Reliable machine prognostic health management in the presence of missing data

2020 ◽  
pp. e5762
Author(s):  
Yu Huang ◽  
Yufei Tang ◽  
James VanZwieten ◽  
Jianxun Liu
Keyword(s):  
Missing Data ◽  
Health Management ◽  
Prognostic Health Management

Parameters Evaluation for Aero-Engine Prognostic Health Management

2012 ◽  
Vol 236-237 ◽  
pp. 192-196
Author(s):  
Fang Bai ◽  
Wen Li Jin
Keyword(s):  
Calculation Method ◽  
Health Management ◽  
Remaining Life ◽  
Gas Temperature ◽  
Aero Engine ◽  
Prognostic Health Management

Prognostic health-management of aero-engine’s fleet have features with multi-source and asynchronous. Cruise engine gas temperature (EGT) and Take-off EGT have always been the focus of evaluating gas performance and predicting the remaining life. The calculation method of EGT Margin was showed based on the introduction of the definition and classification of the EGT. It analyses not only the relationships between Cruise EGT and Take-off EGT but also each other’s main functions. Meanwhile, an instance was used to explain the disadvantages of using Cruise EGT to calculate life prognostics.

scholarly journals The Effects of Missing Data Characteristics on the Choice of Imputation Techniques

2020 ◽  
Vol 07 (02) ◽  
pp. 161-177
Author(s):  
Oyekale Abel Alade ◽  
Ali Selamat ◽  
Roselina Sallehuddin
Keyword(s):  
Missing Data ◽  
Missing Values ◽  
Health Management ◽  
Support Vector ◽  
Multiple Imputations ◽  
Original Dataset ◽  
Learning Machine ◽  
Elm Classifier ◽  
The Right

One major characteristic of data is completeness. Missing data is a significant problem in medical datasets. It leads to incorrect classification of patients and is dangerous to the health management of patients. Many factors lead to the missingness of values in databases in medical datasets. In this paper, we propose the need to examine the causes of missing data in a medical dataset to ensure that the right imputation method is used in solving the problem. The mechanism of missingness in datasets was studied to know the missing pattern of datasets and determine a suitable imputation technique to generate complete datasets. The pattern shows that the missingness of the dataset used in this study is not a monotone missing pattern. Also, single imputation techniques underestimate variance and ignore relationships among the variables; therefore, we used multiple imputations technique that runs in five iterations for the imputation of each missing value. The whole missing values in the dataset were 100% regenerated. The imputed datasets were validated using an extreme learning machine (ELM) classifier. The results show improvement in the accuracy of the imputed datasets. The work can, however, be extended to compare the accuracy of the imputed datasets with the original dataset with different classifiers like support vector machine (SVM), radial basis function (RBF), and ELMs.

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