A novel robust data reconciliation method for industrial processes

2019 ◽  
Vol 83 ◽  
pp. 203-212 ◽  
Author(s):  
Sen Xie ◽  
Chunhua Yang ◽  
Xiaofeng Yuan ◽  
Xiaoli Wang ◽  
Yongfang Xie
Keyword(s):  
Data Reconciliation ◽  
Industrial Processes ◽  
Reconciliation Method

scholarly journals The Reconciliation of Multiple Conflicting Estimates: Entropy-Based and Axiomatic Approaches

Entropy ◽  
2018 ◽  
Vol 20 (11) ◽  
pp. 815
Author(s):  
João Rodrigues ◽  
Michael Lahr
Keyword(s):  
First Principles ◽  
Axiomatic Approach ◽  
Data Reconciliation ◽  
Arithmetic Average ◽  
Weighted Arithmetic ◽  
Harmonic Average ◽  
Alternative Setting ◽  
Axiomatic Approaches ◽  
Reconciliation Method ◽  
The Ideal

When working with economic accounts it may occur that multiple estimates of a single datum exist, with different degrees of uncertainty or data quality. This paper addresses the problem of defining a method that can reconcile conflicting estimates, given best guess and uncertainty values. We proceeded from first principles, using two different routes. First, under an entropy-based approach, the data reconciliation problem is addressed as a particular case of a wider data balancing problem, and an alternative setting is found in which the multiple estimates are replaced by a single one. Afterwards, under an axiomatic approach, a set of properties is defined, which characterizes the ideal data reconciliation method. Under both approaches, the conclusion is that the formula for the reconciliation of best guesses is a weighted arithmetic average, with the inverse of uncertainties as weights, and that the formula for the reconciliation of uncertainties is a harmonic average.

Application of Data Reconciliation Method to Increase CCPP Performance Test Result Accuracy

2015 ◽  
Author(s):  
Zengqian Wang ◽  
Jingjin Ji ◽  
Xinghao Wang ◽  
Bo Sun ◽  
Lei He ◽  
...  
Keyword(s):  
Uncertainty Analysis ◽  
Power Output ◽  
Performance Test ◽  
Heat Rate ◽  
Performance Data ◽  
Data Reconciliation ◽  
Acceptance Test ◽  
Analysis Method ◽  
Reconciliation Method ◽  
Test Result

Performance acceptance test for gas-steam Combined Cycle Power Plant (CCPP) is of great significance for both equipment manufacturer and customer. The influence of measurement error on the calculation of guaranteed performance data as power output and heat rate can lead to unnecessary loss for either party. Commonly used uncertainty analysis method based on ASME PTC 19.1 would require all measuring instrumentation working at designed accuracy range. Meanwhile, due to the complexity of CCPP system and large number of measuring items, and as well the propagation of measurement and data reduction error, the uncertainty of corrected performance data could be significant. In this paper, process data reconciliation method based on VDI 2048 is introduced. With access to complete performance test data from a CCPP project, data reconciliation calculation is performed with an appropriate thermodynamic model. Several measurement values with gross error are identified and verified in heat balance calculation. Moreover, after recalculating with the reconciled data instead of raw data for the corrected power output and heat rate, comparison with the common uncertainty analysis method is also carried out. It is shown that with this reconciliation method, it is not only possible to find out gross errors such as instrumentation drift, but also able to dramatically increase the test result accuracy, which is of great value for both manufacturer and customer.

scholarly journals Industrial Processes: Data Reconciliation and Gross Error Detection

2009 ◽  
Vol 42 (7) ◽  
pp. 209-215 ◽  
Author(s):  
Yu Miao ◽  
Hongye Su ◽  
Rong Gang ◽  
Jian Chu
Keyword(s):  
Error Detection ◽  
Industrial Applications ◽  
Vital Role ◽  
Data Reconciliation ◽  
Industrial Processes ◽  
Random Errors ◽  
Process Data ◽  
Gross Error Detection ◽  
Business Decision ◽  
Gross Error

Process data plays a vital role in industrial processes, which are the basis for process control, monitoring, optimization and business decision making. However, it is inevitable that process data measurements will be corrupted by random errors. Therefore, data reconciliation has been developed to improve accuracy of process data by reducing the effect of random errors. Unfortunately, reconciled values would be deteriorated by gross errors, which may be present during measurement. Therefore, gross error detection is necessary to guarantee the efficiency of data reconciliation, which has been developed to identify and eliminate gross errors in process data. In this paper, a review of data reconciliation and gross error detection and relevant industrial applications are presented. As the efficiency of data reconciliation and gross error detection largely depends upon the locations of sensors, sensor networks design is also included in the review. Meanwhile, some achievements of the authors are also included.

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