The Experts below are selected from a list of 16776 Experts worldwide ranked by ideXlab platform
P S Heyns - One of the best experts on this subject based on the ideXlab platform.
-
the application of order tracking for vibration analysis of a varying speed rotor with a propagating transverse crack
Engineering Failure Analysis, 2012Co-Authors: Kesheng Wang, D Guo, P S HeynsAbstract:Abstract Propagating cracked rotor vibrations may contain substantial information on the rotor crack conditions. Capturing the characteristic vibrations due to the non-stationary response of rotors of which the speed change, is useful for condition monitoring of such systems. From the literature it is clear that vibrations at harmonics of the rotational speed, as well as transient responses, may be considered key indicators of cracks in rotors. Since faults other than cracks, such as misalignment and imbalance also generate harmonic vibrations, the non-order related vibrations therefore become important characteristics in detecting cracked rotor problems. But these signals may present with small amplitude and may easily be missed among the non-stationary harmonic vibrations. Therefore, clear identification of harmonic as well as non-order related (transient) vibrations are both important for detecting cracks in rotor systems. In this paper, a finite element Model is used to calculate the response of a rotor with a propagating transverse crack under varying rotational speed conditions. various order tracking techniques, i.e. computed order tracking, Vold-Kalman filter order tracking, Gabor order tracking, are implemented to remove the varying speed harmonic vibrations so that the non-order related vibrations in which information about the cracks are contained, are emphasized. And some other signal processing methods that may achieve similar effects, i.e. double re-sampling method and Intrinsic Mode Function from empirical Mode decomposition, are also discussed for comparisons to these order tracking techniques. The paper demonstrates the advantages of order tracking methods in rotor crack detection.
-
An empirical re-sampling method on Intrinsic Mode Function to deal with speed variation in machine fault diagnostics
Applied Soft Computing, 2011Co-Authors: Kesheng Wang, P S HeynsAbstract:To implement traditional order tracking in practice requires rotational speed information. However, it may be difficult in some cases to mount an appropriate monitoring device to obtain reliable speed information. In this paper, a novel empirical re-sampling of Intrinsic Mode Functions obtained from empirical Mode decomposition is explored, so that the approximation of order tracking effects without rotational speed is possible. The newly introduced Intrinsic cycle concept in the Intrinsic Mode Function simplifies linking of the resultant spectra to signal variations, and is therefore beneficial for condition monitoring of rotating machines. In the paper the rationale behind the technique is first explained. Secondly, the effectiveness of the technique is demonstrated on a dynamic gear simulation Model. Lastly, the technique is applied to experimental data from a gearbox test rig. Both the simulation and experimental studies corroborate the usefulness of the proposed technique.
S P Harsha - One of the best experts on this subject based on the ideXlab platform.
-
fault diagnosis of rolling element bearing with Intrinsic Mode Function of acoustic emission data using apf knn
Expert Systems With Applications, 2013Co-Authors: D H Pandya, S H Upadhyay, S P HarshaAbstract:This paper presents a fault diagnosis technique based on acoustic emission (AE) analysis with the Hilbert-Huang Transform (HHT) and data mining tool. HHT analyzes the AE signal using Intrinsic Mode Functions (IMFs), which are extracted using the process of Empirical Mode Decomposition (EMD). Instead of time domain approach with Hilbert transform, FFT of IMFs from HHT process are utilized to represent the time frequency domain approach for efficient signal response from rolling element bearing. Further, extracted statistical and acoustic features are used to select proper data mining based fault classifier with or without filter. K-nearest neighbor algorithm is observed to be more efficient classifier with default setting parameters in WEKA. APF-KNN approach, which is based on asymmetric proximity Function with optimize feature selection shows better classification accuracy is used. Experimental evaluation for time frequency approach is presented for five bearing conditions such as healthy bearing, bearing with outer race, inner race, ball and combined defect. The experimental results show that the proposed method can increase reliability for the faults diagnosis of ball bearing.
Junbiao Liu - One of the best experts on this subject based on the ideXlab platform.
-
epileptic state classification based on Intrinsic Mode Function and wavelet packet decomposition
International Conference of the IEEE Engineering in Medicine and Biology Society, 2019Co-Authors: Jiuwen Cao, Xiaoping Lai, Junbiao LiuAbstract:The scalp electroencephalogram (EEG) signal based epileptic seizure analysis has been comprehensively studied in the past. But existing researches are generally concerned with the seizure/non-seizure detection. Few attention has been paid to the epileptic preictal state classification, which is found to be evidently more important to the injury prevention. In this paper, we study the epileptic preictal state classification for seizure prediction. The one hour preictal EEG signal is divided into non-overlapped equilong segments. Statistical features of the first Intrinsic Mode Function (FIMF) of the empirical Mode decomposition (EMD) of the EEG signal as well as the 4-level wavelet packet decomposition (WPD) of the FIMF are extracted for the EEG signal representation. A five-state classification problem is formulated, including one interictal, three preictal, and one seizure states. Experiments on the benchmark epilepsy EEG database collected by the Children’s Hospital Boston and the Massachusetts Institute of Technology (CHB-MIT) using several popular classifiers are provided for the effectiveness demonstration.
-
EMBC - Epileptic State Classification based on Intrinsic Mode Function and Wavelet Packet Decomposition
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Inte, 2019Co-Authors: Jiuwen Cao, Xiaoping Lai, Junbiao LiuAbstract:The scalp electroencephalogram (EEG) signal based epileptic seizure analysis has been comprehensively studied in the past. But existing researches are generally concerned with the seizure/non-seizure detection. Few attention has been paid to the epileptic preictal state classification, which is found to be evidently more important to the injury prevention. In this paper, we study the epileptic preictal state classification for seizure prediction. The one hour preictal EEG signal is divided into non-overlapped equilong segments. Statistical features of the first Intrinsic Mode Function (FIMF) of the empirical Mode decomposition (EMD) of the EEG signal as well as the 4-level wavelet packet decomposition (WPD) of the FIMF are extracted for the EEG signal representation. A five-state classification problem is formulated, including one interictal, three preictal, and one seizure states. Experiments on the benchmark epilepsy EEG database collected by the Children’s Hospital Boston and the Massachusetts Institute of Technology (CHB-MIT) using several popular classifiers are provided for the effectiveness demonstration.
Shivaram P Arunachalam - One of the best experts on this subject based on the ideXlab platform.
-
Intrinsic Mode Function complexity index using empirical Mode decomposition discriminates normal sinus rhythm and atrial fibrillation on a single lead ecg
International Conference of the IEEE Engineering in Medicine and Biology Society, 2018Co-Authors: Suganti Shivaram, Divaakar Siva Baala Sundaram, Rogith Balasubramani, Anjani Muthyala, Shivaram P ArunachalamAbstract:Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia affecting approximately 3 million Americans, and is a prognostic marker for stroke, heart failure and even death. Current techniques to discriminate normal sinus rhythm (NSR) and AF from single lead ECG suffer several limitations in terms of sensitivity and specificity using short time ECG data which distorts ECG and many are not suitable for real-time implementation. The purpose of this research was to test the feasibility of discriminating single lead ECG’s with normal sinus rhythm (NSR) and AF using Intrinsic Mode Function (IMF) complexity index. 15 sets of ECG’s with NSR and AF were obtained from Physionet database. Custom MATLAB® software was written to compute IMF index for each of the data set and compared for statistical significance. The mean IMF index for NSR across 15 data sets was 0.37 ± 0.08, and the mean IMF index for ECG with AF was 0.21 ± 0.07 showing robust discrimination with statistical significance (p<0.01). IMF complexity robustly discriminates single lead ECG with normal sinus rhythm and AF. Further validation of this result is required on a larger dataset. The results also motivate the use of this technique for analysis of other complex cardiac arrhythmias such as ventricular tachycardia (VT) or ventricular fibrillation (VF).
-
single lead ecg discrimination between normal sinus rhythm and sleep apnea with Intrinsic Mode Function complexity index using empirical Mode decomposition
Electro Information Technology, 2018Co-Authors: Divaakar Siva Baala Sundaram, Suganti Shivaram, Rogith Balasubramani, Anjani Muthyala, Shivaram P ArunachalamAbstract:Diagnosis and treatment of sleep apnea in its various forms such as obstructive, central and complex syndrome is extremely important to prevent various diseases such as hypertension, diabetes, coronary artery disease, metabolic syndrome, and cerebrovascular diseases. Current methods to detect sleep apnea interfere with sleep and also require long hours of data recording and therefore, single lead ECG based sleep apnea detection is gaining popularity due to its simplicity and practicality for real-time sleep apnea monitoring. The purpose of this research was to test the feasibility of discriminating single lead ECG's with normal sinus rhythm (NSR) and sleep apnea with Intrinsic Mode Function (IMF) complexity index using empirical Mode decomposition for real-time detection of sleep apnea. Ten sets of ECG's with NSR and ECG's with sleep apnea were obtained from Physionet database. Custom MATLAB® software was written to compute IMF complexity index for each of the data set and compared for statistical significance test $(\mathbf{p} . The mean IMF complexity for NSR across 10 data sets was $0.41\pm 0.06$ and the mean MSF for ECG with sleep apnea was $0.32 \pm 0.05$ showing robust discrimination with statistical significance $(\mathbf{p} . IMF complexity robustly discriminates single lead ECG with normal sinus rhythm and sleep apnea. Further validation of this result is required on a larger dataset.
-
rotor pivot point identification with Intrinsic Mode Function complexity index using empirical Mode decomposition
IEEE EMBS International Student Conference, 2016Co-Authors: Shivaram P Arunachalam, Siva K Mulpuru, Paul A Friedman, Elena G TolkachevaAbstract:Atrial Fibrillation (AF), a most common cardiac arrhythmia affects more than 2.3 million people in the US and associated with increased risk of stroke, heart failure and death. Cather ablation to treat paroxysmal AF patients is somewhat successful with challenges remaining to accurately identify the active sites for persistent AF patients which may occur outside the pulmonary vein (PV) region due to inadequate cardiac mapping systems. In this work, the authors propose an Empirical Mode Decomposition (EMD) approach using multi-scale entropy estimates of the Intrinsic Mode Functions as a complexity measure to accurately identify pivot point of the rotor that were induced in ex-vivo isolated rabbit heart with Ventricular Tachycardia (VT). The new approach using EMD demonstrated successful identification of the rotor core region providing better contrast relative to the periphery region. Validation of the EMD approach using intra-atrial electrograms from paroxysmal and persistent AF patients with rotors is required to accurately identify the rotor pivot point to guide AF ablation.
Kesheng Wang - One of the best experts on this subject based on the ideXlab platform.
-
the application of order tracking for vibration analysis of a varying speed rotor with a propagating transverse crack
Engineering Failure Analysis, 2012Co-Authors: Kesheng Wang, D Guo, P S HeynsAbstract:Abstract Propagating cracked rotor vibrations may contain substantial information on the rotor crack conditions. Capturing the characteristic vibrations due to the non-stationary response of rotors of which the speed change, is useful for condition monitoring of such systems. From the literature it is clear that vibrations at harmonics of the rotational speed, as well as transient responses, may be considered key indicators of cracks in rotors. Since faults other than cracks, such as misalignment and imbalance also generate harmonic vibrations, the non-order related vibrations therefore become important characteristics in detecting cracked rotor problems. But these signals may present with small amplitude and may easily be missed among the non-stationary harmonic vibrations. Therefore, clear identification of harmonic as well as non-order related (transient) vibrations are both important for detecting cracks in rotor systems. In this paper, a finite element Model is used to calculate the response of a rotor with a propagating transverse crack under varying rotational speed conditions. various order tracking techniques, i.e. computed order tracking, Vold-Kalman filter order tracking, Gabor order tracking, are implemented to remove the varying speed harmonic vibrations so that the non-order related vibrations in which information about the cracks are contained, are emphasized. And some other signal processing methods that may achieve similar effects, i.e. double re-sampling method and Intrinsic Mode Function from empirical Mode decomposition, are also discussed for comparisons to these order tracking techniques. The paper demonstrates the advantages of order tracking methods in rotor crack detection.
-
An empirical re-sampling method on Intrinsic Mode Function to deal with speed variation in machine fault diagnostics
Applied Soft Computing, 2011Co-Authors: Kesheng Wang, P S HeynsAbstract:To implement traditional order tracking in practice requires rotational speed information. However, it may be difficult in some cases to mount an appropriate monitoring device to obtain reliable speed information. In this paper, a novel empirical re-sampling of Intrinsic Mode Functions obtained from empirical Mode decomposition is explored, so that the approximation of order tracking effects without rotational speed is possible. The newly introduced Intrinsic cycle concept in the Intrinsic Mode Function simplifies linking of the resultant spectra to signal variations, and is therefore beneficial for condition monitoring of rotating machines. In the paper the rationale behind the technique is first explained. Secondly, the effectiveness of the technique is demonstrated on a dynamic gear simulation Model. Lastly, the technique is applied to experimental data from a gearbox test rig. Both the simulation and experimental studies corroborate the usefulness of the proposed technique.
