is used for acquiring data, different noise data are recorded and compared through the upper computer, and meanwhile, the electrochemical noise data can be detected in a field experiment environment by matching with different duration of the electrode in the solution and recording electrode corrosion degree data, so that the data accuracy is effectively ensured.

This invention belongs to the field of big data analysis and evaluation, specifically relating to a big data analysis-based energy storage safety level evaluation system. The system includes: a high-precision data acquisition device for acquiring weak physical signals such as electrochemical noise; an electrochemical noise analysis device that uses a time-frequency domain joint analysis algorithm to extract feature vectors characterizing internal physical evolution; a dynamic baseline drift compensation device that constructs a health trend space and generates an adaptive evaluation baseline boundary, calculating real-time deviation indicators; a big data analysis and evaluation device that integrates feature vectors and deviation, using a deep learning model to output a quantitative value of the safety level; and a safety early warning execution device that executes control strategies and outputs fault location information based on physical mechanisms. This invention improves the accuracy of hazard identification by capturing microscopic fluctuation signals and introducing a compensation mechanism, achieving interpretable proactive operation and maintenance.

The present invention relates to the technical field of equipment fault prediction, and particularly to a coal mine equipment fault prediction system and method based on big data, which includes the following steps: collecting the thickness of the biofilm on the surface of the equipment, synchronously collecting the vibration acceleration of the equipment and the environmental hydrogen sulfide concentration, and constructing a corrosion multi-modal data set; performing electrochemical noise separation on the corrosion multi-modal data set to construct a corrosion activity fingerprint spectrum; decoupling the coupling effect of mechanical vibration and microbial corrosion to generate a corrosion-fatigue damage correlation map; constructing a dual-channel life prediction model based on the corrosion-fatigue damage correlation map and setting up an early warning mechanism. The present invention accurately extracts the characteristic current of microbial corrosion through wavelet packet decomposition and wavelet entropy analysis, and uses a graph attention network to establish a corrosion-vibration heterogeneous graph to realize the analysis of the corrosion-induced structural vibration enhancement effect. Compared with traditional single-signal-based prediction methods, it effectively overcomes problems such as the limited influence range of local corrosion and unstable vibration response.

The utility model relates to an electrochemical noise signal experiment system for a steel-concrete structure corroded by stray current, and belongs to the technical field of electrochemical noise detection. Comprising a test box body and an electrochemical workstation, a sodium chloride solution is arranged in the test box body, a reinforced concrete test block for simulation detection is soaked in the sodium chloride solution, and a filling liquid for simulating erosion ion conditions is filled between the test box body and the reinforced concrete test block; the electrochemical workstation is provided with a reference electrode interface, an auxiliary electrode interface and a working electrode interface, the reference electrode interface is connected with a saturated calomel electrode of which the end part is arranged in a sodium chloride solution through a wire, the auxiliary electrode interface is connected with an auxiliary electrode, and the working electrode interface is connected with a working electrode. The structural member is convenient to use, and provides effective help for researching the stray current corrosion mechanism and evaluating the structural durability.

This invention belongs to the field of proton exchange membrane fuel cell condition monitoring and fault diagnosis technology. It discloses a broadband impedance measurement excitation signal design method based on electrochemical noise optimization, comprising: selecting a discrete interval binary sequence (DIBS) as the excitation signal; predicting the power spectrum of the measured battery voltage noise using an AR model; establishing an electrochemical noise equivalent circuit model of the battery based on the battery equivalent circuit model parameters and the AR model; synthesizing the DIBS based on the current harmonic number, signal-to-noise ratio, and estimated voltage noise power spectrum; and searching for the optimal combination of signal-to-noise ratio and harmonic number based on the synthesized DIBS and combined with the simulation of the battery's electrochemical noise equivalent circuit model to obtain the desired DIBS, which is then used as the impedance measurement excitation signal. This invention can tailor the impedance test excitation signal according to the actual condition of the battery stack, improving measurement accuracy while ensuring measurement speed and reducing interference to the battery system.

The present application relates to the field of cavitation corrosion electrochemical in-situ testing, and particularly relates to a device and method for monitoring metal cavitation corrosion process by using electrochemical noise, which mainly solves the problems that the incubation period of cavitation corrosion cannot be accurately measured and real-time change information in the development process of cavitation corrosion cannot be obtained in the prior art. The present application mainly comprises a three-electrode part, an ultrasonic cavitation part, an electrochemical noise monitoring part and a data analysis part. The three-electrode part comprises two same surface working electrodes and a reference electrode; the ultrasonic cavitation part mainly comprises a variable amplitude rod, a clamp, a double-layer electrolytic cell and a thermostat; the electrochemical noise monitoring part mainly comprises a fastening device, a flame-retardant soundproof foam, a Faraday cage and an electrochemical workstation; and the data analysis part mainly analyzes the electrochemical noise data in the cavitation corrosion process by means of a computer. The present application can realize continuous in-situ monitoring of metal materials under cavitation corrosion conditions, and can also identify the surface damage evolution process in different cavitation corrosion time according to the change of noise data.

This specification discloses an electrochemical noise testing system and method. By providing a system including a first digital multimeter, a second digital multimeter, a weak current amplifier, a reference electrode, a working electrode, and a counter electrode, the working electrode and the counter electrode are connected to the two current input measurement terminals of the weak current amplifier; the two voltage output terminals of the weak current amplifier are connected to the two voltage input measurement terminals of the first digital multimeter; the working electrode and the reference electrode are connected to the two voltage input measurement terminals of the second digital multimeter, for synchronous potential noise measurement with the current noise measurement, thereby achieving a combination of corrosion detection and monitoring, performing full-process, multi-scale electrochemical measurement of localized corrosion processes, and achieving accurate spatial positioning of corrosion sites.

The application provides a phosphorus-ammonium-containing industrial wastewater treatment data analysis method and system, which comprises the following steps: an electrochemical detection loop is arranged in a crystallization reactor, and a potential and current noise signal is collected. The ratio of the standard deviation of the potential and current noise is used to determine the crystallization starting time. In response to the crystallization starting, the physical morphology of the particles is evaluated by calculating the kurtosis value of the current noise, and the type of the particles is determined by combining the kurtosis value and the electrochemical noise change caused by the periodic change of the flow rate. The operation parameters of the reactor are adjusted based on the diagnosis result. Finally, when the kurtosis value is stable at a high level and the electrochemical noise index is stable, it is determined that the crystal growth is completed. The application solves the technical problem that the physical morphology and type of the growing particles in the reactor cannot be distinguished online by the traditional macroscopic parameter monitoring by analyzing the high-order statistical characteristics of the electrochemical noise and the response of the high-order statistical characteristics to the flow field change.