The Effect of Manual Wheelchair Propulsion Speed on Users’ Shoulder Muscle Coordination Patterns in Time-Frequency and Principal Component Analysis

Aiming to identify the bearing faults level effectively, a new method based on kernel principal component analysis and particle swarm optimization optimized k-nearest neighbour model is proposed. First, the gathered vibration signals are decomposed by time–frequency domain method, i.e., local mean decomposition; as a result, the product functions decomposed from the original signal are derived. Then, the entropy values of the product functions are calculated by Shannon method, which will work as the input features for k-nearest neighbour model. The kernel principal component analysis model is used to reduce the dimension of the features, and then the k-nearest neighbour model which was optimized by the particle swarm optimization method is used to identify the bearing fault levels. Case of test and actually collected signal are analysed. The results validate the effectiveness of the proposed algorithm.

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Forcing variables in simulation of transpiration of water stressed plants determined by principal component analysis

International Agrophysics ◽

10.1515/intag-2016-0006 ◽

2016 ◽

Vol 30 (4) ◽

pp. 431-445

Author(s):

Angelica Durigon ◽

Quirijn de Jong van Lier ◽

Klaas Metselaar

Keyword(s):

Principal Component Analysis ◽

Principal Component ◽

Component Analysis ◽

Specific Humidity ◽

Leaf Angle ◽

Prediction Errors ◽

Angle Distribution ◽

Time Frequency ◽

Soil Dryness

AbstractTo date, measuring plant transpiration at canopy scale is laborious and its estimation by numerical modelling can be used to assess high time frequency data. When using the model by Jacobs (1994) to simulate transpiration of water stressed plants it needs to be reparametrized. We compare the importance of model variables affecting simulated transpiration of water stressed plants. A systematic literature review was performed to recover existing parameterizations to be tested in the model. Data from a field experiment with common bean under full and deficit irrigation were used to correlate estimations to forcing variables applying principal component analysis. New parameterizations resulted in a moderate reduction of prediction errors and in an increase in model performance. Agsmodel was sensitive to changes in the mesophyll conductance and leaf angle distribution parameterizations, allowing model improvement. Simulated transpiration could be separated in temporal components. Daily, afternoon depression and long-term components for the fully irrigated treatment were more related to atmospheric forcing variables (specific humidity deficit between stomata and air, relative air humidity and canopy temperature). Daily and afternoon depression components for the deficit-irrigated treatment were related to both atmospheric and soil dryness, and long-term component was related to soil dryness.

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Combination of principal component analysis and time-frequency representations of multichannel vibration data for gearbox fault detection

Journal of Vibroengineering ◽

10.21595/jve.2016.17114 ◽

2016 ◽

Vol 18 (4) ◽

pp. 2167-2175 ◽

Cited By ~ 23

Author(s):

Radoslaw Zimroz ◽

Jacek Wodecki ◽

Pawel Stefaniak ◽

Jakub Obuchowski ◽

Agnieszka Wylomanska

Keyword(s):

Principal Component Analysis ◽

Fault Detection ◽

Principal Component ◽

Component Analysis ◽

Time Frequency ◽

Vibration Data

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Myoelectric signal classification based on S transform and two-directional two-dimensional principal component analysis

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331217704035 ◽

2017 ◽

Vol 40 (7) ◽

pp. 2387-2395 ◽

Cited By ~ 1

Author(s):

Yi Ji ◽

Hong-Bo Xie

Keyword(s):

Principal Component Analysis ◽

Feature Extraction ◽

Computational Cost ◽

Principal Component ◽

Component Analysis ◽

Two Dimensional ◽

Time Frequency ◽

Frequency Decomposition ◽

S Transform ◽

Efficiency And Effectiveness

Time-frequency representiation has been intensively employed for the analysis of biomedical signals. In order to extract discriminative information, time-frequency matrix is often transformed into a 1D vector followed by principal component analysis (PCA). This study contributes a two-directional two-dimensional principal component analysis (2D2PCA)-based technique for time-frequency feature extraction. The S transform, integrating the strengths of short time Fourier transform and wavelet transform, is applied to perform the time-frequency decomposition. Then, 2D2PCA is directly conducted on the time-frequency matrix rather than 1D vectors for feature extraction. The proposed method can significantly reduce the computational cost while capture the directions of maximal time-frequency matrix variance. The efficiency and effectiveness of the proposed method is demonstrated by classifying eight hand motions using 4-channel myoelectric signals recorded in health subjects and amputees.

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Rolling Bearing Reliability Assessment via Kernel Principal Component Analysis and Weibull Proportional Hazard Model

Shock and Vibration ◽

10.1155/2017/6184190 ◽

2017 ◽

Vol 2017 ◽

pp. 1-11 ◽

Cited By ~ 6

Author(s):

Fengtao Wang ◽

Xutao Chen ◽

Bosen Dun ◽

Bei Wang ◽

Dawen Yan ◽

...

Keyword(s):

Principal Component Analysis ◽

Frequency Domain ◽

Reliability Assessment ◽

Principal Component ◽

Component Analysis ◽

Performance Degradation ◽

Assessment Model ◽

Kernel Principal Component Analysis ◽

Engineering Project ◽

Time Frequency

Reliability assessment is a critical consideration in equipment engineering project. Successful reliability assessment, which is dependent on selecting features that accurately reflect performance degradation as the inputs of the assessment model, allows for the proactive maintenance of equipment. In this paper, a novel method based on kernel principal component analysis (KPCA) and Weibull proportional hazards model (WPHM) is proposed to assess the reliability of rolling bearings. A high relative feature set is constructed by selecting the effective features through extracting the time domain, frequency domain, and time-frequency domain features over the bearing’s life cycle data. The kernel principal components which can accurately reflect the performance degradation process are obtained by KPCA and then input as the covariates of WPHM to assess the reliability. An example was conducted to validate the proposed method. The differences in manufacturing, installation, and working conditions of the same type of bearings during reliability assessment are reduced after extracting relative features, which enhances the practicability and stability of the proposed method.

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How Do Movement Patterns in Weightlifting (Clean) Change When Using Lighter or Heavier Barbell Loads?—A Comparison of Two Principal Component Analysis-Based Approaches to Studying Technique

Frontiers in Psychology ◽

10.3389/fpsyg.2020.606070 ◽

2021 ◽

Vol 11 ◽

Author(s):

Inge Werner ◽

Nicolai Szelenczy ◽

Felix Wachholz ◽

Peter Federolf

Keyword(s):

Principal Component Analysis ◽

Repeated Measures ◽

Principal Component ◽

Movement Pattern ◽

Component Analysis ◽

Whole Body ◽

Load Effects ◽

The One ◽

Coordination Patterns ◽

Initial Elevation

This study compared whole body kinematics of the clean movement when lifting three different loads, implementing two data analysis approaches based on principal component analysis (PCA). Nine weightlifters were equipped with 39 markers and their motion captured with 8 Vicon cameras at 100 Hz. Lifts of 60, 85, and 95% of the one repetition maximum were analyzed. The first PCA (PCAtrial) analyzed variance among time-normed waveforms compiled from subjects and trials; the second PCA (PCAposture) analyzed postural positions compiled over time, subjects and trials. Load effects were identified through repeated measures ANOVAs with Bonferroni-corrected post-hocs and through Cousineau-Morey confidence intervals. PCAtrial scores differed in the first (p < 0.016, ηp2 = 0.694) and fifth (p < 0.006, ηp2 = 0.768) principal component, suggesting that increased barbell load produced higher initial elevation, lower squat position, wider feet position after squatting, and less inclined arms. PCAposture revealed significant timing differences in all components. We conclude, first, barbell load affects specific aspects of the movement pattern of the clean; second, the PCAtrial approach is better suited for detecting deviations from a mean motion trajectory and its results are easier to interpret; the PCAposture approach reveals coordination patterns and facilitates comparisons of postural speeds and accelerations.

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Principal Component Analysis and Independent Component Analysis-Based Prognostic Health Monitoring of Electronic Assemblies Subjected to Simultaneous Temperature-Vibration Loads

Journal of Electronic Packaging ◽

10.1115/1.4047577 ◽

2020 ◽

Vol 143 (1) ◽

Author(s):

Pradeep Lall ◽

Tony Thomas

Keyword(s):

Principal Component Analysis ◽

Printed Circuit Board ◽

Failure Strain ◽

Principal Component ◽

Component Analysis ◽

Circuit Board ◽

Statistical Techniques ◽

Time Frequency ◽

Printed Circuit ◽

Strain Signal

Abstract This paper discusses methods for estimating different feature vectors from strain signals of an electronic assembly under combined temperature and vibration load. A vibrational load of 14 G acceleration-level with an ambient temperature of 55 °C is selected as the operating conditions for this experiment. Strain signals were measured at different time intervals during the vibration of the printed circuit board, and resistance values of the packages on the printed circuit board are monitored to identify the failure. The frequency response was measured by taking the fast Fourier transform of the signal and quantized by frequency quantization techniques. These techniques were able to identify the increase in the number of higher frequency components in the strain signal before failure with increase vibration time. The time-frequency response was also compared by employing different time–frequency analysis, joint time–frequency analysis, and statistical techniques such as principal component analysis (PCA), and independent component analysis (ICA). Statistical techniques like PCA and ICA were used to identify the different patterns of the original strain and filtered signals. These techniques discretely separated the before and after failure strain signals but were unable to predict the progression of failure in the packages. The instantaneous frequency of the strain signal displayed an interesting behavior, in which the variance of the PCA components of the instantaneous frequency had an increasing trend and reached a maximum value before continuously decreasing and reaching a lower value just before failure, indicating a progression of the before failure strain components.

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