Dealing with missing data for prognostic purposes

Centrifugal compressors are considered one of the most critical components in oil industry, making the minimization of their downtime and the maximization of their availability a major target. Maintenance is thought to be a key aspect towards achieving this goal, leading to various maintenance schemes being proposed over the years. Condition based maintenance and prognostics and health management (CBM/PHM), which is relying on the concepts of diagnostics and prognostics, has been gaining ground over the last years due to its ability of being able to plan the maintenance schedule in advance. The successful application of this policy is heavily dependent on the quality of data used and a major issue affecting it, is that of missing data. Missing data's presence may compromise the information contained within a set, thus having a significant effect on the conclusions that can be drawn from the data, as there might be bias or misleading results. Consequently, it is important to address this matter. A number of methodologies to recover the data, called imputation techniques, have been proposed. This paper reviews the most widely used techniques and presents a case study with the use of actual industrial centrifugal compressor data, in order to identify the most suitable ones

Modelling and Forecasting the Unit Cost of Electricity Generated by Fossil Fuel Power Plants in Sri Lanka

The national grid system which is evolved to deliver electricity must be always kept in balance so that it must have a sufficient production to meet the demand of electricity while minimizing the generation cost. This study presents a statistical time series model for forecasting the Unit Cost (UC) of generation of electricity in fossil fuel power plants by using two approaches namely Auto Regressive Integrated Moving Average (ARIMA) and time series regression. This is conducted as a case study in a Diesel/Heavy Fuel Oil (HFO) power plant in Sri Lanka which consists of two sub stations. ARIMA (1,1,0) and ARIMA (2,1,2) were selected as the best models with the lowest Akaike Information Criterion (AIC) under the ARIMA model approach while two dynamic regression models with coefficient of determination (R2) value 0.55 were selected under time series regression approach for Station 1 and Station 2 respectively. The regression model was identified as the best forecasting method for two stations with the minimum Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The forecasts of the future generation cost of electricity are extensively helpful for the national grid system for financial and capacity planning, fuel management and operational planning

Dealing with missing data for prognostic purposes

© 2016 IEEE. Centrifugal compressors are considered one of the most critical components in oil industry, making the minimisation of their downtime and the maximisation of their availability a major target. Maintenance is thought to be a key aspect towards achieving this goal, leading to various maintenance schemes being proposed over the years. Condition based maintenance and prognostics and health management (CBM/PHM), which is relying on the concepts of diagnostics and prognostics, has been gaining ground over the last years due to its ability of being able to plan the maintenance schedule in advance. The successful application of this policy is heavily dependent on the quality of data used and a major issue affecting it, is that of missing data. Missing data's presence may compromise the information contained within a set, thus having a significant effect on the conclusions that can be drawn from the data, as there might be bias or misleading results. Consequently, it is important to address this matter. A number of methodologies to recover the data, called imputation techniques, have been proposed. This paper reviews the most widely used techniques and presents a case study with the use of actual industrial centrifugal compressor data, in order to identify the most suitable ones

FORWARD AND BACKWARD FORECASTING ENSEMBLES FOR THE ESTIMATION OF TIME SERIES MISSING DATA

The presence of missing information in time arrangement is large hindrance to the fruitful execution of estimating models, as it prompts a critical decrease of helpful information. In this work we propose a multipleimputation-type structure for assessing the missing estimations of a period arrangement. This structure depends on iterative and progressive forward and in reverse guaging of the missing qualities, and building outfits of these gauges. The iterative idea of the calculation permits reformist improvement of the estimate exactness. What's more, the distinctive forward and in reverse elements of the time arrangement give helpful variety to the outfit. The created system is general, and can utilize any hidden AI or customary determining model. We have tried the proposed approach on huge informational collections utilizing straight, just as nonlinear hidden estimating models, and show its prosperity