scholarly journals Aero Engine Performance Monitoring Using Least Squares Regression and Spectral Clustering

2021 ◽  
Author(s):  
Kunaal Saxena ◽  
Manisha J Nene
Keyword(s):  
Machine Learning ◽  
Least Squares ◽  
Spectral Clustering ◽  
Performance Monitoring ◽  
Aerospace Industry ◽  
Engine Performance ◽  
Least Squares Regression ◽  
Flight Data ◽  
Aero Engine ◽  
Turboshaft Engine

Threshold-based flight data recorder analysis techniques have been widely used across the aerospace industry for fault detection and accident prevention. These techniques can detect pre-programmed events but fail to capture unknown patterns in the dataset. This research proposes a machine learning (ML) algorithm to analyze and detect unusual aero engine performance of a turboshaft engine mounted on a single engine rotorcraft. The performance is first modelled from an FDR dataset consisting of hundred flights, using least squares regression (LSR). A technique to scale the model by adding flight data from subsequent flights is thereafter discussed. Spectral Clustering is used for testing and validating the hypothesis derived from the regression model, by employing synthetically generated FDR data for twenty-five flights.

scholarly journals Uncertainty Quantification in Machine Learning and Nonlinear Least Squares Regression Models

2021 ◽  
Author(s):  
Ni Zhan ◽  
John Kitchin
Keyword(s):  
Machine Learning ◽  
Least Squares ◽  
Uncertainty Quantification ◽  
Automatic Differentiation ◽  
Nonlinear Least Squares ◽  
Delta Method ◽  
Training Data ◽  
Model Parameters ◽  
Uncertainty Measure ◽  
Least Squares Regression

Machine learning (ML) models are valuable research tools for making accurate predictions. However, ML models often unreliably extrapolate outside their training data. We propose an uncertainty quantification method for ML models (and generally for other nonlinear models) with parameters trained by least squares regression. The uncertainty measure is based on the multiparameter delta method from statistics, which gives the standard error of the prediction. The uncertainty measure requires the gradient of the model prediction and the Hessian of the loss function, both with respect to model parameters. Both the gradient and Hessian can be readily obtained from most ML software frameworks by automatic differentiation. We show that the uncertainty measure is larger for input space regions that are not part of the training data. Therefore this method can be used to identify extrapolation and to aid in selecting training data or assessing model reliability.

scholarly journals A New Least Squares Support Vector Machines Ensemble Model for Aero Engine Performance Parameter Chaotic Prediction

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Dangdang Du ◽  
Xiaoliang Jia ◽  
Chaobo Hao
Keyword(s):  
Particle Swarm Optimization ◽  
Phase Space ◽  
Least Squares ◽  
Particle Swarm ◽  
Engine Performance ◽  
Phase Space Reconstruction ◽  
Support Vector ◽  
Ensemble Model ◽  
Swarm Optimization ◽  
Aero Engine

Aiming at the nonlinearity, chaos, and small-sample of aero engine performance parameters data, a new ensemble model, named the least squares support vector machine (LSSVM) ensemble model with phase space reconstruction (PSR) and particle swarm optimization (PSO), is presented. First, to guarantee the diversity of individual members, different single kernel LSSVMs are selected as base predictors, and they also output the primary prediction results independently. Then, all the primary prediction results are integrated to produce the most appropriate prediction results by another particular LSSVM—a multiple kernel LSSVM, which reduces the dependence of modeling accuracy on kernel function and parameters. Phase space reconstruction theory is applied to extract the chaotic characteristic of input data source and reconstruct the data sample, and particle swarm optimization algorithm is used to obtain the best LSSVM individual members. A case study is employed to verify the effectiveness of presented model with real operation data of aero engine. The results show that prediction accuracy of the proposed model improves obviously compared with other three models.

scholarly journals Machine Learning in Least-Squares Monte Carlo Proxy Modeling of Life Insurance Companies

Risks ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 21 ◽  
Author(s):  
Anne-Sophie Krah ◽  
Zoran Nikolić ◽  
Ralf Korn
Keyword(s):  
Machine Learning ◽  
Monte Carlo ◽  
Least Squares ◽  
Life Insurance ◽  
Capital Requirements ◽  
Multivariate Adaptive Regression Splines ◽  
Insurance Companies ◽  
Least Squares Regression ◽  
Least Squares Monte Carlo ◽  
Proxy Modeling

Under the Solvency II regime, life insurance companies are asked to derive their solvency capital requirements from the full loss distributions over the coming year. Since the industry is currently far from being endowed with sufficient computational capacities to fully simulate these distributions, the insurers have to rely on suitable approximation techniques such as the least-squares Monte Carlo (LSMC) method. The key idea of LSMC is to run only a few wisely selected simulations and to process their output further to obtain a risk-dependent proxy function of the loss. In this paper, we present and analyze various adaptive machine learning approaches that can take over the proxy modeling task. The studied approaches range from ordinary and generalized least-squares regression variants over generalized linear model (GLM) and generalized additive model (GAM) methods to multivariate adaptive regression splines (MARS) and kernel regression routines. We justify the combinability of their regression ingredients in a theoretical discourse. Further, we illustrate the approaches in slightly disguised real-world experiments and perform comprehensive out-of-sample tests.

Use of reflectance spectroscopy to estimate the organic carbon and CaCO3 contents of soils

2012 ◽  
Vol 61 (2) ◽  
pp. 277-290 ◽  
Author(s):  
Ádám Csorba ◽  
Vince Láng ◽  
László Fenyvesi ◽  
Erika Michéli
Keyword(s):  
Organic Carbon ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Mean Squared Error ◽  
Reflectance Spectroscopy ◽  
Least Squares Regression ◽  
Root Mean Squared Error ◽  
Squared Error

Napjainkban egyre nagyobb igény mutatkozik olyan technológiák és módszerek kidolgozására és alkalmazására, melyek lehetővé teszik a gyors, költséghatékony és környezetbarát talajadat-felvételezést és kiértékelést. Ezeknek az igényeknek felel meg a reflektancia spektroszkópia, mely az elektromágneses spektrum látható (VIS) és közeli infravörös (NIR) tartományában (350–2500 nm) végzett reflektancia-mérésekre épül. Figyelembe véve, hogy a talajokról felvett reflektancia spektrum információban nagyon gazdag, és a vizsgált tartományban számos talajalkotó rendelkezik karakterisztikus spektrális „ujjlenyomattal”, egyetlen görbéből lehetővé válik nagyszámú, kulcsfontosságú talajparaméter egyidejű meghatározása. Dolgozatunkban, a reflektancia spektroszkópia alapjaira helyezett, a talajok ösz-szetételének meghatározását célzó módszertani fejlesztés első lépéseit mutatjuk be. Munkánk során talajok szervesszén- és CaCO3-tartalmának megbecslését lehetővé tévő többváltozós matematikai-statisztikai módszerekre (részleges legkisebb négyzetek módszere, partial least squares regression – PLSR) épülő prediktív modellek létrehozását és tesztelését végeztük el. A létrehozott modellek tesztelése során megállapítottuk, hogy az eljárás mindkét talajparaméter esetében magas R2értéket [R2(szerves szén) = 0,815; R2(CaCO3) = 0,907] adott. A becslés pontosságát jelző közepes négyzetes eltérés (root mean squared error – RMSE) érték mindkét paraméter esetében közepesnek mondható [RMSE (szerves szén) = 0,467; RMSE (CaCO3) = 3,508], mely a reflektancia mérési előírások standardizálásával jelentősen javítható. Vizsgálataink alapján arra a következtetésre jutottunk, hogy a reflektancia spektroszkópia és a többváltozós kemometriai eljárások együttes alkalmazásával, gyors és költséghatékony adatfelvételezési és -értékelési módszerhez juthatunk.

Speech Emotion Recognition Based on Sparse Representation

2013 ◽  
Vol 38 (4) ◽  
pp. 465-470 ◽  
Author(s):  
Jingjie Yan ◽  
Xiaolan Wang ◽  
Weiyi Gu ◽  
LiLi Ma
Keyword(s):  
Dimensionality Reduction ◽  
Emotion Recognition ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Speech Emotion Recognition ◽  
Least Squares Regression ◽  
Computer Science Pedagogy ◽  
Reduction Methods ◽  
Analysis Computer

Abstract Speech emotion recognition is deemed to be a meaningful and intractable issue among a number of do- mains comprising sentiment analysis, computer science, pedagogy, and so on. In this study, we investigate speech emotion recognition based on sparse partial least squares regression (SPLSR) approach in depth. We make use of the sparse partial least squares regression method to implement the feature selection and dimensionality reduction on the whole acquired speech emotion features. By the means of exploiting the SPLSR method, the component parts of those redundant and meaningless speech emotion features are lessened to zero while those serviceable and informative speech emotion features are maintained and selected to the following classification step. A number of tests on Berlin database reveal that the recogni- tion rate of the SPLSR method can reach up to 79.23% and is superior to other compared dimensionality reduction methods.

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