The invention relates to a failure prediction method based on the nonlinear failure reconstruction. The method comprises the following steps that (1), a KPCA model is utilized for carrying out off-line nonlinear modeling on process data monitored when a rotary mechanical system operates, carrying out abnormity detection and extracting failure information, wherein the KPCA model is the kernel principal component analysis model; (2), the failure degree is quantificationally described through the KPCA model and the abnormity detection in the step (1), and failure estimation is achieved under the failure reconstruction through the optimization method; (3), the development tendency of the estimated failure amplitude f is predicted through the multi-layer hierarchical method. The failure prediction method based on the nonlinear failure reconstruction can effectively improve the failure prediction efficiency, reduce false alarm rate and omitted alarm rate and lay the foundation for further researching the prediction maintenance technology of a complex mechanical system. The failure prediction method based on the nonlinear failure reconstruction can also be widely applied to online monitoring systems of large electromechanical devices of petrochemical enterprises, metallurgy enterprises, coal enterprises and other many enterprises.

Failure prediction method based on nonlinear failure reconstruction

The invention provides a KPCA-based fused magnesium furnace fault diagnosis method for fault separation and reconstruction. The method comprises the following steps: acquiring historical normal data of a fused magnesium furnace, historical fault data when the fused magnesium furnace generates a fault, and test data when a fault occurs in online monitoring; performing preprocessing on the historical normal data of the fused magnesium furnace and the historical fault data; performing high-dimension mapping and then PCA decomposition on the historical normal data of the fused magnesium furnace by use of a nucleus principal component analytical method; and performing diagnosing and fault reconstruction on the test fault data when the fault occurs in the online monitoring by use of a fault load vector set, determining a fault type, and recovering the fault data to corresponding normal data so that the fault is eliminated. According to the method provided by the invention, the fault separation and reconstruction problems of non-linear data of the fused magnesium furnace are solved, the test data when the fault occurs in the online monitoring can be monitored, only a fault model corresponding to a current fault can correctly remove fault information in the data, based on this, the fault type can be determined, and the purpose of fault separation is realized.

KPCA-based fused magnesium furnace fault diagnosis method for fault separation and reconstruction

The invention provides a rolling process slip diagnosis and prediction method based on data driving Normal data in the normal rolling state are used for nonlinear analysis, kernel principal component information is extracted, and a kernel principal component model is established Test data are mapped to the kernel principal component model, whether the statistics T2 and SPE of the test data exceed control limits or not is checked and whether a slip fault exists in a rolling mill or not is judged If the slip fault exists, fault isolation is performed on variables through a contribution chart method, slip reasons are judged accurately, corresponding rolling parameter set values are modified, and the slip phenomenon is avoided By the adoption of the method, the slip fault in the rolling process can be monitored on line and the preset rolling regulation can be diagnosed

Rolling process slip diagnosis and prediction method based on data driving

The invention discloses a randomized kernel principle component analysis-based large-scale non-linear process monitoring method, belonging to the technical field of industrial process monitoring and diagnosis and mainly solving the rapid fault detection problem of the non-linear process under the industrial big data background. The randomized kernel principle component analysis-based large-scale non-linear process monitoring method comprises the following steps: I. collecting the process history running data, and performing standardized preprocessing to the data; II. according to a randomizedkernel principle component analysis method, extracting a non-linear principle component, and determining the detection control limit; and III. collecting online measurement data, calculating the detection statistics of the online measurement data according to the non-linear principle component extracted from training data, and comparing with the detection control limit, if exceeding the control limit, reminding the process of abnormality. By using the randomized kernel principle component analysis-based large-scale non-linear process monitoring method, the process monitoring efficiency can beimproved, the abnormality in the large-scale non-linear industrial process can be rapidly detected, and the loss caused by faults can be decreased.

Randomized kernel principle component analysis-based large-scale non-linear process monitoring method

The invention discloses a system design method for minor fault detection and position of an electrical traction system The method comprises the following steps: 1) establishing an off-line data model: collecting sensor steady-state operation data of the electrical traction system and carrying out pretreatment on the off-line data; calculating values of a principal component and a residual component in different layers of subspaces of the data obtained after pretreatment and a load vector; determining and calculating a performance index and a probability density function of a principle component analysis method and a fault detection threshold; and 2) on-line fault diagnosis: processing online data; calculating a performance index of the principal component and the residual component in different layers of subspaces; and through the obtained fault detection threshold, constructing a probability matrix and carrying out fault diagnosis through Bayesian reasoning The method can carry outeffective multi-feature description on tiny signals of an electrical drive device before occurrence of a fault, and can also carry out real-time online fault diagnosis under the condition that the model and parameters of the electrical drive system are unknown

System design method for minor fault detection and position of electrical traction system

The invention relates to a method and a system of failure prediction. The method of the failure prediction comprises calculating a kernel principal element, and detecting a failure according to control limits. A method based on kernel principal component analysis (KPCA) reconfiguration is adopted in the failure prediction aiming at rotating machinery, the nonlinear problem of process data can be solved well, a failure direction is obtained from the data implying failures, a failure amplitude value is estimated, the multi-dimensional character of the failures is considered, and an accurate failure prediction result can be obtained.

Method and system of failure prediction

Popular Tasks used by Researchers

How can I help with your research?

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

We encountered an error while loading the panel. Please try refreshing the page.

Something went wrong

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

Anyone with the link can view this chat. You can switch it back to private anytime.

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Chen Mo

Author Tools

Chat about Author

Papers

Method and system of failure prediction