135 results about "Control limits" patented technology

The invention discloses a regulation and control quota distribution method and system fusing subjective and objective multi-dimensional features. The method comprises the following steps: acquiring electrical load data, historical response behavior data and subjective response intention information of a user; firstly, a Ward system is used for clustering, users are clustered according to active power, then a primary clustering center is used as an initial clustering center of secondary clustering, FCM clustering is carried out, and a typical load curve of the users is described based on a secondary clustering method; load prediction is carried out based on an NARX neural network, a predicted load curve is compared with a typical load curve, and adjustable potential is calculated; constructing a DR feature data set; and the DR feature data set is fused with an entropy weight method and an analytic hierarchy process to obtain a combined weight, a fuzzy relation matrix is constructed, a user comprehensive score is quantified, and a comprehensive response potential score of the user is formed.

The invention discloses a multi-working-condition process monitoring method and system based on zero-forgetting continuous dictionary learning, and the method comprises the steps: carrying out the offline modeling: firstly, carrying out the dictionary learning through employing an initial working condition data set, obtaining an initial dictionary, carrying out the decomposition, obtaining a low-rank matrix of an initial working condition, and calculating the control limit of the initial working condition through calculating a sample reconstruction error; performing incremental updating on the low-rank matrix obtained by learning the old working condition by using the new working condition data set to obtain a low-rank matrix of a new working condition, and calculating a control limit of the new working condition; on-line monitoring comprises the following steps: firstly, a weight selector is used for distributing weights for a low-rank matrix of a learned working condition according to monitoring data, and a monitoring dictionary is constructed in a self-adaptive manner; reconstructing the monitoring data by using the monitoring dictionary, solving a reconstruction error, and further judging the working condition of the monitoring data; and finally, calculating a fault detection statistic according to the reconstruction error and the control limit of the corresponding working condition, and judging whether the monitoring data is abnormal or not according to the statistic. According to the invention, accurate monitoring of a multi-working-condition process is realized.

The invention relates to the technical field of aviation safety and data processing, in particular to a flight data anomaly detection method based on DEWMA-IForest, and the method comprises the following steps: carrying out the preprocessing of original flight data; initializing a smoothing coefficient and a sliding window size parameter; calculating an arithmetic mean value and a standard deviation by taking data in the sliding window as a statistical sample, and respectively taking the arithmetic mean value and the standard deviation as a DEWMA statistic initial value and a control limit calculation reference value; setting an initial control limit range; calculating the DEWMA statistical magnitude and the standard deviation of the data to be measured; dynamically updating the control limit range; and DEWMA fitting reconstruction data and a control limit are calculated. According to the method, the dynamic control limit is adapted to the time-varying property of the QAR data, so that the sensitivity of small-amplitude trend anomaly is improved; high-dimensional data dimension disasters are avoided through multi-algorithm fusion; and the abnormal criterion can be explained, so that a scientific basis is provided for aircraft operation monitoring and intelligent maintenance decision making.

The invention provides a non-stationary industrial process monitoring method and system. The method comprises an offline training stage: calculating a time Laplacian matrix and a space Laplacian matrix based on a historical data matrix; constructing an objective function of the stationary subspace analysis method, and adding a time constraint term of a time Laplacian matrix and a space constraint term of a space Laplacian matrix into the objective function; solving the objective function to obtain a stable projection matrix; calculating a stationary component and a monitoring index of each sample in the data matrix X in sequence; determining a control limit by using a kernel density estimation method; an online monitoring stage: based on the real-time operation data x, calculating a stationary component of the real-time operation data x and a corresponding real-time monitoring index according to the stationary projection matrix, and if the real-time monitoring index is greater than a control limit, judging that the operation of the non-stationary process has a fault; the monitoring accuracy can be improved.

The invention discloses a chemical process anomaly type identification method based on a multi-dimensional causal association index, and belongs to the technical field of chemical process monitoring and fault diagnosis. The method comprises the following steps: firstly, carrying out offline modeling: acquiring normal working condition data, constructing a monitoring model by utilizing a trend slow feature analysis algorithm, and determining a statistic control limit; during online application, statistics of real-time data are calculated through a model, and an alarm is given and a type identification process is triggered if the statistics exceed a limit. A multi-dimensional causal association index (MCAI) is constructed by analyzing whether a quality variable deviates or not and further performing comprehensive analysis on a root variable causing an anomaly from four dimensions of a position (CPI) of the root variable in a process, an attribute (CAI) of the root variable in a causal network and an influence propagation capability (CPD) of the root variable. The problem that a traditional anomaly detection method cannot distinguish process faults from new working conditions is solved, false alarm can be effectively avoided, and a decision basis is provided for accurate maintenance.

The invention relates to the technical field of flow measurement, in particular to a gas flow process monitoring and abnormity diagnosis method based on EWMA. The method comprises the following steps: when a gas flow process is in a stable state, collecting sample data and calculating a mean value mu0 and a standard deviation sigma; setting a smoothing parameter lambda and a control limit parameter L of the EWMA control chart; acquiring traffic data in real time, and constructing an EWMA statistic Zi; calculating dynamic upper and lower control limits according to the mean value, the standard deviation, lambda and L; and judging whether the statistic Zi exceeds a control limit or not, and if so, judging that the statistic Zi is abnormal and giving out an early warning. According to the method, the EWMA control chart is used for carrying out weighted averaging on historical data, the detection sensitivity of tiny and slow trend drift is enhanced, the defects that a traditional Shewhart control chart and a fixed threshold value method are prone to missing report and false report in gas flow monitoring are overcome, early-stage and accurate early warning of gas flow abnormity is achieved, and the gas flow monitoring accuracy is improved. The intelligent level and reliability of the flow metering system are improved, and hardware cost does not need to be increased.

The invention provides a quality control system based on a multivariate exponential weighted moving average control chart, and belongs to the technical field of quality management and statistical process control. The system comprises a quality data acquisition module, a data preprocessing module, a statistical process control module, an anomaly judgment module and a quality evaluation and improvement module, and is used for monitoring and controlling the production process of a product with a plurality of quality characteristics in real time. The method comprises the following steps of: acquiring quality data of a plurality of quality characteristics in a production process by a system, preprocessing the quality data, and constructing a multivariate quality characteristic data set; estimating a mean vector and a covariance matrix in a controlled state based on the historical quality data, performing weighted updating on the multivariate quality data by adopting a multivariate exponential weighted moving average method, calculating a corresponding MEWMA statistic and generating an MEWMA control chart; and comparing the MEWMA statistical magnitude with a preset control limit to judge whether the production process is in an out-of-control state or not, and outputting early warning information and a corresponding quality evaluation result and improvement suggestion when abnormality is detected. The method can comprehensively analyze related information among multivariate quality characteristics, improves the sensitivity and accuracy of anomaly detection in the production process, and effectively improves the quality control level of the production process in the manufacturing industry.

The invention belongs to the field of fault diagnosis, and particularly relates to a fault detection method based on sparse representation, and the method comprises the steps: a preprocessing step: collecting normal working condition data samples under a preset working condition, and constructing a fault dictionary matrix; a control limit determination step of determining a sparse representation reconstruction error control limit and a distance control limit interval based on the fault dictionary matrix; in the field monitoring step, working field data are collected, and sparse representation reconstruction error statistics and distance statistics are calculated; and a fault detection step: judging whether the system has a fault by comparing the relationship between the statistical magnitude and the control limit. According to the method, accurate detection can be carried out under the condition that data does not obey normal distribution, the method has high detection sensitivity to tiny faults, meanwhile, the method is suitable for industrial process data of Gaussian and non-Gaussian distribution, and the accuracy and reliability of fault detection are effectively improved.

The invention discloses an industrial process fault monitoring method, which comprises the following steps of: acquiring real-time multivariable time sequence data of an industrial process, and performing standardized preprocessing; extracting the preprocessed data through a multi-scale feature extraction module to generate multi-scale potential feature representation; processing the multi-scale potential feature representation through a double-branch slow feature analysis module, and extracting a slow feature representation with a stable time sequence; based on the slow feature representation and the reconstruction data, constructing a T2 statistic and an SPE statistic; comparing with a preset control limit according to the T2 statistic and the SPE statistic; and judging whether the industrial process fails or not according to the comparison result. According to the method, the multi-scale features and the time sequence stable features of the industrial process data are effectively extracted, and the fault detection performance is remarkably improved.

The invention discloses a wind turbine generator running state monitoring method based on multi-source heterogeneous data, and the method comprises the steps: collecting SCADA multi-source heterogeneous data, removing abnormal samples, and carrying out the standardization; converting the collected non-normal data into approximate multivariate normal distribution based on a space rank; an elastic network penalty likelihood function is constructed, and mean vector estimation representing SCADA sparse fault signals is solved; calculating a basic monitoring statistic based on mean vector estimation and performing robust optimization to obtain a robust monitoring statistic and an upper control limit; real-time robust monitoring statistics are calculated, and if the real-time robust monitoring statistics exceed the upper control limit, a fault alarm is given out; and after a fault alarm is triggered, reversely deducing mean vector estimation according to the optimal penalty coefficient, and identifying and outputting fault component information. According to the method, the problems of complex SCADA data distribution, variable grouping correlation interference, difficulty in sparse fault identification, insufficient controlled samples and high multi-unit monitoring cost in the prior art are solved.

The application provides a closed loop process monitoring method based on improved dynamic latent variable analysis, and relates to the technical field of industrial process monitoring, and specifically comprises the following steps: collecting a section of sensor measurement data under normal working conditions of an industrial process as training data; calculating the neighborhood weight matrix of input data and output data respectively; establishing an improved dynamic latent variable analysis model to obtain the weight matrix and load matrix of input and output; constructing the feature matrix of input and output, calculating the covariance matrix and the control limit of the reduced rank Mahalanobis distance index; collecting test data, calculating the feature vector of input and output by using the projection direction matrix, calculating the reduced rank Mahalanobis distance index, and comparing with the control limit to judge whether a fault occurs or not. The technical scheme of the application overcomes the problem in the prior art that the detection method based on the open loop assumption design is difficult to effectively distinguish process faults, interference changes and normal adjustment behaviors of the controller, thereby causing the problems of missed report or false positive rate increase.

The embodiment of the invention discloses a quality data evaluation method for a multi-variety small-batch manufacturing process, and the method comprises the steps: 1, employing a tolerance coefficient method as a data conversion algorithm, and obtaining a tolerance utilization rate of a single monitoring characteristic; step 2, performing process capability evaluation on each monitoring characteristic, combining standardized data of multiple monitoring characteristics for single monitoring characteristics capable of accumulating the total sample number N and single monitoring characteristics incapable of accumulating the total sample number N so as to perform control process capability evaluation, and performing process control in an auxiliary single-value monitoring mode; and step 3, evaluating the control process capability in the step 2, and formulating a quality state evaluation criterion of multi-variety small-batch process control. According to the technical scheme provided by the embodiment of the invention, the problems of low accuracy of quality evaluation, difficulty in establishing an effective control model and control limit and the like of a quality control mode of multi-variety and small-batch products are solved.

The invention relates to the technical field of air conditioning equipment performance detection, and discloses an air conditioning cabinet air door cycle performance evaluation method and system in a high and low temperature environment, and the method comprises the steps: collecting initial performance reference parameters and normal temperature difference historical data of an air conditioning cabinet air door in a normal temperature standard environment, constructing an AR time sequence model, and calculating residual reference parameters; constructing a residual error EWMA control chart, and determining an EWMA statistic calculation formula and a control limit; executing an air conditioning cabinet air door cycle test under the high and low temperature test working condition, calculating an EWMA statistical magnitude, and recording abnormal parameters; matching an expert rule base according to the abnormal parameters; and calculating a performance attenuation rate according to the initial performance reference parameter and the air door performance parameter after the cycle test, setting a performance evaluation grade, and generating an air conditioning cabinet air door cycle performance evaluation report. By adapting to high and low temperature working conditions, dynamic interference is eliminated through an AR model and an EWMA control chart, air door micro-attenuation is captured, a fault source is determined by means of an expert rule, closed-loop evaluation is formed, and evaluation accuracy and operation and maintenance efficiency are improved.

The invention provides a system and computer program for identifying the depth of leaks in well casing strings after receiving temperature-depth data from a thermally conditioned well. Upon receiving the data, the system calculates moving average temperature gradients over a specified length of the casing. It then computes the mean and standard deviation of these gradients to determine a lower control limit (LCL) using a predefined formula. The system identifies leak depths by locating gradients below the LCL. The computer program generates control charts, visual alerts, and detailed reports to facilitate leak detection, offering a streamlined and effective approach to well integrity assessment.

The application discloses a wind turbine operation state monitoring method based on multi-source heterogeneous data, comprising: collecting SCADA multi-source heterogeneous data, and eliminating abnormal samples and standardizing; based on space rank, converting the collected non-normal data into approximate multivariate normal distribution; constructing an elastic net penalty likelihood function, and solving the mean vector estimation representing the SCADA sparse fault signal; based on the mean vector estimation, calculating the basic monitoring statistics and robust optimization, obtaining the robust monitoring statistics and upper control limit; calculating the real-time robust monitoring statistics, if exceeding the upper control limit, issuing a fault alarm; after triggering the fault alarm, according to the optimal penalty coefficient, backstepping the mean vector estimation, identifying and outputting the fault component information. The application solves the problems of complex SCADA data distribution, variable grouping correlation interference, sparse fault difficulty in identification, insufficient controlled samples and high multi-unit monitoring cost in the prior art.

The application discloses an air compressor quality related process monitoring method based on ASSA-KPLS, relates to the field of data-driven process monitoring, and comprises the following steps: offline modeling, an ASSA-KPLS model: obtaining sample data under a normal working state of an air compressor as training sample data, performing normalization processing on the training sample data, obtaining a stationary projection matrix through analytic stationary subspace analysis (ASSA) after the normalization processing, obtaining stationary sources from the stationary projection matrix and the normalized training set, then taking the stationary sources as input to establish a kernel partial least squares (KPLS) model, and constructing monitoring statistics and a control limit; online monitoring: collecting data under a fault state of the air compressor in real time as test data, obtaining stationary sources from the established ASSA model, obtaining monitoring statistics through the KPLS model, comparing the monitoring statistics with the control limit, and thus determining whether a quality related fault of the air compressor occurs. The non-stationarity and non-linear characteristics of the air compressor process data are considered simultaneously, and the efficiency of process monitoring and fault detection is effectively improved.

This invention discloses an adaptive monitoring and early warning system for ship main engines, comprising a multi-source heterogeneous data acquisition and cleaning module, a dynamic operating condition self-identification module, an adaptive condition monitoring model library, an operating condition matching and model scheduling unit, a real-time monitoring and anomaly triggering unit, a multi-dimensional contribution tracing and decision support module, and a model adaptive update engine. The system performs anomaly detection by calling PCA-based sub-monitoring models to calculate and statistically analyze data through data cleaning, online operating condition identification and matching scheduling. When an anomaly is triggered, the system locates the suspected root cause by calculating the contribution of variables and generates maintenance suggestions based on a fault case library. If no anomaly is triggered, the system uses health data to continuously update model parameters and control limits through a sliding window mechanism. This invention achieves dynamic adaptive tracking of main engine operating conditions, self-learning evolution of monitoring models, and a closed loop from anomaly detection to intelligent decision support, improving the accuracy and reliability of ship main engine condition monitoring.

The invention discloses a near-wall quadrotor safety control method based on model compensation, which belongs to the technical field of near-wall quadrotor safety control, improves a near-wall dynamic model in an MPC system by introducing suction compensation, and constructs an MPC into a factor graph comprising dynamic control, a reference trajectory, a control rate and a control limiting factor. The factor graph optimization FGO can solve and control input thrust and angular velocity under multiple constraints; the method specifically comprises three parts of dynamic modeling, suction compensation model prediction control and dynamic identification. A near-wall dynamic model in an MPC system is improved by introducing suction compensation, and the MPC is constructed into a factor graph FGO comprising dynamic control, a reference trajectory, a control rate and a control limiting factor; the SC-MPC is derived from force measurement data at different distances and rotating speeds, and the effectiveness of the SC-MPC is verified through a near-wall trajectory tracking experiment; compared with cascade proportion-integration-differentiation (PID) and manifold model predictive control (MPC), the tracking precision is remarkably improved.

This invention discloses a monitoring method and system for the preparation process of ternary cathode materials based on RVAE. It constructs a nonlinear dynamic system model of the sintering process based on a variational autoencoder; assigns different weights to samples at different times in the constructed nonlinear dynamic system model of the sintering process, derives the loss function of the nonlinear dynamic system model of the sintering process, and trains the model parameters through backpropagation; defines the statistics of the nonlinear dynamic system model of the sintering process based on the cyclic variational autoencoder, and obtains the control threshold of the nonlinear dynamic system model of the sintering process through kernel density estimation; collects online data as a test set for the nonlinear dynamic system model, calculates the monitoring statistics online and compares them with the control limits to determine whether a fault has occurred. This invention can significantly improve the fault detection rate and false alarm rate, providing a strong guarantee for the stable operation of the sintering process.

The invention belongs to the technical field of bridge structure key restraint device performance monitoring and evaluation, and discloses an unsupervised learning driven bridge support tensile function degradation grading diagnosis method. The method comprises the following steps: (1) constructing a relationship between support tensile stiffness degradation and bridge frequency; (2) unsupervised learning driven environmental effect separation; and (3) carrying out multi-stage diagnosis on degradation of the tensile function of the support. According to the method, the mapping relation between the tensile stiffness of the support and the frequency of the bridge in different states is obtained based on the Rayleigh method. Then, a manifold learning assisted regularization Gaussian mixture model is constructed, and environmental effect separation of frequency data is realized. And taking a negative logarithm form of a model output value as a diagnosis index. And setting a hierarchical control limit by adopting an EWMA control chart based on statistical characteristics of indexes when the support is normal and locally fractured. Finally, the example research of a cable-stayed bridge shows that the method can realize hierarchical diagnosis of the degradation of the tensile function of the support, and can provide guidance for a management and maintenance department to adopt different decisions for different degradation degrees of the tensile function of the support. The method has a high engineering application value in the technical field of performance monitoring and evaluation of the key constraint device based on the bridge structure.

The invention belongs to the technical field of wind power plant SCADA (supervisory control and data acquisition) data detection, and particularly relates to a wind power SCADA data online self-adaptive abnormal value detection method considering concept drift, which comprises the following steps: S100, collecting actual wind power in real time through an SCADA system; carrying out pretreatment and rationality screening; s200, calculating the wind power of the unit through a power model, obtaining a prior wind power sequence, and constructing a residual sequence; introducing an input wind speed as a scaling factor to obtain a scaling residual sequence; processing the scaling residual error sequence, and marking an abnormal value according to a preset threshold value; s300, dynamically monitoring the scaling residual error sequence by adopting an exponentially weighted moving average method; triggering a concept drift candidate event when the EWMA value exceeds a control limit UCL; carrying out difference test on the current residual error distribution and the historical reference distribution by adopting KS test, and if the difference exceeds a preset threshold value, confirming that concept drift occurs; and S400, after judging that the concept drift occurs, updating the parameters of the power model.

The application discloses a kind of early fault detection methods for dynamic industrial process, belong to industrial process monitoring and fault diagnosis field, this method includes: two groups of independent measurement data under normal working condition are collected as training data, and autoregressive model with external input is constructed;Solving optimization problem obtains the time delay of industrial process, autoregressive coefficient and time-independent component;For each sample of time-independent component, the optimal principal component of each sample is selected by maximizing detection performance index;According to optimal principal component, the statistics of training data is constructed, and its control limit is determined;Collect industrial process real-time data as test sample, and the time-independent component of test sample is calculated using autoregressive coefficient;The optimal principal component of test sample is calculated by maximizing detection performance index;Calculate statistics and optimize, compare with control limit to realize fault detection.The application does not need the accurate mathematical model of process and fault data, and can realize the efficient detection of early fault.

The application provides a charging pile group error characteristic control method and device based on statistical testing, equipment and a medium, comprising: S101, calculating a virtual verification standard value in a charging pile group measurement guarantee scheme; S102, calculating process parameters of the virtual verification standard in the charging pile group measurement guarantee scheme based on the virtual verification standard value; S103, controlling the error characteristics of the charging pile group according to the process parameters of the virtual verification standard; in the control process, if it is found that the error of all piles in the charging pile group exceeds the control limit, the process parameters of the virtual verification standard in the charging pile group measurement guarantee scheme are modified. The advantage lies in: the error characteristics of the charging pile group are monitored, the errors of each pile in the charging pile group are monitored, the effectiveness of the virtual verification standard is ensured, and the efficiency of the charging pile determination is greatly improved.

The application discloses a strip steel hot rolling process multi-performance index abnormality monitoring method and system, and belongs to the technical field of industrial process control and fault diagnosis, and the method comprises the following steps: collecting process variable data and performance index data in a historical strip steel hot rolling process and performing standardization pretreatment; dividing the strip steel hot rolling process into multiple different process subblocks; for each process subblock, performing space-time feature extraction on the corresponding process variable data to obtain a space-time feature representation; constructing a local statistic quantity of each performance index; fusing the local statistic quantities to obtain a global statistic quantity and a global control limit; and based on this, realizing multi-performance index abnormality monitoring and alarm. The application effectively solves the problems that cross-process time lag dependence is difficult to capture, space-time feature extraction is not comprehensive, and single performance index monitoring leads to abnormality missed reports in the strip steel hot rolling process, significantly improves the fault detection rate and reduces the false alarm rate.

The invention discloses an industrial system fault monitoring method based on physical information enhanced probability principal component analysis, which comprises the following steps: performing fault monitoring on an industrial system by utilizing a physical information enhanced probability principal component analysis model to obtain T2 statistic and SPE statistic of a monitoring sample, comparing the control limit with the corresponding T2 statistic control limit and the corresponding SPE statistic control limit to obtain a monitoring result; the physical information-based enhanced probability principal component analysis model is composed of a probability principal component analysis model and a linear physical constraint equation substituted into the probability principal component analysis model. Compared with traditional probabilistic principal component analysis (PPCA) and factor analysis (FA), the physical information-based enhanced probabilistic principal component analysis (PI-PPCA) model adopted by the method has a lower omission ratio.

The invention relates to the technical field of automatic control, in particular to an automatic leveling control method for an aerial work platform, which comprises the following steps of: acquiring angle data through a tilt angle sensor, performing moving average filtering and noise reduction, calculating an angle change rate, constructing a gradient threshold value through a sliding window, outputting leveling parameters in a grading manner, and calculating a real-time angle by adopting a PID (Proportion Integration Differentiation) control algorithm. And dynamically adjusting proportional integral, performing fuzzy control on displacement of the amplitude-limiting landing leg, judging a main arm and an azimuth angle, outputting a safety instruction, matching a pressure curve with an opening curve, and triggering attenuation at a low matching degree until locking. According to the method, a dynamic gradient threshold value is constructed through sliding window statistics, leveling parameters and the inclination angle rate are matched, PID control cooperation coefficient self-matching adjustment is started and stopped, response and precision are balanced, main arm and rotation angle multi-dimensional constraint is achieved, a safety boundary is set through fuzzy amplitude limiting, leveling and stability are optimized, hydraulic frequency motion is reduced, and load and disturbance robustness is enhanced; and energy consumption and impact are reduced.

The application discloses a process quality detection method, system, equipment and medium based on wavelet packet decomposition and T2 control chart, which comprises the following steps: collecting original signals of multiple production characteristics of a chemical process in real time to obtain stable section signals; performing wavelet packet decomposition on the preprocessed stable section signals to obtain multiple sub-band signals; calculating multivariate statistical values of the sub-band signal data one by one and drawing control charts; calculating control limits of T2 statistics of the sub-band signals under normal working conditions; comparing the multivariate statistical values of the real-time sub-band signals with the control limits to determine whether a fault occurs and output a fault point; performing periodic adaptive updating, collecting the chemical process signals confirmed as normal working conditions in a period as a new training sample set at intervals of a preset time period, and repeatedly calculating the control limits of the new training sample set. The application improves the sensitivity and reliability of abnormal detection and the real-time monitoring capability of an industrial site.

The invention relates to a thermal power generating unit fault monitoring method based on a domain adversarial auto-encoder model, and the method comprises the following steps: S1, collecting and storing the normal data of a historical mode and a small sample mode in the operation process of a thermal power generating unit, and carrying out the preprocessing of the data; s2, constructing a domain adversarial auto-encoder model and training the model by using the preprocessed historical data to obtain an SPE statistical magnitude control limit of the model under a preset confidence interval; S3, obtaining real-time operation data of a thermal power generating unit on line and preprocessing the operation data; and S4, taking the preprocessed real-time data as input data of the domain adversarial auto-encoder model, obtaining SPE statistics in real time, if the SPE statistics are greater than the SPE statistics, taking the data as fault data, and otherwise, taking the data as normal data. Compared with the prior art, the method has the advantages of high fault monitoring precision, high adaptability and the like.

The invention provides a working condition monitoring method for a petrochemical industrial process, a working condition monitoring system for the petrochemical industrial process and a computer readable storage medium. The working condition monitoring method for the petrochemical industrial process comprises the following steps: acquiring monitoring data and extracting time sequence data; the time series data are input to a monitoring model, the monitoring model comprises a gating circulation unit network and a Gaussian mixture variational auto-encoder with a Copula function introduced, the gating circulation unit network is used for extracting potential representation of the time series data and determining distribution parameters of potential variables, and the Copula function is used for inputting the potential representation of the time series data into the Gaussian mixture variational auto-encoder; a Gaussian mixture variational auto-encoder introducing a Copula function realizes potential variable modeling according to distribution parameters of potential variables; based on the monitoring model, using a kernel density estimation method to calculate monitoring statistics and setting a control limit of the monitoring statistics; and performing anomaly detection according to the monitoring statistics and the control limit of the monitoring statistics.

This invention provides a fault detection method and system based on low-rank decomposition and common trend decoupling. The method first acquires historical process data under normal operating conditions of an industrial process as training data. Then, it decomposes the training data into clean data representing the main structural information and sparse noise data representing noise interference using low-rank decomposition. Next, it constructs a common trend learning model based on the clean data, reconstructs the non-stationary common trend, and uses the residual between the clean data and the non-stationary common trend as a stationary feature. Decoupling is achieved by applying stationarity constraints. Further, a fault detection model is obtained through joint optimization. In the detection stage, the process data to be detected is input into the model, and monitoring statistics based on the reconstruction error of the stationary feature and the common trend are constructed respectively. The result of the control limit comparison determines whether a fault has occurred, thereby improving the robustness of fault detection in non-stationary industrial processes.