scholarly journals Research on Feature Extraction of Performance Degradation for Flexible Material R2R Processing Roller Based on PCA

2020 ◽  
Vol 2020 ◽  
pp. 1-23
Author(s):  
Yaohua Deng ◽  
Huiqiao Zhou ◽  
Kexing Yao ◽  
Zhiqi Huang ◽  
Chengwang Guo
Keyword(s):  
Principal Component ◽  
Vibration Signal ◽  
Performance Degradation ◽  
Mean Square ◽  
Feature Parameters ◽  
Roll To Roll ◽  
Flexible Material ◽  
Parameter Matrix ◽  
Feature Parameter ◽  
Original Feature

Performance feature extraction is the primary problem in equipment performance degradation assessment. To handle the problem of high-dimensional performance characterization and complexity of calculating the performance indicators in flexible material roll-to-roll processing, this paper proposes a PCA method for extracting the degradation characteristic of roll shaft. Based on the analysis of the performance influencing factors of flexible material roll-to-roll processing roller, a principal component analysis extraction model was constructed. The original feature parameter matrix composed of 10-dimensional feature parameters such as time domain, frequency domain, and time-frequency domain vibration signal of the roll shaft was established; then, we obtained a new feature parameter matrix Z org ∗ by normalizing the original feature parameter matrix. The correlation measure between every two parameters in the matrix Z org ∗ was used as the eigenvalue to establish the covariance matrix of the performance degradation feature parameters. The Jacobi iteration method was introduced to derive the algorithm for solving eigenvalue and eigenvector of the covariance matrix. Finally, using the eigenvalue cumulative contribution rate as the screening rule, we linearly weighted and fused the eigenvectors and derived the feature principal component matrix F of the processing roller vibration signal. Experiments showed that the initially obtained, 10-dimensional features of the processing rollers’ vibration signals, such as average, root mean square, kurtosis index, centroid frequency, root mean square of frequency, standard deviation of frequency, and energy of the intrinsic mode function component, can be expressed by 3-dimensional principal components F 1 , F 2 , and F 3 . The vibration signal features reduction dimension was realized, and F 1 , F 2 , and F 3 contain 98.9% of the original vibration signal data, further illustrating that the method has high precision in feature parameters’ extraction and the advantage of eliminating the correlation between feature parameters and reducing the workload selecting feature parameters.

scholarly journals Study on Mutual Information and Fractal Dimension-Based Unsupervised Feature Parameters Selection: Application in UAVs

Entropy ◽  
2018 ◽  
Vol 20 (9) ◽  
pp. 674 ◽  
Author(s):  
Xiaohong Wang ◽  
Yidi He ◽  
Lizhi Wang
Keyword(s):  
Fractal Dimension ◽  
Mutual Information ◽  
Information Fusion ◽  
Motor Performance ◽  
Learning Algorithm ◽  
Subspace Learning ◽  
Performance Degradation ◽  
Feature Parameters ◽  
Parameters Selection ◽  
Feature Parameter

In this study, due to the redundant and irrelevant features contained in the multi-dimensional feature parameter set, the information fusion performance of the subspace learning algorithm was reduced. To solve the above problem, a mutual information (MI) and fractal dimension-based unsupervised feature parameters selection method was proposed. The key to this method was the importance ordering algorithm based on the comprehensive consideration of the relevance and redundancy of features, and then the method of fractal dimension-based feature parameter subset evaluation criterion was adopted to obtain the optimal feature parameter subset. To verify the validity of the proposed method, a brushless direct current (DC) motor performance degradation test was designed. Vibrational sample data during motor performance degradation was used as the data source, and motor health-fault diagnosis capacity and motor state prediction effect ware evaluation indexes to compare the information fusion performance of the subspace learning algorithm before and after the use of the proposed method. According to the comparison result, the proposed method is able to eliminate highly-redundant parameters that are less correlated to feature parameters, thereby enhancing the information fusion performance of the subspace learning algorithm.

scholarly journals Prediction of shallow bit position based on vibration signal monitoring of bit broken rock

2021 ◽  
Vol 17 (1) ◽  
pp. 155014772199170
Author(s):  
Jinping Yu ◽  
Deyong Zou
Keyword(s):  
Acoustic Emission ◽  
Time Domain ◽  
Vibration Signal ◽  
Rock Breaking ◽  
Drill String ◽  
Mean Square ◽  
Drilling Force ◽  
Emission Signal ◽  
Force Signal ◽  
The Mean

The speed of drilling has a great relationship with the rock breaking efficiency of the bit. Based on the above background, the purpose of this article is to predict the position of shallow bit based on the vibration signal monitoring of bit broken rock. In this article, first, the mechanical research of drill string is carried out; the basic changes of the main mechanical parameters such as the axial force, torque, and bending moment of drill string are clarified; and the dynamic equilibrium equation theory of drill string system is analyzed. According to the similarity criterion, the corresponding relationship between drilling process parameters and laboratory test conditions is determined. Then, the position monitoring test system of the vibration bit is established. The acoustic emission signal and the drilling force signal of the different positions of the bit in the process of vibration rock breaking are collected synchronously by the acoustic emission sensor and the piezoelectric force sensor. Then, the denoised acoustic emission signal and drilling force signal are analyzed and processed. The mean value, variance, and mean square value of the signal are calculated in the time domain. The power spectrum of the signal is analyzed in the frequency domain. The signal is decomposed by wavelet in the time and frequency domains, and the wavelet energy coefficients of each frequency band are extracted. Through the wavelet energy coefficient calculated by the model, combined with the mean, variance, and mean square error of time-domain signal, the position of shallow buried bit can be analyzed and predicted. Finally, by fitting the results of indoor experiment and simulation experiment, it can be seen that the stress–strain curve of rock failure is basically the same, and the error is about 3.5%, which verifies the accuracy of the model.

scholarly journals Identification of Pilots’ Fatigue Status Based on Electrocardiogram Signals

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3003
Author(s):  
Ting Pan ◽  
Haibo Wang ◽  
Haiqing Si ◽  
Yao Li ◽  
Lei Shang
Keyword(s):  
Back Propagation ◽  
Principal Component ◽  
Back Propagation Neural Network ◽  
Practical Significance ◽  
Support Vector ◽  
State Identification ◽  
Feature Parameter ◽  
The Time Domain ◽  
Pilot Fatigue ◽  
Identification Model

Fatigue is an important factor affecting modern flight safety. It can easily lead to a decline in pilots’ operational ability, misjudgments, and flight illusions. Moreover, it can even trigger serious flight accidents. In this paper, a wearable wireless physiological device was used to obtain pilots’ electrocardiogram (ECG) data in a simulated flight experiment, and 1440 effective samples were determined. The Friedman test was adopted to select the characteristic indexes that reflect the fatigue state of the pilot from the time domain, frequency domain, and non-linear characteristics of the effective samples. Furthermore, the variation rules of the characteristic indexes were analyzed. Principal component analysis (PCA) was utilized to extract the features of the selected feature indexes, and the feature parameter set representing the fatigue state of the pilot was established. For the study on pilots’ fatigue state identification, the feature parameter set was used as the input of the learning vector quantization (LVQ) algorithm to train the pilots’ fatigue state identification model. Results show that the recognition accuracy of the LVQ model reached 81.94%, which is 12.84% and 9.02% higher than that of traditional back propagation neural network (BPNN) and support vector machine (SVM) model, respectively. The identification model based on the LVQ established in this paper is suitable for identifying pilots’ fatigue states. This is of great practical significance to reduce flight accidents caused by pilot fatigue, thus providing a theoretical foundation for pilot fatigue risk management and the development of intelligent aircraft autopilot systems.

scholarly journals HKF-SVR Optimized by Krill Herd Algorithm for Coaxial Bearings Performance Degradation Prediction

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 660 ◽  
Author(s):  
Fang Liu ◽  
Liubin Li ◽  
Yongbin Liu ◽  
Zheng Cao ◽  
Hui Yang ◽  
...  
Keyword(s):  
Kernel Method ◽  
Back Propagation ◽  
Vibration Signal ◽  
Back Propagation Neural Network ◽  
Industrial Applications ◽  
Performance Degradation ◽  
Support Vector ◽  
Krill Herd Algorithm ◽  
Krill Herd ◽  
Learning Machine

In real industrial applications, bearings in pairs or even more are often mounted on the same shaft. So the collected vibration signal is actually a mixed signal from multiple bearings. In this study, a method based on Hybrid Kernel Function-Support Vector Regression (HKF–SVR) whose parameters are optimized by Krill Herd (KH) algorithm was introduced for bearing performance degradation prediction in this situation. First, multi-domain statistical features are extracted from the bearing vibration signals and then fused into sensitive features using Kernel Joint Approximate Diagonalization of Eigen-matrices (KJADE) algorithm which is developed recently by our group. Due to the nonlinear mapping capability of the kernel method and the blind source separation ability of the JADE algorithm, the KJADE could extract latent source features that accurately reflecting the performance degradation from the mixed vibration signal. Then, the between-class and within-class scatters (SS) of the health-stage data sample and the current monitored data sample is calculated as the performance degradation index. Second, the parameters of the HKF–SVR are optimized by the KH (Krill Herd) algorithm to obtain the optimal performance degradation prediction model. Finally, the performance degradation trend of the bearing is predicted using the optimized HKF–SVR. Compared with the traditional methods of Back Propagation Neural Network (BPNN), Extreme Learning Machine (ELM) and traditional SVR, the results show that the proposed method has a better performance. The proposed method has a good application prospect in life prediction of coaxial bearings.

Rolling bearing degradation condition clustering using multidimensional degradation feature and Gath–Geva fuzzy clustering algorithm

2020 ◽  
pp. 107754632095495
Author(s):  
Bing Wang ◽  
Xiong Hu ◽  
Tao X Mei ◽  
Sun D Jian ◽  
Wang Wei
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Chaotic Map ◽  
Vibration Signal ◽  
Degradation Process ◽  
Rolling Bearing ◽  
Performance Degradation ◽  
Time Parameter ◽  
Mechanical Equipment ◽  
Time Dimension

In allusion to the issue of rolling bearing degradation feature extraction and degradation condition clustering, a logistic chaotic map is introduced to analyze the advantages of C0 complexity and a technique based on a multidimensional degradation feature and Gath–Geva fuzzy clustering algorithmic is proposed. The multidimensional degradation feature includes C0 complexity, root mean square, and curved time parameter which is more in line with the performance degradation process. Gath–Geva fuzzy clustering is introduced to divide different conditions during the degradation process. A rolling bearing lifetime vibration signal from intelligent maintenance system bearing test center was introduced for instance analysis. The results show that C0 complexity is able to describe the degradation process and has advantages in sensitivity and calculation speed. The introduced degradation indicator curved time parameter can reflect the agglomeration character of the degradation condition at time dimension, which is more in line with the performance degradation pattern of mechanical equipment. The Gath–Geva fuzzy clustering algorithmic is able to cluster degradation condition of mechanical equipment such as bearings accurately.

Performance degradation analysis of solid lubricated bearings based on Laplace wavelet filter and self-organizing map

2013 ◽  
Vol 228 (10) ◽  
pp. 1743-1753 ◽  
Author(s):  
Chao Zhang ◽  
Shaoping Wang
Keyword(s):  
Feature Vector ◽  
Vibration Signal ◽  
Degradation Process ◽  
Performance Degradation ◽  
Friction Torque ◽  
Self Organizing Map ◽  
Wavelet Filter ◽  
Degradation Analysis ◽  
Space Mechanisms ◽  
Self Organizing

Solid lubricated bearings are commonly used in space mechanisms and other appliances, and their reliability analysis has drawn more and more attention. This paper focuses on the performance degradation analysis of solid lubricated bearings. Based on the vibration and friction torque signal of solid lubricated bearings, Laplace wavelet filter is adopted to process vibration signal and feature vector is constructed by calculating time-domain parameters of filtered vibration signal and original friction torque signal. Self-organizing map is then adopted to analyze the performance degradation based on extracted feature vectors. Experimental results show that this method can describe performance degradation process effectively.

Clustering Algorithm for Vehicle’s Driving Data Feature based on Integrated Navigation

2021 ◽  
Vol 13 (4) ◽  
Author(s):  
Na Guo ◽  
Yiyi Zhu
Keyword(s):  
Clustering Algorithm ◽  
Principal Component ◽  
Kernel Principal Component Analysis ◽  
Integrated Navigation ◽  
Clustering Problem ◽  
Incremental Method ◽  
Feature Parameters ◽  
Vehicle Acceleration ◽  
Feature Based ◽  
Clustering Problems

The clustering result of K-means clustering algorithm is affected by the initial clustering center and the clustering result is not always global optimal. Therefore, the clustering analysis of vehicle’s driving data feature based on integrated navigation is carried out based on global K-means clustering algorithm. The vehicle mathematical model based on GPS/DR integrated navigation is constructed and the vehicle’s driving data based on GPS/DR integrated navigation, such as vehicle acceleration, are collected. After extracting the vehicle’s driving data features, the feature parameters of vehicle’s driving data are dimensionally reduced based on kernel principal component analysis to reduce the redundancy of feature parameters. The global K-means clustering algorithm converts clustering problem into a series of sub-cluster clustering problems. At the end of each iteration, an incremental method is used to select the next cluster of optimal initial centers. After determining the optimal clustering number, the feature clustering of vehicle’s driving data is completed. The experimental results show that the global K-means clustering algorithm has a clustering error of only 1.37% for vehicle’s driving data features and achieves high precision clustering for vehicle’s driving data features.

Evaluating the Performance Degradation of Centrifugal Pumps Using the Principal Component Analysis

2021 ◽  
Author(s):  
Andrew Eaton ◽  
Wael Ahmed ◽  
Marwan A. Hassan
Keyword(s):  
Principal Component Analysis ◽  
Distribution Systems ◽  
Water Distribution ◽  
Principal Component ◽  
Component Analysis ◽  
Performance Degradation ◽  
Centrifugal Pumps ◽  
Pump Performance ◽  
Hydraulic Machines ◽  
Wide Range

Abstract Centrifugal pumps are used in a variety of engineering applications, such as power production, heating, cooling, and water distribution systems. Although centrifugal pumps are considered to be highly reliable hydraulic machines, they are susceptible to a wide range of damage due to several degradation mechanisms, which make them operate away from their best efficiency range. Therefore, evaluating the energy efficiency and performance degradation of pumps is an important consideration to the operation of these systems. In the present study, the hydraulic performance along with the vibration response of an industrial scale centrifugal pump (7.5KW) subjected to different levels of impeller unbalance were experimentally investigated. Extensive testing of pump performance along with vibration measurements were carried. Both time and frequency domain techniques coupled with principal component analysis (PCA) were used in this evaluation. The effect of unbalance on the pump performance was found to be mainly on the shaft power, while no change in the flow rate and the pump head were observed. As the level of unbalance increased, the power required to operate the pump at the designated speed increased by as much as 12%. The PCA found to be a useful tool in comparing the pump vibrations in the field in order to determine the presence of unbalance as well as the degree of damage. The results of this work can be used to evaluate and monitor pump performance under prescribed degradation in order to enhance preventative maintenance programs.

scholarly journals Sensor Distribution Optimization for Structural Impact Monitoring Based on NSGA-II and Wavelet Decomposition

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4264 ◽  
Author(s):  
Peng Li ◽  
Liuwei Huang ◽  
Jiachao Peng
Keyword(s):  
Wavelet Decomposition ◽  
Principal Component ◽  
Experimental System ◽  
Vibration Signal ◽  
The Finite Element Method ◽  
Nsga Ii ◽  
Impact Monitoring ◽  
Impact Location ◽  
Average Accuracy ◽  
The Impact

Optimal sensor placement is a significant task for structural health monitoring (SHM). In this paper, an SHM system is designed which can recognize the different impact location and impact degree in the composite plate. Firstly, the finite element method is used to simulate the impact, extracting numerical signals of the structure, and the wavelet decomposition is used to extract the band energy. Meanwhile, principal component analysis (PCA) is used to reduce the dimensions of the vibration signal. Following this, the non-dominated sorting genetic algorithm (NSGA-II) is used to optimize the placement of sensors. Finally, the experimental system is established, and the Product-based Neural Network is used to recognize different impact categories. Three sets of experiments are carried out to verify the optimal results. When three sensors are applied, the average accuracy of the impact recognition is 59.14%; when the number of sensors is four, the average accuracy of impact recognition is 76.95%.

A degradation phase division technique based on Gath–Geva fuzzy clustering

2020 ◽  
Vol 26 (15-16) ◽  
pp. 1147-1154
Author(s):  
Bing Wang ◽  
Wang Wei ◽  
Xiong Hu ◽  
Dejian Sun
Keyword(s):  
Fuzzy Clustering ◽  
Chaotic Map ◽  
Vibration Signal ◽  
Degradation Process ◽  
Rolling Bearing ◽  
Performance Degradation ◽  
Mechanical Equipment ◽  
Spectral Entropy ◽  
Variation Pattern ◽  
Intelligent Maintenance

In allusion to the issue of degradation feature extraction and degradation phase division, a logistic chaotic map is used to study the variation pattern of spectral entropy, and a technique based on Gath–Geva fuzzy clustering is proposed. The degradation features include spectral entropy, root mean square, and “curved time,” which are more in line with the performance degradation process than degradation time. Gath–Geva fuzzy clustering is introduced to divide different phases in the degradation process. The rolling bearing lifetime vibration signal from the intelligent maintenance systems (IMS) bearing test center was introduced for instance analysis. The results show that spectral entropy is able to effectively describe the complexity variation pattern in the performance degradation process and has some advantages in sensitivity and calculation speed. The introduced “curved time” is able to reflect the agglomeration character of the degradation condition on a time scale, which is more in line with the performance degradation pattern of mechanical equipment. Gath–Geva fuzzy clustering is able to divide the degradation phase of mechanical equipment such as bearings accurately.

Sign in / Sign up

Export Citation Format

Share Document