87 results about "Control limits" patented technology

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 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 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.

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.

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 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.

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.

This invention discloses a method and system for monitoring medical data anomalies using multivariate adaptive weights, relating to the fields of medical artificial intelligence and data analysis. The method includes: acquiring a standard medical data matrix of historical medical data samples and new observation samples, the standard medical data matrix including a first feature; updating the initial weights of the first feature based on the residuals of the first feature to obtain a first weight; and obtaining T based on the first weights and the new observation samples. 2 Statistics; the Q-statistic is obtained based on the standard medical data matrix and the new observation sample; the contribution value of the Q-statistic is obtained based on the residual vector of the Q-statistic and the first weight; the T-statistic is obtained based on the historical medical data sample. 2 Dynamic control threshold and Q dynamic control threshold; if T 2 The statistic is greater than T 2 If the dynamic control threshold or Q statistic is greater than the Q dynamic control threshold, the new observation sample is determined to be abnormal. Abnormal data is obtained based on the contribution value, which solves the problems of adaptive deviation of control limits and fixed variable contribution in the existing MSPC method.

This invention relates to a method for detecting anomalies in non-stationary industrial processes based on slow feature decomposition and Koopman high-dimensional space prediction, belonging to the field of industrial process time-series anomaly detection technology. The method includes: using slow feature analysis to decompose industrial time-series data into fast and slow changing components; mapping the fast and slow features to a linearly observable Koopman high-dimensional space based on Koopman theory, constructing a deep prediction model to achieve stepwise prediction of the feature sequence; constructing a SPE statistic that approximately follows a weighted chi-square distribution by combining the deviation between the prediction results and the actual values, and setting SPE control limits for anomaly detection accordingly; introducing KL divergence to measure the distribution difference between the current operating condition and the normal operating condition, and adaptively updating the SPE control limits based on its magnitude; and during the online deployment phase of the model, calculating the SPE value in real time and comparing it with the control limits to achieve rapid identification and dynamic early warning of anomalies under non-stationary conditions.

The invention relates to the field of water treatment automation, and discloses an intelligent disinfection method, system, medium and equipment combining feedforward optimization and feedback correction, and the method comprises the following steps: in a feedforward stage, based on measurable parameters such as dosage, water inflow, water temperature, turbidity, pH and the like, predicting factory residual chlorine by utilizing a machine learning model, and optimizing and calculating basic dosage by combining a genetic algorithm; in the feedback stage, a short-time-delay chlorine consumption prediction model is established, and the deviation between the actual chlorine consumption and the theoretical chlorine consumption predicted after dosing is calculated; and monitoring the deviation in real time by using a statistical process control method, judging that unmonitored water quality disturbance exists when the deviation exceeds a statistical control limit, and calculating the compensation dosage according to the magnitude of the deviation. And superposing the optimal basic dosing amount and the compensation dosing amount to obtain a final execution dosing amount, and controlling a dosing pump to execute dosing.

The present application relates to the field of industrial process fault monitoring, and particularly relates to an uncertain process fault monitoring method based on local and global interval embedding. The method comprises the following steps: S1: obtaining an inaccurate single value data set of equipment under normal conditions; S2: establishing a local and global interval embedding model; S3: calculating statistics associated with the normal interval value data set according to the interval principal component; S4: collecting a new data sample; S5: calculating the statistics of the new interval value data by using the interval principal component determined by the local and global interval embedding model; S6: monitoring whether the four newly obtained statistics exceed the control limit, if the control limit is exceeded, the system is faulty, and step S7 is executed, otherwise, the next sample is monitored by returning to step S4; S7: if a fault occurs at the new sample, the variable with high contribution to the fault in the contribution map is calculated as the fault variable.

The application discloses an industrial process fault monitoring method, comprising the following steps: acquiring real-time multivariate time series data of an industrial process and performing standardization preprocessing; extracting the preprocessed data through a multiscale feature extraction module to generate multiscale latent feature representation; then processing the multiscale latent feature representation through a double-branch slow feature analysis module to extract time series stable slow feature representation; based on the slow feature representation and reconstructed data, constructing T 2 statistic and an SPE statistic; comparing the T 2 statistic and the SPE statistic with preset control limits; and determining whether the industrial process has a fault according to a comparison result. The application effectively extracts multiscale features and time series stable features of industrial process data, and significantly improves fault detection performance.

The invention relates to the technical field of boilers, and discloses an air pre-heater blockage monitoring method and system, and the method comprises the following steps: S1, offline training: taking a clean parameter in historical operation data of an air pre-heater as a sample to establish a principal component analysis monitoring model, carrying out the offline training, and determining a control limit SPElim of a residual space statistic SPE; s2, online monitoring: inputting real-time operation data of the air pre-heater into the principal component analysis monitoring model, then calculating SPE, calculating a blockage evaluation index bias, and then judging whether the blockage state of the air pre-heater is normal or not based on the bias: if yes, not performing early warning; if not, early warning is carried out, and early warning grading is carried out according to the numerical value of bias; wherein bias is equal to SPE / SPElim. The problems that in the prior art, the blockage state is difficult to accurately monitor, and the early warning capacity is weak are solved.

This invention discloses a multi-condition intelligent monitoring method and system for industrial processes based on continuous learning of element-aware dictionaries. It mainly comprises two parts: continuous learning of new modes and online monitoring. Continuous learning of new modes involves first sequentially learning the optimal dictionary and importance matrix corresponding to the first N modes; then, constructing an objective function based on the reconstruction error of the (N+1)th mode monitoring data and the loss of historical modes, and learning the optimal dictionary for the (N+1)th mode; finally, using this optimal dictionary for the latest mode, a monitoring model based on control limits is established. Online monitoring utilizes control limits to monitor new online data and determine whether the industrial process is currently malfunctioning. This invention can continuously learn new modes while maintaining a memory of historical modes, overcoming the "catastrophic forgetting" problem of traditional process monitoring methods in multi-modal situations, and achieving a high level of monitoring accuracy.

This invention designs a fault detection method for chemical processes based on SDAE-LSVDD with correlated and independent variables. The method includes: collecting chemical process data X0 under normal operating conditions and standardizing the data to obtain data X. N The mutual information p between each data point is calculated. i And the mutual information q between each data point and the random Gaussian distribution. i X N Divided into the relevant variable space X NR and the space of independent variables X NI Using relevant variable spatial data X NR Train the SDAE and find its optimal parameter set θ; use data X from the independent variable space. NI Train the LSVDD and calculate its radius R; use SDAE to calculate the characteristic space H and residual space R of the relevant variable space, and calculate T. 2 The sum and Q statistic are obtained using kernel density estimation. lim ; Use SDAE to calculate the relevant variable space X MR The feature space and residual space are calculated, and T is calculated. 2 The Q statistic and the Q statistic are used to calculate the independent variable space X. MI The distance D from the data point to the center of the LSVDD circle is calculated; the statistical value is compared with the control limit, and the distance from the data point to the center of the circle is compared with the radius to achieve online real-time monitoring.

This invention discloses a robot welding quality monitoring method and system based on control charts, belonging to the field of welding monitoring technology. The method includes: real-time scanning of indentation and porosity defects in the workpiece weld; calculating the spacing between indentation and porosity defects to construct binary defect vector data; fitting a GBE distribution model to the binary defect vector data to estimate actual dependency parameters and actual size parameters, constructing an actual mean vector; truncating the binary defect vector data at the actual mean vector using a truncation method; constructing a truncated binary defect vector; and using a MEWMA control chart to monitor the robot welding quality. During monitoring, monitoring statistics are constructed and control limits are searched. By comparing the positional relationship of the monitoring statistics relative to the control limits, it is determined whether the robot's welding quality is under control. This invention can detect out-of-control signals earlier, significantly shorten the average running chain length, and achieve early detection and early warning of quality risks.

The invention discloses a clinical examination error identification and classification method. The method comprises the following steps: acquiring patient detection data of a clinical examination item and preprocessing the patient detection data; inputting the preprocessed data into the optimized first algorithm function and the optimized second algorithm function to obtain a corresponding first algorithm function value and a corresponding second algorithm function value; based on a preset control limit, identifying a data point with the first algorithm function value or the second algorithm function value exceeding the control limit as an initial alarm point; extracting features of the initial alarm point; and inputting the features into a pre-trained classification model to obtain a classification result of the initial alarm point, the classification result being used for indicating that the initial alarm point is a system error, a random error or a false alarm. According to the invention, the error detection and classification capability in the clinical examination process can be improved.