scholarly journals Mapping Relation between Contour Error Components of Crankshaft Pin Journal and Axis Position Control Error of Oscillating Grinding Machine

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6497
Author(s):  
Xiaoyan Fang ◽  
Xiaowei Sheng ◽  
Yize Sun ◽  
Yang Xu
Keyword(s):  
Position Control ◽  
High Reliability ◽  
Decomposition Methods ◽  
Ensemble Empirical Mode Decomposition ◽  
Contour Error ◽  
Control Error ◽  
Error Components ◽  
Mode Decomposition ◽  
Axis Position ◽  
Grinding Machine

Automatic crankshaft production lines require high reliability and accuracy stability for the oscillating grinding machine. Crankshaft contour error represent the most intuitive data in production field selective inspection. If the mapping relation between the contour error components of the crankshaft pin journal and the axis position control error of the oscillating grinding machine can be found, it would be great significance for the reliability maintenance of the oscillating grinding machine. Firstly, a contour error decomposition method based on ensemble empirical mode decomposition (EEMD) is proposed. Secondly, according to the contour generating principle of the pin journal by oscillating grinding, a calculation method to obtain the effect of the axis position control error of the oscillating grinder on the contour error of the pin journal is proposed. Finally, through the grinding experiments, the error data are acquired and measured to calculate and decompose the contour error by using the proposed methods for obtaining the mapping relation between the crankshaft pin journal contour error and the axis position control error. The conclusions show that the proposed calculation and decomposition methods can obtain the mapping relation between the contour error components of the crankshaft pin journal and the axis position control error of the oscillating grinding machine, which can be used to predict the key functional component performance of the machine tool from the oscillating grinding workpiece contour error.

scholarly journals Complete Ensemble Empirical Mode Decomposition on FPGA for Condition Monitoring of Broken Bars in Induction Motors

Mathematics ◽  
2019 ◽  
Vol 7 (9) ◽  
pp. 783 ◽  
Author(s):  
Martin Valtierra-Rodriguez ◽  
Juan Amezquita-Sanchez ◽  
Arturo Garcia-Perez ◽  
David Camarena-Martinez
Keyword(s):  
Feature Extraction ◽  
Empirical Mode Decomposition ◽  
Condition Monitoring ◽  
Induction Motors ◽  
A Priori ◽  
Digital Signal ◽  
Decomposition Methods ◽  
Ensemble Empirical Mode Decomposition ◽  
Processing Technique ◽  
Mode Decomposition

Empirical mode decomposition (EMD)-based methods are powerful digital signal processing techniques because they do not need a priori information of the target signal due to their intrinsic adaptive behavior. Moreover, they can deal with non-linear and non-stationary signals. This paper presents the field programmable gate array (FPGA) implementation for the complete ensemble empirical mode decomposition (CEEMD) method, which is applied to the condition monitoring of an induction motor. The CEEMD method is chosen since it overcomes the performance of EMD and EEMD (ensemble empirical mode decomposition) methods. As a first application of the proposed FPGA-based system, the proposal is used as a processing technique for feature extraction in order to detect and classify broken rotor bar faults in induction motors. In order to obtain a complete online monitoring system, the feature extraction and classification modules are also implemented on the FPGA. Results show that an average effectiveness of 96% is obtained during the fault detection.

scholarly journals Extracting Modulated Annual Cycle in Climate and Ocean Time Series Using an Enhanced Harmonic Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yibo Zhang ◽  
Haidong Pan ◽  
Shuang Li ◽  
Xianqing Lv
Keyword(s):  
Harmonic Analysis ◽  
Annual Cycle ◽  
Numerical Experiments ◽  
Decomposition Methods ◽  
Ensemble Empirical Mode Decomposition ◽  
Annual Cycles ◽  
Mode Decomposition ◽  
Observation Series ◽  
Climate Series ◽  
Modulated Annual Cycle

Accurate extraction of the modulated annual cycle (MAC) is important for climatic and oceanic research. A variety of methods are available to extract the annual cycle with inconsistent results. Since actual annual cycles are unknown in the observation series, the reliability and applicability of the results extracted by these methods are difficult to estimate. In this study, three widely used decomposition methods, ensemble empirical mode decomposition (EEMD), nonlinear mode decomposition (NMD), and enhanced harmonic analysis (EHA), are evaluated by idealized numerical experiments for extracting modulated annual cycles from climate series. Idealized numerical experiments are carried out and show that the recently proposed EHA had the most accuracy in extracting the MAC from the constructed data. The optimal independent point (IP) number, which makes the most accurate result for EHA, can be found in each ideal experiment. In the actual experiment, two IP selection criteria are proposed for EHA to extract MAC from observations.

scholarly journals Analysis method of the cavitation vibration signals in poppet valve based on EEMD

2021 ◽  
Vol 13 (2) ◽  
pp. 168781402199811
Author(s):  
Beibei Li ◽  
Qiao Zhao ◽  
Huaiyi Li ◽  
Xiumei Liu ◽  
Jichao Ma ◽  
...  
Keyword(s):  
Ensemble Empirical Mode Decomposition ◽  
Center Frequency ◽  
Analysis Method ◽  
Vibration Signals ◽  
Poppet Valve ◽  
Mode Decomposition ◽  
Marginal Spectrum ◽  
Cavitation Intensity ◽  
Eemd Method ◽  
Peak Value

To study the vibration characteristics of the poppet valve induced by cavitation, the signal analysis method based on the ensemble empirical mode decomposition (EEMD) method was studied experimentally. The component induced by cavitation was separated from the vibration signals through the EEMD method. The results show that the IMF2 component has the largest amplitude and energy of all components. The root mean square (RMS) value, peak value of marginal spectrum, and center frequency of marginal spectrum of the IMF2 component were studied in detail. The RMS value and the peak value of the marginal spectrum decrease with a decrease of cavitation intensity. The center frequency of marginal spectrum is between 12 kHz and 20 kHz, and the center frequency first increases and then decreases with a decrease of cavitation intensity. The change rate of the center frequency also decreases with an increase of inlet pressure.

scholarly journals Bearing Fault Classification Using Ensemble Empirical Mode Decomposition and Convolutional Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1248
Author(s):  
Rafia Nishat Toma ◽  
Cheol-Hong Kim ◽  
Jong-Myon Kim
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Fault Classification ◽  
Original Signal ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Reconstructed Signal

Condition monitoring is used to track the unavoidable phases of rolling element bearings in an induction motor (IM) to ensure reliable operation in domestic and industrial machinery. The convolutional neural network (CNN) has been used as an effective tool to recognize and classify multiple rolling bearing faults in recent times. Due to the nonlinear and nonstationary nature of vibration signals, it is quite difficult to achieve high classification accuracy when directly using the original signal as the input of a convolution neural network. To evaluate the fault characteristics, ensemble empirical mode decomposition (EEMD) is implemented to decompose the signal into multiple intrinsic mode functions (IMFs) in this work. Then, based on the kurtosis value, insignificant IMFs are filtered out and the original signal is reconstructed with the rest of the IMFs so that the reconstructed signal contains the fault characteristics. After that, the 1-D reconstructed vibration signal is converted into a 2-D image using a continuous wavelet transform with information from the damage frequency band. This also transfers the signal into a time-frequency domain and reduces the nonstationary effects of the vibration signal. Finally, the generated images of various fault conditions, which possess a discriminative pattern relative to the types of faults, are used to train an appropriate CNN model. Additionally, with the reconstructed signal, two different methods are used to create an image to compare with our proposed image creation approach. The vibration signal is collected from a self-designed testbed containing multiple bearings of different fault conditions. Two other conventional CNN architectures are compared with our proposed model. Based on the results obtained, it can be concluded that the image generated with fault signatures not only accurately classifies multiple faults with CNN but can also be considered as a reliable and stable method for the diagnosis of fault bearings.

scholarly journals A Hydraulic Pump Fault Diagnosis Method Based on the Modified Ensemble Empirical Mode Decomposition and Wavelet Kernel Extreme Learning Machine Methods

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2599
Author(s):  
Zhenbao Li ◽  
Wanlu Jiang ◽  
Sheng Zhang ◽  
Yu Sun ◽  
Shuqing Zhang
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Recognition Accuracy ◽  
Ensemble Empirical Mode Decomposition ◽  
Hydraulic Pump ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

To address the problem that the faults in axial piston pumps are complex and difficult to effectively diagnose, an integrated hydraulic pump fault diagnosis method based on the modified ensemble empirical mode decomposition (MEEMD), autoregressive (AR) spectrum energy, and wavelet kernel extreme learning machine (WKELM) methods is presented in this paper. First, the non-linear and non-stationary hydraulic pump vibration signals are decomposed into several intrinsic mode function (IMF) components by the MEEMD method. Next, AR spectrum analysis is performed for each IMF component, in order to extract the AR spectrum energy of each component as fault characteristics. Then, a hydraulic pump fault diagnosis model based on WKELM is built, in order to extract the features and diagnose faults of hydraulic pump vibration signals, for which the recognition accuracy reached 100%. Finally, the fault diagnosis effect of the hydraulic pump fault diagnosis method proposed in this paper is compared with BP neural network, support vector machine (SVM), and extreme learning machine (ELM) methods. The hydraulic pump fault diagnosis method presented in this paper can diagnose faults of single slipper wear, single slipper loosing and center spring wear type with 100% accuracy, and the fault diagnosis time is only 0.002 s. The results demonstrate that the integrated hydraulic pump fault diagnosis method based on MEEMD, AR spectrum, and WKELM methods has higher fault recognition accuracy and faster speed than existing alternatives.

scholarly journals Short-Term Electricity Price Forecasting by Employing Ensemble Empirical Mode Decomposition and Extreme Learning Machine

Forecasting ◽  
2021 ◽  
Vol 3 (3) ◽  
pp. 460-477
Author(s):  
Sajjad Khan ◽  
Shahzad Aslam ◽  
Iqra Mustafa ◽  
Sheraz Aslam
Keyword(s):  
Extreme Learning Machine ◽  
Empirical Mode Decomposition ◽  
Electricity Market ◽  
Ensemble Empirical Mode Decomposition ◽  
Electricity Price ◽  
Price Forecasting ◽  
Short Term ◽  
Mode Decomposition ◽  
Electricity Price Forecasting ◽  
Learning Machine

Day-ahead electricity price forecasting plays a critical role in balancing energy consumption and generation, optimizing the decisions of electricity market participants, formulating energy trading strategies, and dispatching independent system operators. Despite the fact that much research on price forecasting has been published in recent years, it remains a difficult task because of the challenging nature of electricity prices that includes seasonality, sharp fluctuations in price, and high volatility. This study presents a three-stage short-term electricity price forecasting model by employing ensemble empirical mode decomposition (EEMD) and extreme learning machine (ELM). In the proposed model, the EEMD is employed to decompose the actual price signals to overcome the non-linear and non-stationary components in the electricity price data. Then, a day-ahead forecasting is performed using the ELM model. We conduct several experiments on real-time data obtained from three different states of the electricity market in Australia, i.e., Queensland, New South Wales, and Victoria. We also implement various deep learning approaches as benchmark methods, i.e., recurrent neural network, multi-layer perception, support vector machine, and ELM. In order to affirm the performance of our proposed and benchmark approaches, this study performs several performance evaluation metric, including the Diebold–Mariano (DM) test. The results from the experiments show the productiveness of our developed model (in terms of higher accuracy) over its counterparts.

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