The Use of Probabilistic Reasoning to Improve Least Squares Based Gas Path Diagnostics

2006 ◽  
Vol 129 (4) ◽  
pp. 970-976 ◽  
Author(s):  
C. Romessis ◽  
Ph. Kamboukos ◽  
K. Mathioudakis
Keyword(s):  
Fault Diagnosis ◽  
Least Squares ◽  
Probabilistic Reasoning ◽  
Statistical Information ◽  
Least Square ◽  
Jet Engine ◽  
Engine Component ◽  
New Type ◽  
Component Faults ◽  
Health Parameters

A method is proposed to support least square type of methods for deriving health parameters from a small number of independent gas path measurements. The method derives statistical information using sets of solutions derived from a number of data records, to produce sets of candidate solutions with a lesser number of parameters. These sets can then be processed to derive an accurate component fault diagnosis. It could thus be classified as a new type of "concentrator" approach, which is shown to be more effective than previously existing schemes. The method's effectiveness is demonstrated by application to a number of typical jet engine component faults.

The Use of Probabilistic Reasoning to Improve Least Squares Based Gas Path Diagnostics

2006 ◽  
Author(s):  
C. Romessis ◽  
Ph. Kamboukos ◽  
K. Mathioudakis
Keyword(s):  
Fault Diagnosis ◽  
Least Squares ◽  
Probabilistic Reasoning ◽  
Statistical Information ◽  
Least Square ◽  
Jet Engine ◽  
Engine Component ◽  
New Type ◽  
Component Faults ◽  
Health Parameters

A method is proposed to support least square type of methods for deriving health parameters from a small number of independent gas path measurements. The method derives statistical information using sets of solutions derived from a number of data records, to produce sets of candidate solutions with a lesser number of parameters. These sets can then be processed to derive an accurate component fault diagnosis. It could thus be classified as a new type of “concentrator” approach, which is shown to be more effective than previously existing schemes. The method’s effectiveness is demonstrated by application to a number of typical jet engine component faults.

Jet Engine Component and Transducer Fault Diagnosis Using an Inverse Dynamic Model

2005 ◽  
Author(s):  
M. Lichtsinder ◽  
Y. Levy
Keyword(s):  
Fault Diagnosis ◽  
Inverse Dynamic ◽  
Fast Evaluation ◽  
Engine Operation ◽  
Inverse Dynamic Model ◽  
Jet Engine ◽  
Engine Model ◽  
Engine Component ◽  
Using Data ◽  
Real Time Simulations

Engine component and transducer degradation/fault diagnosis, are analyzed. The analysis is performed using an aero-thermodynamic nonlinear inverse jet-engine model while using data acquired during transient engine operation. A shortened inverse jet-engine model (without one or more engine component maps) was recently proposed by the authors for real-time simulations and for fast evaluation of engine component maps. The algorithm for the engine component’s fault diagnosis is significantly simplified using shortened inverse engine models. A diagnostic example of combined faults of a single transducer and a single engine component for a single spool jet engine is described using different combinations of shortened inverse jet engine models. In the present paper it is assumed that only a single transducer (out of the seven transducers) and /or a single engine component (compressor or turbine) fault could be present in the engine at a given time.

Sparse Bayesian Learning for Gas Path Diagnostics

2013 ◽  
Vol 135 (7) ◽  
Author(s):  
Xingxing Pu ◽  
Shangming Liu ◽  
Hongde Jiang ◽  
Daren Yu
Keyword(s):  
Bayesian Learning ◽  
Diagnostic Method ◽  
Learning Algorithm ◽  
Least Square ◽  
Real Problem ◽  
Sparse Bayesian Learning ◽  
Single Fault ◽  
Sparse Solutions ◽  
Component Faults ◽  
Health Parameters

A gas path diagnostic method based on sparse Bayesian learning is presented. Most gas path diagnostic problems present the case where there are fewer measurements than health parameters. In addition, the measurement readings can be faulty themselves and need to be determined, which further increases the number of unknown variables. The number of unknown variables exceeds the number of measurements in gas path diagnostics, making the estimation problem underdetermined. For gradual deterioration, it is common to apply a weighted-least-square algorithm to estimate the component health parameters at the same time sensor errors are being determined. However, this algorithm may underestimate the real problem and attribute parts of it to other component faults for accidental single fault events. The accidental single fault events impact at most one or two component(s). This translates mathematically into the search for a sparse solution. In this paper, we proposed a new gas path diagnostic method based on sparse Bayesian learning favoring sparse solutions for accidental single fault events. The sparse Bayesian learning algorithm is applied to a heavy-duty gas turbine considering component faults and sensor biases to demonstrate its capability and improved performance in gas path diagnostics.

Determination of Pyridostigmine Bromide in Presence of its Related Impurities by Four Modified Classical Least Square Based Models: A Comparative Study

2020 ◽  
Vol 17 (1) ◽  
pp. 87-94
Author(s):  
Ibrahim A. Naguib ◽  
Fatma F. Abdallah ◽  
Aml A. Emam ◽  
Eglal A. Abdelaleem
Keyword(s):  
Comparative Study ◽  
Least Squares ◽  
Orthogonal Projection ◽  
Predictive Ability ◽  
Least Square ◽  
Projection To Latent Structures ◽  
Preprocessing Method ◽  
Spectral Residual ◽  
Latent Structures

: Quantitative determination of pyridostigmine bromide in the presence of its two related substances; impurity A and impurity B was considered as a case study to construct the comparison. Introduction: Novel manipulations of the well-known classical least squares multivariate calibration model were explained in detail as a comparative analytical study in this research work. In addition to the application of plain classical least squares model, two preprocessing steps were tried, where prior to modeling with classical least squares, first derivatization and orthogonal projection to latent structures were applied to produce two novel manipulations of the classical least square-based model. Moreover, spectral residual augmented classical least squares model is included in the present comparative study. Methods: 3 factor 4 level design was implemented constructing a training set of 16 mixtures with different concentrations of the studied components. To investigate the predictive ability of the studied models; a test set consisting of 9 mixtures was constructed. Results: The key performance indicator of this comparative study was the root mean square error of prediction for the independent test set mixtures, where it was found 1.367 when classical least squares applied with no preprocessing method, 1.352 when first derivative data was implemented, 0.2100 when orthogonal projection to latent structures preprocessing method was applied and 0.2747 when spectral residual augmented classical least squares was performed. Conclusion: Coupling of classical least squares model with orthogonal projection to latent structures preprocessing method produced significant improvement of the predictive ability of it.

Application of Fitting Estimation Method in GPS Height Fitting

2013 ◽  
Vol 694-697 ◽  
pp. 2545-2549 ◽  
Author(s):  
Qian Wen Cheng ◽  
Lu Ben Zhang ◽  
Hong Hua Chen
Keyword(s):  
Least Squares ◽  
Estimation Method ◽  
Least Square ◽  
Least Squares Collocation ◽  
Normal Height ◽  
Geodetic Height ◽  
The Given ◽  
Surveying And Mapping

The key point researched by many scholars in the field of surveying and mapping is how to use the given geodetic height H measured by GPS to obtain the normal height. Although many commonly-used fitting methods have solved many problems, they all value the pending parameters as the nonrandom variables. Figuring out the best valuations, according to the traditional least square principle, only considers its trend or randomness, which is theoretically incomprehensive and have limitations in practice. Therefore, a method is needed not only considers its trend but also takes randomness into account. This method is called the least squares collocation.

Research on the Wire Drawing Die Pass Parameter Calculation Based on the Least Square Algorithm

2012 ◽  
Vol 591-593 ◽  
pp. 850-853
Author(s):  
Huai Xing Wen ◽  
Yong Tao Yang
Keyword(s):  
Pore Structure ◽  
Least Squares ◽  
Least Squares Method ◽  
Piecewise Linear ◽  
Wire Drawing ◽  
Least Square ◽  
Structure Characteristics ◽  
Drawing Die ◽  
The Wire ◽  
Drawing Dies

Drawing Dies meter A / D acquisition module will be collected from the mold hole contour data to draw a curve in Matlab. According to the mold pore structure characteristics of the curve, the initial cut-off point of each part of contour is determined and iteratived optimization to find the best cut-off point, use the least squares method for fitting piecewise linear and fitting optimization to find the function of the various parts of the curve function, finally calculate the pass parameters of drawing mode. Parameters obtained compare with the standard mold, both of errors are relatively small that prove the correctness of the algorithm. Also a complete algorithm flow of pass parameters is designed, it can fast and accurately measure the wire drawing die hole parameters.

Sign in / Sign up

Export Citation Format

Share Document