Diagnosis of a battery energy storage system based on principal component analysis

Banguero, Edison; Correcher, A.; Pérez‐Navarro, A.; García, Emilio José; Aristizábal, Andrés Julián

doi:10.1016/j.renene.2019.08.064

Cited by 47 publications

(16 citation statements)

References 39 publications

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“…The second category is objective weighting method, such as principal component analysis, Entropy method, TOPSIS method, etc. Banguero, E. et al (2020) used principal component analysis (PCA) to diagnose the health status of a battery energy storage system, established a PCA model and determined the causes that affect it. Liu, F. et al (2017) established a large commercial building fire risk evaluation system, calculated the weight of each index by the Entropy method, and finally calculated the fire risk rating value of the evaluation system.…”

Section: Classification Methods Of Production System Equipment Importamentioning

confidence: 99%

Classification method of production system equipment importance based on grey correlation interval AHP-Entropy method

Wang

Luo

et al. 2020

JAMDSM

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In order to better implement maintenance strategies according to equipment levels, this paper proposes a method based on the grey correlation interval Analytic hierarchy process(AHP)-Entropy method to divide the importance of production system equipment. First, a three-level importance evaluation index system for the production system equipment is established considering the characteristics of the production system in five aspects of operating rate, reliability, maintainability, economy and detectability. Next, the interval number is combined with the AHP and the Entropy method to obtain the interval AHP and the interval Entropy method, and the two methods are combined into an interval AHP-Entropy method to determine the combined weight of each evaluation index. Combined with the evaluated equipment, the grey correlation comprehensive evaluation analysis is performed, the grey correlation degree is calculated and ranked, and the equipment is finally divided by the ABC classification method. Finally, the method is applied to the equipment division of the actual production system. Compared with the interval AHP and interval Entropy method, the evaluation difference rates are reduced by 20% and 15%, respectively, which verifies the effectiveness of the method and provides an accurate and scientific method for equipment division.

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Section: Classification Methods Of Production System Equipment Importamentioning

confidence: 99%

Classification method of production system equipment importance based on grey correlation interval AHP-Entropy method

Wang

Luo

et al. 2020

JAMDSM

View full text Add to dashboard Cite

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“…Its core idea is to map the n-dimensional features to k-dimensional space [26]. e two orthogonal features of this dimension are called principal components, which are built on the basis of the original dimensional features [27]. In order to keep the information of the original data as much as possible in the new dataset, the PCA algorithm needs to find a set of dimensional basis vectors, so that the new data points generated when a feature of the original data is projected on the basis vector can be scattered as far as possible, that is, they have a large variance [28].…”

Section: Signal Feature Extraction and Optimizationmentioning

confidence: 99%

Compound Fault Diagnosis of Stator Interturn Short Circuit and Air Gap Eccentricity Based on Random Forest and XGBoost

Tian

Chen

Dong

2021

Mathematical Problems in Engineering

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Taking the traction motor of CRH2 high-speed train as the research object, this paper proposes a diagnosis method based on random forest and XGBoost for the compound fault resulting from stator interturn short circuit and air gap eccentricity. First, the U-phase and V-phase currents are used as fault diagnosis signal and then the Savitzky–Golay filtering method is used for the noise deduction from the signal. Second, the wavelet packet decomposition is used to extract the composite fault features and then the high-dimensional features are optimized by the principal component analysis (PCA) method. Finally, the random forest and XGBoost are combined to detect composite faults. Using the experimental data of CRH2 semiphysical simulation platform, the diagnosis of different fault modes is completed, and the high diagnosis accuracy is achieved, which verifies the validity of this method.

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“…Secondly, dimensionality reduction reserves the most contributing features of high-dimensional data, removing noise and inconsequential features, thereby achieving the goal of improving data processing speed. Principal component analysis (PCA) [35] is a widely used method of dimensionality reduction of high-dimensional data while minimizing information loss [36]. Suppose the data set is matrix X = [x 1 , x 2 , x 3 , · · · , x n ], then the process of centralizing the data set matrix would be:…”

Section: Classification Of Reconstructed Xasmentioning

confidence: 99%

Effect of Reconstruction Algorithm on the Identification of 3D Printing Polymers Based on Hyperspectral CT Technology Combined with Artificial Neural Network

Zheng

Wang

et al. 2020

Materials

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Hyperspectral X-ray CT (HXCT) technology provides not only structural imaging but also the information of material components therein. The main purpose of this study is to investigate the effect of various reconstruction algorithms on reconstructed X-ray absorption spectra (XAS) of components shown in the CT image by means of HXCT. In this paper, taking 3D printing polymer as an example, seven kinds of commonly used polymers such as thermoplastic elastomer (TPE), carbon fiber reinforced polyamide (PA-CF), acrylonitrile butadiene styrene (ABS), polylactic acid (PLA), ultraviolet photosensitive resin (UV9400), polyethylene terephthalate glycol (PETG), and polyvinyl alcohol (PVA) were selected as samples for hyperspectral CT reconstruction experiments. Seven kinds of 3D printing polymer and two interfering samples were divided into a training set and test sets. First, structural images of specimens were reconstructed by Filtered Back-Projection (FBP), Algebra Reconstruction Technique (ART) and Maximum-Likelihood Expectation-Maximization (ML-EM). Secondly, reconstructed XAS were extracted from the pixels of region of interest (ROI) compartmentalized in the images. Thirdly, the results of principal component analysis (PCA) demonstrated that the first four principal components contain the main features of reconstructed XAS, so we adopted Artificial Neural Network (ANN) trained by the reconstructed XAS expressed by the first four principal components in the training set to identify that the XAS of corresponding polymers exist in both of test sets from the training set. The result of ANN displays that FBP has the best performance of classification, whose ten-fold cross-validation accuracy reached 99%. It suggests that hyperspectral CT reconstruction is a promising way of getting image features and material features at the same time, which can be used in medical imaging and nondestructive testing.

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Diagnosis of a battery energy storage system based on principal component analysis

Cited by 47 publications

References 39 publications

Classification method of production system equipment importance based on grey correlation interval AHP-Entropy method

Classification method of production system equipment importance based on grey correlation interval AHP-Entropy method

Compound Fault Diagnosis of Stator Interturn Short Circuit and Air Gap Eccentricity Based on Random Forest and XGBoost

Effect of Reconstruction Algorithm on the Identification of 3D Printing Polymers Based on Hyperspectral CT Technology Combined with Artificial Neural Network

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