scholarly journals Fault identification for chiller sensor based on partial least square method

2021 ◽  
Vol 233 ◽  
pp. 03057
Author(s):  
Bang Wu ◽  
Yunpeng Hu ◽  
Chuanhui Zhou ◽  
Guaiguai Chen ◽  
Guannan Li
Keyword(s):  
Principal Component Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Principal Component ◽  
Component Analysis ◽  
Fault Analysis ◽  
Partial Least Square ◽  
Least Square ◽  
Fault Identification ◽  
Least Squares Regression

Sensor failures can lead to an imbalance in heating, ventilation and air conditioning (HVAC) control systems and increase energy consumption. The partial least squares algorithm is a multivariate statistical method, compared with the principal component analysis, its compression factor score contains more original data characteristic information, therefore, partial least squares have greater potential for fault diagnosis than the principal component analysis. However, there are few studies based on partial least squares in the field of HVAC. In order to introduce partial least squares into the field, based on the partial least squares fault detection theory, a fault analysis method suitable for this field is proposed, and the RP1403 data published by ASHARE was used to verify this method. The results show that on the basis of selecting the appropriate number of principal components, partial least squares have the ability to diagnose the fault of the chiller sensor. With the known fault source, partial least squares regression, a method with better data reconstruction accuracy than principal component analysis, is used to repair the fault. Finally, the purpose of fault identification can be achieved.

scholarly journals Modelos PCA a partir de conjuntos de datos con información faltante. ¿Se afectan sus propiedades?

SaberEs ◽  
2010 ◽  
Author(s):  
María Susana Vitelleschi ◽  
Directora: Marta Beatriz Quaglino
Keyword(s):  
Principal Component Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Expectation Maximization ◽  
Principal Component ◽  
Component Analysis

En este trabajo se aborda la problemática de la construcción de modelos PCA (Principal Component Analysis) a partir de conjuntos de datos con información faltante. Se trabaja sobre tres situaciones diferentes con relación a la matriz de datos originales. En cada situación se generaron pérdidas a través de mecanismos aleatorios y no aleatorios, en diferentes porcentajes en una sola variable por vez, seleccionada mediante dos criterios: la que más contribuye y menos contribuye en la formación de la primera componente principal. A partir de cada conjunto de datos incompletos se construye el modelo PCA utilizando: Casos Completos, Nonlinear Iterative Partial Least Squares (NIPALS) y Expectation Maximization (EM). Se comparan los resultados con los obtenidos a través del conjunto de datos originales. Se definen una serie de medidas para estudiar cómo se afectan los resultados según la dimensión de la matriz de datos, el porcentaje y el mecanismo de pérdida, con relación a: bondad del ajuste, bondad de predicción, vectores cargas, ortonormalidad de la matriz de cargas y ortogonalidad de la matriz de “scores”.

scholarly journals Analisis Partial Least Square Menghasilkan Output Alternatif Antarmuka Tambahan Dari Kansei Engineering

2020 ◽  
Vol 17 (2) ◽  
pp. 67
Author(s):  
Arief Ginanjar ◽  
Awan Setiawan
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Correlation Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Partial Least Square ◽  
Least Square ◽  
Type I ◽  
Kansei Engineering ◽  
Coefficient Corrélation

Ketika menggunakan Kansei Engineering dalam mencari kandidat terbaik untuk menentukan model perancangan antarmuka website, peneliti menggunakan metode analisis Partial Least Square (PLS) yang dilakukan secara berulang hingga ditemukan elemen terbaik yang dapat diimplementasikan. PLS sebagai alat bantu untuk menentukan nilai terbaik antara elemen website. Output perbandingan yang dihasilkan akan dikelompokkan berdasarkan Kansei Word sebagaimana yang telah ditentukan dalam rencana awal implementasi Kansei Engineering, output perbandingan PLS iterasi pertama mempunyai kemungkinan mendapatkan nilai usulan terbaik jika digabung dengan melakukan iterasi kedua terhadap asimilasi dua atau tiga elemen yang mempunyai nilai tertinggi. Metodologi yang digunakan mengacu kepada Kansei Engineering Type I dengan melalui pengolahan data menggunakan Cronbach’s Alpha untuk menguji kelayakan responden, kemudian untuk mengetahui hubungan Kansei Words dapat menggunakan Coefficient Correlation Analysis (CCA), sedangkan hubungan antara Kansei Words dengan spesimen dapat menggunakan Principal Component Analysis (PCA), sedangkan mencari pengaruh Kansei Words paling kuat dapat menggunakan Factor Analysis (FA) dan analisis Partial Least Square (PLS) namun harus dilakukan iterasi proses PLS hingga variabel rekomendasi model perancangan antarmuka yang dihasilkan menjadi lebih bervariatif.

Comparison and Discrimination of Artemisia argyi and Artemisia lavandulifolia by Gas Chromatography-Mass Spectrometry-Based Metabolomic Approach

2019 ◽  
Vol 102 (6) ◽  
pp. 1814-1821 ◽  
Author(s):  
Long Guo ◽  
Dan Zhang ◽  
Lei Wang ◽  
Zijing Xue ◽  
Mei Guo ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Least Squares ◽  
Volatile Compounds ◽  
Partial Least Squares ◽  
Herbal Medicines ◽  
Principal Component ◽  
Component Analysis ◽  
Chemical Markers ◽  
Artemisia Argyi ◽  
Metabolomic Approach

Abstract Background: Artemisia argyi and A. lavandulifolia are two morphologically similar herbal medicines derived from the Artemisia genus. Although the two Artemisia herbs have been used as herbal medicines for a long time, studies on their phytochemicals and bioactive compositions are still limited, and no research has been devoted to compare the volatile compounds in A. argyi and A. lavandulifolia. Objective: To compare the volatile constituents in A. argyi and A. lavandulifolia and to explore chemical markers for discrimination and quality evaluation of the two Artemisia herbal medicines. Methods: A GC-MS-based metabolomic approach was employed to compare and discriminate A. argyi and A. lavandulifolia from the aspect of volatile compounds. Multivariate statistical methods, including principal component analysis and orthogonal partial least-squares discriminate analysis, were applied to explore chemical markers for discrimination of the two Artemisia herbal medicines. Results: Thirty volatile compounds were identified, and the chemical profiles of volatile compounds in A. argyi and A. lavandulifolia were quite similar. Principal component analysis and orthogonal partial least-squares discrimination analysis results indicated that the two Artemisia herbal medicines could be distinguished effectively from each other. Ten volatile compounds were selected as potential chemical markers for discrimination of the two Artemisia herbal medicines. Conclusions: The GC-MS-based metabolomics could be an acceptable strategy for comparison and discrimination of A. argyi and A. lavandulifolia as well as authentication of herbal medicines derived from other closely related species. Highlights: GC-MS based metabolomic approach was firstly applied to compare and discriminate Artemisia argyi and Artemisia lavandulifolia.

A Consensus Partial Least Squares Regression for Analysis of Near-Infrared Spectroscopy

2013 ◽  
Vol 765-767 ◽  
pp. 528-531
Author(s):  
Dan Peng ◽  
Qing Chen Nie
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Least Squares Regression ◽  
Prediction Ability ◽  
Successful Model ◽  
Consensus Strategy

To improve the prediction performance of partial least square regression algorithm (PLS), the consensus strategy was applied to develop the multivariate regression model using near-infrared (NIR) spectra and named as C-PLS. Coupled with the consensus strategy, this algorithm can take the advantage of reducing dependence on single model to obtain prediction precision and stability by randomly changing the calibration set. Through an optimization of the parameters involved in the model including criterion threshold and number of sub-models, a successful model was achieved by effectively combining many sub-models with different accuracy and diversity together. To validate the C-PLS algorithm, it was applied to measure the original extract concentration of beer using NIR spectra. The experimental results showed that the prediction ability and robustness of model obtained in subsequent partial least squares calibration using consensus strategy was superior to that obtained using conventional PLS algorithm, and the root mean square error of prediction can improve by up to 45.2%, indicating that it is an efficient tool for NIR spectra regression.

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