NDLI: Three-step imputation of missing values in condition monitoring datasets

Content Provider	IET Digital Library
Author	Liu, Hang Wang, Youyuan Chen, Wei Gen
Abstract	Missing values are a common occurrence in condition monitoring datasets. To effectively improve the integrity of data, many data imputation methods have been developed to replace the missing values with the estimated values. However, these methods do not always perform well in datasets containing different types of missing values. Three types of missing data are defined, namely isolated missing value, continuous missing variable, and continuous missing sample. A three-step data imputation method is proposed to sequentially impute these missing values following the principle from easy to difficult. The original time series data is first to split into different segments according to the positions of continuous missing samples. Then, interpolation and space-based methods are applied to sequentially estimate isolated missing values and continuous missing variables in each segment. Finally, a stepwise extrapolation prediction model based on the long short-term memory network is established to repair continuous missing samples between each segment. Two application examples are implemented on different dissolved gas analysis datasets and load datasets. Compared with state-of-the-art techniques, the proposed three-step data imputation method is general and can be applied to many scenarios because it establishes a rational data recovery sequence to accurately repair both stationary and non-stationary condition monitoring data.
Starting Page	3288
Ending Page	3300
Page Count	13
ISSN	17518687
Volume Number	14
e-ISSN	17518695
Issue Number	Issue 16, Aug (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-gtd/14/16
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-gtd.2019.1446
Journal	IET Generation, Transmission & Distribution
Publisher Date	2020-05-19
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Bayes Method Condition Monitoring Condition Monitoring Datasets Continuous Missing Sample Continuous Missing Variable Data Handling Technique Data Mining Fault Diagnosis Interpolation Interpolation And Function Approximation Knowledge Engineering Technique Long Short-term Memory Network Maintenance Engineering Neural Computing Technique Nonstationary Condition Monitoring Data Numerical Analysis Power Engineering Computing Recurrent Neural Nets Regression Analysis Space-based Method Stationary Condition Monitoring Data Statistics Stepwise Extrapolation Prediction Model Three-step Data Imputation Method Time Series
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Energy Engineering and Power Technology Electrical and Electronic Engineering

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Hybrid data-driven approach for truck travel time imputation

Feature embedding and conditional neural processes for data imputation

Optimal search algorithm in a big database using interpolation–extrapolation method

Urban road traffic condition forecasting based on sparse ride-hailing service data

Imputing missing values using cumulative linear regression

Supervisory control and data acquisition data-based non-linear state estimation technique for wind turbine gearbox condition monitoring

Hybrid feature selection–artificial intelligence–gravitational search algorithm technique for automated transformer fault determination based on dissolved gas analysis

Robust and flexible strategy for missing data imputation in intelligent transportation system

Study on fault diagnosis of broken rotor bars in squirrel cage induction motors: a multi-agent system approach using intelligent classifiers

Three-step imputation of missing values in condition monitoring datasets

Similar Documents

Hybrid data-driven approach for truck travel time imputation

Feature embedding and conditional neural processes for data imputation

Optimal search algorithm in a big database using interpolation–extrapolation method

Urban road traffic condition forecasting based on sparse ride-hailing service data

Imputing missing values using cumulative linear regression

Supervisory control and data acquisition data-based non-linear state estimation technique for wind turbine gearbox condition monitoring

Hybrid feature selection–artificial intelligence–gravitational search algorithm technique for automated transformer fault determination based on dissolved gas analysis

Robust and flexible strategy for missing data imputation in intelligent transportation system

Study on fault diagnosis of broken rotor bars in squirrel cage induction motors: a multi-agent system approach using intelligent classifiers

Three-step imputation of missing values in condition monitoring datasets