Ar processes lasso procedure11/9/2023 Compared with the prior art, the industrial process fault detection method based on wavelet transform and the Lasso function has the advantages that all the eigenvalues are taken into consideration, and detection accuracy is improved. The industrial process fault detection method comprises the steps of (1) obtaining normal data and fault data from a Tennessee and Eastman industrial process model, carrying out standardization processing on the obtained data, (2) carrying out wavelet transform on the normal data, compressing the normal data, carrying out Lasso regression between each set of training data processed through wavelet transform and a training data matrix in the mode that each set of training data is used as a pivot element column vector, obtaining different minimum estimated values (please see the symbol in the specification), (3) obtaining the optimal minimum estimated value (please see the symbol in the specification) through a probability density estimation method, using the optimal minimum estimated value as a threshold, and (4) sequentially carrying out wavelet transform and Lasso regression on test data, comparing the minimum estimated value (please see the symbol in the specification) obtained from each set of test data with the threshold, and judging whether each set of test data has a fault or not. The invention relates to an industrial process fault detection method based on wavelet transform and a Lasso function. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.) Filing date Publication date Application filed by East China University of Science and Technology filed Critical East China University of Science and Technology Priority to CN201410177158.6A priority Critical patent/CN103926919B/en Publication of CN103926919A publication Critical patent/CN103926919A/en Application granted granted Critical Publication of CN103926919B publication Critical patent/CN103926919B/en Status Expired - Fee Related legal-status Critical Current Anticipated expiration legal-status Critical Links Original Assignee East China University of Science and Technology Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)Įast China University of Science and Technology ( en Inventor 江晓栋 赵海涛 沙钰杰 Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.) Granted Application number CN201410177158.6A Other languages Chinese ( zh) Google Patents Industrial process fault detection method based on wavelet transform and Lasso functionĭownload PDF Info Publication number CN103926919A CN103926919A CN201410177158.6A CN201410177158A CN103926919A CN 103926919 A CN103926919 A CN 103926919A CN 201410177158 A CN201410177158 A CN 201410177158A CN 103926919 A CN103926919 A CN 103926919A Authority CN China Prior art keywords data lasso industrial process wavelet transformation fault Prior art date Legal status (The legal status is an assumption and is not a legal conclusion. Google Patents CN103926919A - Industrial process fault detection method based on wavelet transform and Lasso function 1.3 R Code for Two Examples in Lessons 1.1 and 1.CN103926919A - Industrial process fault detection method based on wavelet transform and Lasso function.1.2 Sample ACF and Properties of AR(1) Model.1.1 Overview of Time Series Characteristics.In our example, the intercept for the simulated model for y t,1 equals The structure is that each variable is a linear function of past lags of itself and past lags of the other variables.Īs an example suppose that we measure three different time series variables, denoted by \(x_ $$ VAR models (vector autoregressive models) are used for multivariate time series.
0 Comments
Leave a Reply.AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |