196 results about "Control limits" patented technology

The invention discloses a non-stationary industrial process early fault diagnosis method based on a common trend model, and belongs to the field of industrial process monitoring and fault diagnosis. The method comprises the following steps: collecting training data of a non-stationary industrial process under a normal working condition, performing denoising by using singular spectrum analysis, and obtaining a stationary projection matrix and a stationary data matrix by using a common trend model; orthogonal conversion is carried out on the stationary data matrix, a conversion projection matrix is calculated, and an orthogonal conversion data matrix and a statistic data matrix thereof are obtained; calculating a mean value and a covariance matrix of the statistic data matrix to obtain a control limit; the method comprises the following steps: acquiring test data under a real-time working condition of an industrial process, processing to obtain statistic data, calculating a mahalanobis distance index, and comparing with a control limit to judge whether a fault occurs; and if a fault is detected, realizing fault separation according to fault reconstruction and a mapping relation. According to the method, the interference of the non-stationary characteristic on the fault diagnosis model can be reduced, and fault detection and separation in the non-stationary process are realized.

The invention discloses a shield tunneling machine tunneling key parameter real-time multi-step prediction method based on ST-GCN-LSTM, and the method comprises the following steps: obtaining the operation data of a shield tunneling machine under a normal working condition, and carrying out the preprocessing of the operation data; analyzing the preprocessed operation data, and determining related variables of total thrust, cutterhead torque, cutterhead rotating speed, penetration and propelling speed average value tunneling key variables; respectively calculating the control limit of each tunneling key variable for different soil layers, and training a multi-step prediction model based on an ST-GCN and LSTM combined model; acquiring operation data of the shield tunneling machine in real time, inputting the operation data into the trained model for preprocessing, dynamically predicting tunneling key parameter values of multiple steps in the future, and judging whether predicted values continuously exceed a control limit or not; according to the method, real-time multi-step prediction of the tunneling key parameters can be carried out in combination with soil layer classification, and prediction inaccuracy caused by changes of external factors such as soil layers is reduced.

The invention provides a generator excitation system fault detection method and system, and the method comprises the steps: firstly collecting the historical data of a generator excitation system, and forming a historical data set; then, constructing a stacked sparse auto-encoder network, taking the historical data set as input, and training the stacked sparse auto-encoder network in combination with a physical constraint equation of the generator excitation system; and finally, collecting real-time data of a generator excitation system, inputting the real-time data into the trained stacked sparse auto-encoder network, calculating an SPE index, and carrying out fault diagnosis through an SPE index control limit. Physical information is fused in the model training process, the detection result has high accuracy, the interpretability of the result is enhanced, and compared with a pure data driving method, the method has the advantages that dependence on large-scale fault samples is remarkably reduced, and the detection accuracy is greatly improved in an unknown scene or a scene without faults. High detection precision and robustness can still be kept, and the generalization ability and engineering applicability of the model are improved.

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 provides an SPC control method, system and device based on dynamic threshold optimization and a medium, and can solve the problems caused by a traditional SPC static threshold, and the method comprises the steps: collecting and preprocessing an original data stream containing key quality characteristics or key process parameters from various data sources on a production line in real time, obtaining a structured valid data sequence; intercepting current sliding window data from the structured valid data sequence by adopting a sliding time window mechanism, performing abnormal value processing, and dynamically calculating a control limit of the SPC control chart based on the processed sliding window data to obtain a first control limit; when the process parameter step change of the production line is monitored, updating the control limit of the SPC control chart to obtain a second control limit; and comparing the latest effective data output in the structured effective data sequence with the first control limit or the second control limit in real time, and triggering an alarm when judging that the production process is abnormal according to a comparison result.

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 provides a strip steel hot continuous rolling industrial process monitoring method and device, and relates to the technical field of ferrous metallurgy industrial process monitoring. The method comprises the following steps: an off-line modeling stage: collecting multi-dimensional sensor data in the strip steel hot continuous rolling industrial process for standardized cleaning; carrying out data enhancement on the cleaned data by adopting DDIM; random shielding is carried out by using a feature focusing cooperation mechanism based on random shielding; constructing an unsupervised process monitoring model, and training the constructed unsupervised process monitoring model by using the data after the shielding operation; processing data reconstructed by the trained unsupervised process monitoring model by adopting a kernel density estimation method, and determining a control limit; and an on-line monitoring stage: judging whether the current sample is normal or not by using the control limit to realize abnormal working condition detection. By adopting the method, the monitoring sensitivity of the strip steel hot continuous rolling industrial process can be improved.

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 discloses a cloud edge collaborative intelligent autonomous monitoring method for a dynamic industrial process. The method comprises the steps that a cloud end establishes a dictionary model and trains and updates the model; the edge device obtains the latest dictionary, classifier and control limit from the cloud end, and then carries out abnormity monitoring and working condition identification on the current industrial process; wherein the cloud process comprises the following steps: establishing a dictionary model and training to obtain a dictionary, a classifier and a coding matrix; screening the historical samples based on the multi-azimuth hardness values of the historical samples, and forming a balanced data set with the new working condition samples to update the dictionary and the classifier; and taking the reconstructed data set and the reconstructed label as complete working condition knowledge, compressing and simplifying the updated over-complete dictionary and classifier, and taking the compressed and simplified dictionary and classifier as a dictionary and a classifier which are finally deployed to an edge layer. According to the invention, reliable and efficient monitoring of dynamic industrial occasions can be realized.

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 discloses a heterogeneous low-quality industrial time series data anomaly detection method, which comprises the steps of collecting heterogeneous time series data, performing continuous process variable augmentation, and further constructing a training set; in a heterogeneous feature integration pre-training stage, a heterogeneous representation learning network is constructed, and pre-training is performed by using a pre-training sample set in a training set; in a fusion detection adaptive fine tuning stage, a heterogeneous fusion detection network is constructed, and a fine tuning sample set in a training set is used for training; and obtaining a reconstruction value of a continuous process variable by using the finally trained heterogeneous fusion detection network, and completing anomaly detection according to an anomaly detection control limit. According to the method, the time sequence dependency relationship between heterogeneous variables can be accurately modeled in a data low-quality scene with variable isomerism and measurement point missing, the accuracy of anomaly detection is effectively improved, and powerful support is provided for intelligent operation and maintenance and optimization decision making of an industrial production system.

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 invention relates to a non-stationary industrial process anomaly detection method based on slow characteristic decomposition and Kupman high-dimensional space prediction, and belongs to the technical field of industrial process time sequence anomaly detection.The method comprises the steps that characteristic decomposition is conducted on industrial time sequence data through a slow characteristic analysis method, and the industrial time sequence data are divided into fast and slow change parts; respectively mapping the fast and slow features to a linearly observable Kupman high-dimensional space based on the Kupman theory, and constructing a depth prediction model to realize step-by-step prediction of a feature sequence; in combination with the deviation between a prediction result and an actual value, constructing an SPE statistical magnitude approximately obeying weighted chi-square distribution, and setting an SPE control limit of anomaly detection according to the SPE statistical magnitude; introducing KL divergence to measure a distribution difference between a current working condition and a normal working condition, and adaptively updating an SPE control limit according to the distribution difference; and in the model online deployment stage, an SPE value is calculated in real time and compared with a control limit, so that abnormal rapid identification and dynamic early warning under a non-stable working condition are realized.

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 present invention discloses a method for monitoring an on-load tap changer transmission mechanism based on sparse filtering, which relates to the technical field of on-load tap changer transmission mechanism detection. The method involves performing multiple gear switching on the on-load tap changer to collect vibration signals of the transmission mechanism during the on-load tap changer switching process. A sparse filtering network-based vibration signal characteristic distribution matrix of the on-load tap changer transmission mechanism is calculated based on the vibration signals. The eigenvalues ​​and eigenvectors of the principal component components of the vibration signal characteristic distribution matrix are calculated. The weighted mean of the elements of the statistic and the corresponding control limits are calculated based on the eigenvalues ​​and eigenvectors of the principal component components of the characteristic distribution matrix. The mechanical state of the transmission mechanism is determined based on the weighted mean of the elements of the statistic and the corresponding control limits. The method of the present invention efficiently and accurately determines the mechanical state of the on-load tap changer transmission mechanism by performing real-time monitoring and computational analysis of the vibration signals of the transmission mechanism during the on-load tap changer switching process.

The invention discloses a data secondary correction-based current transformer error state online detection method and system. The method comprises the following steps of: acquiring historical data and real-time test data of a current transformer to form current data; performing primary correction on the test data to obtain primary correction data of the current data; performing secondary correction on the primary correction data to obtain secondary correction data of the current data; calculating a Q statistic by using the secondary correction data, comparing the Q statistic with a statistic control limit Qc, and judging whether the error state of the current transformer is abnormal or not; the method has the advantage that the online detection of the error state of the current transformer can be realized under the condition that the fluctuation of the unbalance degree is relatively large.

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 blast furnace ironmaking process anomaly detection method based on score co-integration, which comprises the following steps: acquiring key process variables in a blast furnace ironmaking process, and forming a time sequence by the key process variables; carrying out stationarity analysis on the time sequence by adopting an augmented Dickey-Fuller test method, and identifying non-stationary variables in the data to obtain a non-stationary time sequence; extracting a non-stationary trend part of the non-stationary time sequence by adopting a trend extraction algorithm; modeling the trend part of the non-stationary variable by using the FCVAR, and constructing an anomaly detection model based on the FCVAR; through the control limit detection of the statistical magnitude, the abnormality detection of the blast furnace ironmaking process is realized, and if the statistical magnitude exceeds the control limit, the abnormality exists in the blast furnace ironmaking process. The method solves the problem of non-stability of variables in the ironmaking process, is particularly suitable for variables with long memory characteristics, and can accurately capture the long-term equilibrium relation between the variables, find tiny abnormal changes in time and ensure the safety and stability of production.

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.

The invention relates to the technical field of oil and gas metering, in particular to a flowmeter fault online detection and classification method based on time-frequency characteristics. According to the flowmeter fault online detection and classification method based on the time-frequency characteristics, firstly, vibration and sound are extracted through wavelet packet decomposition, time-frequency parameters are calculated, a PCA model is constructed, the PCA model is used for determining the control limits of SPE statistics and T2 statistics, the statistics and the control limits of real-time data are compared, the contribution rate of each time-frequency characteristic is calculated, and the time-frequency characteristics of the real-time data are calculated. And screening the time-frequency characteristics according to the contribution rate and establishing a training set and a test set, finally training the MASP model by using the training set and the test set, and sequentially substituting to-be-detected data into the PCA model and the trained MASP model. According to the flowmeter fault on-line detection and classification method based on the time-frequency characteristics, through comprehensive use of the PCA model and the MASP model, the operation condition of the scraper flowmeter can be judged, and the fault condition can be classified.

The present invention discloses a process quality control method combining FMEA analysis and multivariate control charts, including: planning and preparing for PFMEA; identifying special quality indicators of process parameter diagrams; determining control limits and abnormal patterns through failure analysis and risk analysis in PFMEA, so as to accurately identify abnormal fluctuations in the product processing process; decoupling and correlation analysis of multivariate quality indicators; constructing multivariate SPC control charts; identifying and tracing the abnormal patterns of multivariate SPC control charts; optimizing PFMEA and adjusting the sampling frequency and control limits in combination with SPC control charts. The present invention solves technical problems such as it is difficult to control the product quality of large parts through frequent inspections, the abnormal patterns identified by univariate SPC control charts are inaccurate, the SPC control charts cannot be formulated according to the actual production process, and the SPC technology cannot effectively improve the production process.

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.