scholarly journals Research on Prediction Method of Hydraulic Pump Remaining Useful Life Based on KPCA and JITL

2021 ◽  
Vol 11 (20) ◽  
pp. 9389
Author(s):  
Zhenbao Li ◽  
Wanlu Jiang ◽  
Sheng Zhang ◽  
Decai Xue ◽  
Shuqing Zhang
Keyword(s):  
Nearest Neighbor ◽  
Prediction Method ◽  
Principal Component ◽  
Remaining Useful Life ◽  
Kernel Principal Component Analysis ◽  
Gear Pump ◽  
K Nearest Neighbor ◽  
Multiple Characteristic ◽  
Gear Pumps ◽  
Useful Life

Hydraulic pumps are commonly used; however, it is difficult to predict their remaining useful life (RUL) effectively. A new method based on kernel principal component analysis (KPCA) and the just-in-time learning (JITL) method was proposed to solve this problem. First, as the research object, the non-substitute time tac-tail life experiment pressure signals of gear pumps were collected. Following the removal and denoising of the DC component of the pressure signals by the wavelet packet method, multiple characteristic indices were extracted. Subsequently, the KPCA method was used to calculate the weighted fusion of the selected feature indices. Then the state evaluation indices were extracted to characterize the performance degradation of the gear pumps. Finally, an RUL prediction method based on the k-vector nearest neighbor (k-VNN) and JITL methods was proposed. The k-VNN method refers to both the Euclidean distance and angle relationship between two vectors as the basis for modeling. The prediction results verified the feasibility and effectiveness of the proposed method. Compared to the traditional JITL RUL prediction method based on the k-nearest neighbor algorithm, the proposed prediction model of the RUL of a gear pump presents a higher prediction accuracy. The method proposed in this paper is expected to be applied to the RUL prediction and condition monitoring and has broad application prospects and wide applicability.

Two-parameter KNN algorithm and its application in recognition of brand rice

2021 ◽  
pp. 1-7
Author(s):  
Zhu Siyu ◽  
He Chongnan ◽  
Song Mingjuan ◽  
Li Linna
Keyword(s):  
Near Infrared ◽  
Nearest Neighbor ◽  
Recognition Accuracy ◽  
Principal Component ◽  
Kernel Principal Component Analysis ◽  
K Nearest Neighbor ◽  
Fisher Discriminant Analysis ◽  
Pattern Class ◽  
Fisher Discriminant ◽  
Two Parameter

In response to the frequent counterfeiting of Wuchang rice in the market, an effective method to identify brand rice is proposed. Taking the near-infrared spectroscopy data of a total of 373 grains of rice from the four origins (Wuchang, Shangzhi, Yanshou, and Fangzheng) as the observations, kernel principal component analysis(KPCA) was employed to reduce the dimensionality, and Fisher discriminant analysis(FDA) and k-nearest neighbor algorithm (KNN) were used to identify brand rice respectively. The effects of the two recognition methods are very good, and that of KNN is relatively better. Howerver the shortcomings of KNN are obvious. For instance, it has only one test dimension and its test of samples is not delicate enough. In order to further improve the recognition accuracy, fuzzy k-nearest neighbor set is defined and fuzzy probability theory is employed to get a new recognition method –Two-Parameter KNN discrimination method. Compared with KNN algorithm, this method increases the examination dimension. It not only examines the proportion of the number of samples in each pattern class in the k-nearest neighbor set, but also examines the degree of similarity between the center of each pattern class and the sample to be identified. Therefore, the recognition process is more delicate and the recognition accuracy is higher. In the identification of brand rice, the discriminant accuracy of Two-Parameter KNN algorithm is significantly higher than that of FDA and that of KNN algorithm.

scholarly journals Remaining useful life prediction for power-shift steering transmission based on fusion of multiple oil spectra

2018 ◽  
Vol 10 (6) ◽  
pp. 168781401878420 ◽  
Author(s):  
Shu-Fa Yan ◽  
Biao Ma ◽  
Chang-Song Zheng
Keyword(s):  
Life Prediction ◽  
Health Management ◽  
Principal Component ◽  
Remaining Useful Life ◽  
Cumulative Distribution ◽  
Kernel Principal Component Analysis ◽  
Degradation Index ◽  
Life Estimation ◽  
Power Shift ◽  
Useful Life

Remaining useful life prediction is a critical issue to fault diagnosis and health management of power-shift steering transmission. Power-shift steering transmission wear, which leads to the increase of wear particles and severe wear afterwards, is a slow degradation process, which can be monitored by oil spectral analysis, but the actual degree of the power-shift steering transmission degradation is often difficult to evaluate. The main purpose of this article is to provide a more accurate remaining useful life prediction methodology for power-shift steering transmission compared to relying solely on an individual spectral oil data. Our methodology includes multiple degradation data fusion, degradation index construction, degradation modelling and remaining useful life estimation procedures. First, the robust kernel principal component analysis is used to reduce the data dimension, and the state space model is utilized to construct the wear degradation index. Then, the Wiener process–based degradation model is established based on the constructed degradation index, and the explicit formulas for several important quantities for remaining useful life estimation such as the probability density function and cumulative distribution function are derived. Finally, a case study is presented to demonstrate the applicability of the proposed methodology. The results show that the proposed remaining useful life prediction methodology can objectively describe the power-shift steering transmission degradation law, and the predicted remaining useful life has been extended as 65 Mh (38.2%) compared with specified maintenance interval. This will reduce the maintenance times of power-shift steering transmission life cycle and finally save the maintenance costs.

Safety Monitoring Information System of Railway Tunnel Construction Based on KNN

2013 ◽  
Vol 303-306 ◽  
pp. 815-818
Author(s):  
Ning Suo ◽  
Hui Lin Wang
Keyword(s):  
Principal Component Analysis ◽  
Data Analysis ◽  
Nearest Neighbor ◽  
Principal Component ◽  
Safety Monitoring ◽  
Kernel Principal Component Analysis ◽  
K Nearest Neighbor ◽  
Railway Tunnel ◽  
Neighbor Classification ◽  
Monitoring Information

This paper presents a novel approach for railway tunnel deformation data analysis in Safety Monitoring Information System. The proposed work introduces a nonlinear machine learning method, Kernel Principal Component Analysis (KPCA), and K nearest neighbor classification (KNN) classifier for railway tunnel deformation data analysis. Kernel Principal Component Analysis (KPCA) is first applied to 1-dimension signals derived from a sequence of silhouette images to reduce its dimensionality. Then, we performed K nearest neighbor classification (KNN) for railway tunnel deformation data analysis. The experimental results show the KNN based railway tunnel deformation data analysis algorithm is better than that based on KPCA.

scholarly journals Comparison of Some Non-Parametric Quality Control Methods

2021 ◽  
Vol 27 (130) ◽  
pp. 197-209
Author(s):  
Hiba Mustafa Fawzi ◽  
Asmaa Ghalib Jaber
Keyword(s):  
Quality Control ◽  
Control Charts ◽  
Nearest Neighbor ◽  
Principal Component ◽  
Kernel Principal Component Analysis ◽  
Control Methods ◽  
K Nearest Neighbor ◽  
Signed Rank ◽  
Control Limits ◽  
Non Parametric

    Multivariate Non-Parametric control charts were used to monitoring the data that generated by using the simulation, whether they are within control limits or not. Since that non-parametric methods do not require any assumptions about the distribution of the data.  This research aims to apply the multivariate non-parametric quality control methods, which are Multivariate Wilcoxon Signed-Rank ( ) , kernel principal component analysis (KPCA) and k-nearest neighbor ( −

Fault Diagnosis of Bearing Based on KPCA and KNN Method

2014 ◽  
Vol 986-987 ◽  
pp. 1491-1496 ◽  
Author(s):  
Qiang Wang ◽  
Yong Bao Liu ◽  
Xing He ◽  
Shu Yong Liu ◽  
Jian Hua Liu
Keyword(s):  
Principal Component Analysis ◽  
Fault Diagnosis ◽  
Nearest Neighbor ◽  
Feature Vector ◽  
Principal Component ◽  
Feature Space ◽  
Component Analysis ◽  
Kernel Functions ◽  
Kernel Principal Component Analysis ◽  
K Nearest Neighbor

Selection of secondary variables is an effective way to reduce redundant information and to improve efficiency in nonlinear system modeling. The combination of Kernel Principal Component Analysis (KPCA) and K-Nearest Neighbor (KNN) is applied to fault diagnosis of bearing. In this approach, the integral operator kernel functions is used to realize the nonlinear map from the raw feature space of vibration signals to high dimensional feature space, and structure and statistics in the feature space to extract the feature vector from the fault signal with the principal component analytic method. Assessment method using the feature vector of the Kernel Principal Component Analysis, and then enter the sensitive features to K-Nearest Neighbor classification. The experimental results indicated that this method has good accuracy.

Hybrid remaining useful life prediction method. A case study on railway D-cables

2021 ◽  
pp. 107746
Author(s):  
Yu Zang ◽  
Wei Shangguan ◽  
Baigen Cai ◽  
Huasheng Wang ◽  
Michael. G. Pecht
Keyword(s):  
Life Prediction ◽  
Prediction Method ◽  
Remaining Useful Life ◽  
Useful Life

scholarly journals Remaining Useful Life Prediction of Broken Rotor Bar Based on Data-Driven and Degradation Model

2021 ◽  
Vol 11 (16) ◽  
pp. 7175
Author(s):  
Islem Bejaoui ◽  
Dario Bruneo ◽  
Maria Gabriella Xibilia
Keyword(s):  
Principal Component Analysis ◽  
Induction Motor ◽  
Life Prediction ◽  
Principal Component ◽  
Component Analysis ◽  
Remaining Useful Life ◽  
Degradation Model ◽  
Broken Rotor Bar ◽  
Industrial Sectors ◽  
Useful Life

Rotating machines such as induction motors are crucial parts of most industrial systems. The prognostic health management of induction motor rotors plays an essential role in increasing electrical machine reliability and safety, especially in critical industrial sectors. This paper presents a new approach for rotating machine fault prognosis under broken rotor bar failure, which involves the modeling of the failure mechanism, the health indicator construction, and the remaining useful life prediction. This approach combines signal processing techniques, inherent metrics, and principal component analysis to monitor the induction motor. Time- and frequency-domains features allowing for tracking the degradation trend of motor critical components that are extracted from torque, stator current, and speed signals. The most meaningful features are selected using inherent metrics, while two health indicators representing the degradation process of the broken rotor bar are constructed by applying the principal component analysis. The estimation of the remaining useful life is then obtained using the degradation model. The performance of the prediction results is evaluated using several criteria of prediction accuracy. A set of synthetic data collected from a degraded Simulink model of the rotor through simulations is used to validate the proposed approach. Experimental results show that using the developed prognostic methodology is a powerful strategy to improve the prognostic of induction motor degradation.

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