Research on Bearing Life Trend Prediction Method Based on Principal Component Analysis and Grey Model

2021 ◽  
Author(s):  
MA Hailong ◽  
Li Zhen
Keyword(s):  
Principal Component Analysis ◽  
Prediction Method ◽  
Principal Component ◽  
Component Analysis ◽  
Grey Model ◽  
Trend Prediction ◽  
Bearing Life

Stormwater inflow prediction using radar rainfall data compressed by principal component analysis

2006 ◽  
Vol 1 (1) ◽  
Author(s):  
K. Katayama ◽  
K. Kimijima ◽  
O. Yamanaka ◽  
A. Nagaiwa ◽  
Y. Ono
Keyword(s):  
Principal Component Analysis ◽  
Prediction Model ◽  
Principal Components ◽  
Prediction Method ◽  
Principal Component ◽  
Component Analysis ◽  
Rainfall Data ◽  
Radar Rainfall ◽  
Input Variables ◽  
Inflow Prediction

This paper proposes a method of stormwater inflow prediction using radar rainfall data as the input of the prediction model constructed by system identification. The aim of the proposal is to construct a compact system by reducing the dimension of the input data. In this paper, Principal Component Analysis (PCA), which is widely used as a statistical method for data analysis and compression, is applied to pre-processing radar rainfall data. Then we evaluate the proposed method using the radar rainfall data and the inflow data acquired in a certain combined sewer system. This study reveals that a few principal components of radar rainfall data can be appropriate as the input variables to storm water inflow prediction model. Consequently, we have established a procedure for the stormwater prediction method using a few principal components of radar rainfall data.

scholarly journals Risk Assessment of Coal Mine Water Inrush Based On PCA-DBN

2021 ◽  
Author(s):  
Zhang ye ◽  
Tang Shoufeng ◽  
Shi Ke
Keyword(s):  
Risk Assessment ◽  
Principal Component Analysis ◽  
Coal Mine ◽  
Water Inrush ◽  
Prediction Method ◽  
Principal Component ◽  
Component Analysis ◽  
Classification Model ◽  
Mine Safety ◽  
Training Time

Abstract To provide an effective risk assessment of water inrush for coal mine safety production, a BP neural network prediction method for water inrush based on principal component analysis and deep confidence network optimization was proposed. Because deep belief network (DBN) is disadvantaged by a long training time when establishing a high-dimensional data classification model, the principal component analysis (PCA) method is used to reduce the dimensionality of many factors affecting the water inrush of the coal seam floor, thus reducing the number of variables of the research object, redundancy and the difficulty of feature extraction and shortening the training time of the model. Then, a DBN network was used to extract secondary features from the processed nonlinear data, and a more abstract high-level representation was formed by combining low-level features to find the expression of the nonlinear relationship between the characteristics of water inbursts. Finally, a prediction model was established to predict the water inrush in coal mines. The superiority of this method was verified by comparing the prediction of the actual working face with the actual situation in typical mining areas of North China.

scholarly journals The CIPCA-BPNN Failure Prediction Method Based on Interval Data Compression and Dimension Reduction

2021 ◽  
Vol 11 (8) ◽  
pp. 3448
Author(s):  
Linchao Yang ◽  
Guozhu Jia ◽  
Fajie Wei ◽  
Wenbing Chang ◽  
Chen Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Data Compression ◽  
Back Propagation ◽  
Prediction Method ◽  
Principal Component ◽  
Interval Data ◽  
Component Analysis ◽  
Fault Prediction ◽  
Flight Data

This paper proposes a complete-information-based principal component analysis (CIPCA)-back-propagation neural network (BPNN)_ fault prediction method using real unmanned aerial vehicle (UAV) flight data. Unmanned aerial vehicles are widely used in commercial and industrial fields. With the development of UAV technology, it is imperative to diagnose and predict UAV faults and improve their safety and reliability. The data-driven fault prediction method provides a basis for UAV fault prediction. A UAV is a typical complex system. Its flight data is a kind of typical high-dimensional large sample dataset, and traditional methods cannot meet the requirements of data compression and dimensionality reduction at the same time. The method used interval data to compress UAV flight data, used CIPCA to reduce the dimensionality of the compressed data, and then used a back propagation (BP) neural network to predict UAV failure. Experimental results show that the CIPCA-BPNN method had obvious advantages over the traditional principal component analysis (PCA)-BPNN method and could accurately predict a failure about 9 s before the UAV failure occurred.

Blade damage prognosis based on kernel principal component analysis and grey model using subsampled tip-timing signals

2014 ◽  
Vol 228 (17) ◽  
pp. 3178-3185 ◽  
Author(s):  
Chen Zhongsheng ◽  
Yang Yongmin ◽  
Guo Bin ◽  
Hu Zheng
Keyword(s):  
Principal Component Analysis ◽  
High Speed ◽  
Damage Index ◽  
Principal Component ◽  
Component Analysis ◽  
Vibration Monitoring ◽  
Grey Model ◽  
Kernel Principal Component Analysis ◽  
Damage Prognosis ◽  
Blade Tip

Damage prognosis of high-speed blades is very important for industrial turbomachinery. Nowadays, vibration monitoring using blade tip-timing methods is becoming promising. However, its main drawback is blade tip-timing signals are subsampled. Very few works have been done on damage prognosis using subsampled blade tip-timing signals. This paper investigates a novel method of blade damage prognosis based on kernel principal component analysis and grey model. Firstly, a nonaliasing reconstruction algorithm of subsampled blade tip-timing signals is proposed based on the Shannon theorem and wavelet packet transform. Secondly, kernel principal component analysis is done on the damage feature space and a damage index is defined by Mahalanobis distance. Then a grey model (1) model is proposed for damage prognosis. In the end, an experimental setup is built and a long time testing is done for collecting samples. The experimental results validate the superiority of the proposed method.

scholarly journals Evidence-Based Statistical Evaluation of Japanese L2-Learners’ Proficiency using Principal Component Analysis

2021 ◽  
Vol 102 ◽  
pp. 01005
Author(s):  
Masafumi Arai ◽  
Hajime Tsubaki ◽  
Yoshinori Sagisaka
Keyword(s):  
Principal Component Analysis ◽  
Prediction Method ◽  
Principal Component ◽  
Objective Evaluation ◽  
Component Analysis ◽  
Global Scale ◽  
L2 Learners ◽  
Scale Scores ◽  
The Common ◽  
Conventional Methods

This paper aims at an automatic evaluation of second language (L2) learners’ proficiencies and tries to analyze English conversation data having 94 statistics and Global Scale scores of the Common European Framework of Reference (CEFR) given to each participant. The CEFR defines Range, Accuracy, Fluency, Interaction and Coherence as 5 subcategories, which constitute the CEFR Global Scale score. The statistics were classified into the CEFR’s 5 subcategories. We used the Principal Component Analysis (PCA), an unsupervised machine learning method, on each subcategory and obtained the participants’ principal component scores (PC scores) of the 5 subcategories for estimation parameters. We predicted the participants’ CEFR Global scores using the Multiple Regression Analysis (MRA). The proposed prediction method using the PC scores was compared with conventional methods with the 94 statistics. Based on the coefficients of determination (R2), the value of the proposed method (0.82) was nearly equivalent to one of values obtained by the conventional methods. Meanwhile, as for standard deviation, the proposed method showed the smallest value in the comparison. The results indicated usability of the PCA and PC scores calculated from the CEFR subcategory data for objective evaluation of L2 learners’ English proficiencies.

A German version of the Intermittent Claudication Questionnaire (ICQ): cultural adaptation and validation

VASA ◽  
2012 ◽  
Vol 41 (5) ◽  
pp. 333-342 ◽  
Author(s):  
Kirchberger ◽  
Finger ◽  
Müller-Bühl
Keyword(s):  
Principal Component Analysis ◽  
Intermittent Claudication ◽  
Completion Time ◽  
Short Form ◽  
Principal Component ◽  
Component Analysis ◽  
German Version ◽  
Average Completion Time ◽  
Sf 36 ◽  
Related Quality

Background: The Intermittent Claudication Questionnaire (ICQ) is a short questionnaire for the assessment of health-related quality of life (HRQOL) in patients with intermittent claudication (IC). The objective of this study was to translate the ICQ into German and to investigate the psychometric properties of the German ICQ version in patients with IC. Patients and methods: The original English version was translated using a forward-backward method. The resulting German version was reviewed by the author of the original version and an experienced clinician. Finally, it was tested for clarity with 5 German patients with IC. A sample of 81 patients were administered the German ICQ. The sample consisted of 58.0 % male patients with a median age of 71 years and a median IC duration of 36 months. Test of feasibility included completeness of questionnaires, completion time, and ratings of clarity, length and relevance. Reliability was assessed through a retest in 13 patients at 14 days, and analysis of Cronbach’s alpha for internal consistency. Construct validity was investigated using principal component analysis. Concurrent validity was assessed by correlating the ICQ scores with the Short Form 36 Health Survey (SF-36) as well as clinical measures. Results: The ICQ was completely filled in by 73 subjects (90.1 %) with an average completion time of 6.3 minutes. Cronbach’s alpha coefficient reached 0.75. Intra-class correlation for test-retest reliability was r = 0.88. Principal component analysis resulted in a 3 factor solution. The first factor explained 51.5 of the total variation and all items had loadings of at least 0.65 on it. The ICQ was significantly associated with the SF-36 and treadmill-walking distances whereas no association was found for resting ABPI. Conclusions: The German version of the ICQ demonstrated good feasibility, satisfactory reliability and good validity. Responsiveness should be investigated in further validation studies.

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