Online monitoring of tool chatter in turning based on ensemble empirical mode decomposition and Teager Filter

2019 ◽  
Vol 42 (6) ◽  
pp. 1166-1179 ◽  
Author(s):  
Yogesh Shrivastava ◽  
Bhagat Singh
Keyword(s):  
Online Monitoring ◽  
Cutting Parameters ◽  
Ensemble Empirical Mode Decomposition ◽  
Exact Nature ◽  
The Past ◽  
Mode Decomposition ◽  
Cutting Zone ◽  
Experimental Values ◽  
Processing Techniques ◽  
Suitable Technique

Online monitoring of acquired vibration signals of the cutting tool can help in predicting the severity of chatter. In the past, researchers have reported that the signals recorded using various types of sensors are usually contaminated with background noise and other disturbances. Processing these contaminated signals results in inappropriate feature extraction. Hence, for predicting the exact nature of chatter it is required to identify: (1) Best suitable sensor for recording the chatter signal, (2) nature of the recorded signals, (3) appropriate technique to filter out the contamination, (4) applying suitable technique to identify chatter frequency and safe cutting zone. In the present work, a theoretical analysis has been done to extract the frequency pertaining to chatter. Moreover, experiments have been performed and a suitable signal pre and post-processing techniques have been adopted to identify the chatter frequency. It has been found that the variation between the theoretical and experimental values is nominal. Furthermore, the safe cutting zone has been identified theoretically as well as experimentally for a given range of input cutting parameters.

scholarly journals Investigating an Optimal Signal Epoch Length for Cardiotocographic Classification

2020 ◽  
Vol 6 (3) ◽  
pp. 514-517
Author(s):  
Patricio Fuentealba ◽  
Alfredo Illanes ◽  
Frank Ortmeier ◽  
Prabal Poudel
Keyword(s):  
Main Idea ◽  
Ensemble Empirical Mode Decomposition ◽  
Classification Performance ◽  
Autoregressive Modelling ◽  
Mode Decomposition ◽  
Adaptive Noise ◽  
Signal Segment ◽  
Processing Techniques ◽  
Signal Processing Techniques ◽  
Optimal Signal

AbstractThis work focuses on investigating an optimal foetal heart rate (FHR) signal segment to be considered for automatic cardiotocographic (CTG) classification. The main idea is to evaluate a set of signal segments of different length and location based on their classification performance. For this purpose, we employ a feature extraction operation based on two signal processing techniques, such as the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and time-varying autoregressive modelling. For each studied segment, the features are extracted and evaluated based on their performance in CTG classification. For the proposed evaluation, we make use of real CTG data extracted from the CTU-UHB database. Results show that the classification performance depends considerably on the selected FHR segment. Likewise, we have found that an optimal FHR segment for foetal welfare assessment during labour corresponds to a segment of 30 minutes long.

Assessment of stable cutting zone in CNC turning based on empirical mode decomposition and genetic algorithm approach

2017 ◽  
Vol 232 (20) ◽  
pp. 3573-3594 ◽  
Author(s):  
Yogesh Shrivastava ◽  
Bhagat Singh
Keyword(s):  
Genetic Algorithm ◽  
Empirical Mode Decomposition ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Noise Analysis ◽  
Cutting Parameters ◽  
Mode Decomposition ◽  
Cutting Zone ◽  
Tool Chatter

Assessment of optimum stable cutting zone is the key requirement to maintain high productivity with enhanced surface quality of work-piece. Tool chatter is one of the factors responsible for deviation from these features. Despite the immense work done within this domain, still many aspects related to regenerative chatter remains unexplored. Usually, the chatter signals recorded from sensors are contaminated by background noise. Analysis of these contaminated signals results in faulty information regarding the identification of tool chatter. So, it becomes imperative that these signals should be denoised before further processing. In the present work, empirical mode decomposition technique has been adopted to pre-process the acquired raw chatter signals, which have been overlooked by the previous researchers. Initially, acoustic signals have been recorded by performing experiments at different combinations of cutting parameters. The preprocessed signals have been used to evaluate a new output parameter i.e. chatter index. Material removal rate has also been measured for each experiment. For estimating the dependence of output on input cutting parameters, mathematical models have been developed using response surface methodology. Moreover, the optimum cutting zone has been assessed by adopting multi-objective genetic algorithm. Finally, more experiments have been conducted to validate the obtained cutting zone. It has been found that the acquired cutting zone is capable of producing work pieces with good surface finish and acceptable material removal rate.

scholarly journals Utilisation of Ensemble Empirical Mode Decomposition in Conjunction with Cyclostationary Technique for Wind Turbine Gearbox Fault Detection

2020 ◽  
Vol 10 (9) ◽  
pp. 3334 ◽  
Author(s):  
Sanaz Roshanmanesh ◽  
Farzad Hayati ◽  
Mayorkinos Papaelias
Keyword(s):  
Signal Processing ◽  
Wind Turbine ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Noisy Environment ◽  
Wind Turbine Gearbox ◽  
Multiple Faults ◽  
Mode Decomposition ◽  
Processing Techniques ◽  
Signal Processing Techniques

In this paper the application of cyclostationary signal processing in conjunction with Ensemble Empirical Mode Decomposition (EEMD) technique, on the fault diagnostics of wind turbine gearboxes is investigated and has been highlighted. It is shown that the EEMD technique together with cyclostationary analysis can be used to detect the damage in complex and non-linear systems such as wind turbine gearbox, where the vibration signals are modulated with carrier frequencies and are superimposed. In these situations when multiple faults alongside noisy environment are present together, the faults are not easily detectable by conventional signal processing techniques such as FFT and RMS.

scholarly journals A comparative study of wavelet- and empirical mode decomposition-based GPR models for river discharge relationship modeling at consecutive hydrometric stations

2021 ◽  
Author(s):  
Kiyoumars Roushangar ◽  
Masoumeh Chamani ◽  
Roghayeh Ghasempour ◽  
Hazi Mohammad Azamathulla ◽  
Farhad Alizadeh
Keyword(s):  
Empirical Mode Decomposition ◽  
Flow Characteristics ◽  
Ensemble Empirical Mode Decomposition ◽  
The State ◽  
Applied Model ◽  
River Stage ◽  
Mode Decomposition ◽  
Model Efficiency ◽  
Processing Techniques ◽  
State 1

Abstract River stage-discharge relationship has an important impact on modeling, planning, and management of river basins and water resources. In this study, the capability of Gaussian Process Regressions (GPR) kernel-based approach was assessed in predicting the daily river stage-discharge (RSD) relationship. Three successive hydrometric stations of Housatonic River were considered and based on the flow characteristics during the period of 2002–2006 several models were developed and tested via GPR. To enhance the applied model efficiency, two pre-processing techniques namely Wavelet Transform (WT) and Ensemble Empirical Mode Decomposition (EEMD) were used. Also, two states of the RSD modeling were investigated. In the state 1, each station's own data was used and in the state 2, the upstream stations’ datasets were used as input to model the RSD at downstream of the river. The single and integrated models results showed that the integrated WT- and EEMD-GPR models resulted in more accurate outcomes. Data processing enhanced the models capability between 25 and 40%. The results showed that the RSD modeling in the state 1 led to better results; however, when the stations’ own data were not available the integrated methods could be applied successfully for the RSD modeling using the previous stations’ data.

Stable cutting zone prediction in computer numerical control turning based on empirical mode decomposition and artificial neural network approach

2018 ◽  
Vol 41 (1) ◽  
pp. 193-209 ◽  
Author(s):  
Yogesh Shrivastava ◽  
Bhagat Singh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Surface Quality ◽  
Empirical Mode Decomposition ◽  
Cutting Parameters ◽  
Computer Numerical Control ◽  
Numerical Control ◽  
Mode Decomposition ◽  
Cutting Zone ◽  
Tool Chatter

Stable cutting zone prediction is the key requirement for retaining high-productivity with enhanced surface quality of work-piece. Tool chatter is one of the factors responsible for abrupt change in surface quality and productivity. In this research work, an optimum safe cutting zone has been predicted by analyzing the tool chatter so that higher productivity can be achieved. Initially, chatter signals have been recorded by performing experiments at different combinations of cutting parameters on computer numerical control trainer lathe. Further, these recorded signals have been preprocessed by empirical mode decomposition technique, followed by the selection of dominating intrinsic mode functions using Fourier transform. The preprocessed signals have been used to evaluate a new output parameter, that is, chatter index (CI). Artificial neural network (ANN) based on the feedforward backpropagation network has been proposed for predicting tool chatter in turning process. The input machining parameters considered are depth of cut, feed rate and cutting speed. It has also been deduced that from available different transfer functions, the Hyperbolic Tangent transfer function in ANN is best suitable to predict tool chatter severity in turning operation. Moreover, the safe cutting zone has been assessed by evaluating the dependency of CI on cutting parameters. Finally, more experiments have been conducted to validate the obtained cutting zone.

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.

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