Karunesh Kumar Gupta

Feature Adaptive Wavelet Shrinkage for Image Denoising

In this paper, a new wavelet shrinkage denoising algorithm is presented. The algorithm uses wavelet transform (WT) to extract information about sharp variation in multiresolution images and applies shrinkage function adapting the image features. The shrinkage function depends on energy of neighboring pixels, whereas in standard wavelet methods, the empirical wavelet coefficients shrink pixel by pixel, on the basis of their individual magnitude. Experiments show that wavelet shrinkage algorithm which uses neighboring pixels energy improves the denoising performance and achieves better peak signal to noise ratio compared to other thresholding algorithms. Due to its low complexity, the proposed algorithm is very suitable for hardware implementation

Comparative study between VMD and EMD in bearing fault diagnosis

This paper proposes a novel Variational mode decomposition (VMD) algorithm for bearing fault diagnosis. The Fast Fourier Transform fails to analyse the transient and non-stationary signals. Discrete Fourier transform and Empirical mode decomposition do not have the ability to attain the accurate Intrinsic mode functions under dynamic system fault conditions because the characteristic of exponentially decaying dc offset is not consistent. EMD is a fully data-driven, not model-based, adaptive filtering procedure for extracting signal components. The EMD technique has high computational complexity and requires a large data series. The proposed technique has high accuracy and convergent speed, and is greatly appropriate for bearing fault diagnosis. This paper illustrates that VMD removes the exponentially decaying dc offset and evaluates its performance compared to EMD.

Vibro acoustic signal analysis in fault finding of bearing using Empirical Mode Decomposition

Bearing fault is an issue in process and control industries, and has significant impact in the production flow. The behaviour of the machinery can be well understood from the frictional forces of the bearing due to load, and also the wear and tear of the ball bearings. The characteristic of this ball bearing can predict the exact nature of the load and any future malfunction in the operating equipments. The signals generated from these bearings can be of any types i.e., sound or vibration. The acoustic phenomenon is tough to predict in noisy environment, where as the vibration data can be used when the acoustic cannot be the source of information. In general the fault diagnosis in bearing is done by comparing the mathematical interpreted data with vibration signal. This method can only be applicable to those system where the complete information about the ball bearing is known. But, this paper predict the fault in the ball bearing using acoustic and vibration signatures without knowing complete bearing information. Signal processing is used rather than using both signal processing and mathematical formulation all together to predict the fault in the bearing under different states. The signal analysis using FFT fails to analyse the signals of transient and non-stationary in nature. The extraction and analysis of the transient signal can be better done using Empirical Mode Decomposition (EMD) technique.

Multi-channel vibro-acoustic fault analysis of ball bearing using wavelet based multi-scale principal component analysis

Ball bearing fault segmentation at different time steps are important to avert failure. This paper studies the Vibro-acoustic characteristic of the ball bearing using Wavelet Based Multi Scale Principal Component Analysis (WMSPCA) and FFT. Firstly, the characteristic frequencies of the ball bearing for healthy and unhealthy states are verified using an impulse exciter hammer; and the generated frequencies are acquired using a Zigbee wireless accelerometer sensor. Secondly, the acoustic and vibration characteristics are acquired using three channel accelerometer sensor and a array microphone. Lastly, the actual characteristics of the ball bearing are extracted using WMSPCA. The main advantage of WMSPCA lies in the actual feature segmentation from different channels independent relative to the direction of propagation of faults. WMSPCA uses wavelet and PCA to auto-correlate and cross-correlate the signal simultaneously. The algorithm extracts the frequency range of operation of the ball bearing and assists in determining the precise frequency of vibration excluding its perplexed frequency components associated along tangential, axial and radial direction of the ball bearing. The paper also correlates the significance of acoustic-vibration in the fault finding of bearing.

A review of emerging trends on water quality measurement sensors

New concepts and techniques are replacing traditional methods of water quality parameters measurement systems. In modern sensor era, Optical Sensors (OS), Microelectronic Mechanical Systems (MEMS) and Bio-Sensors are important sensing techniques for different water quality parameter detection. Furthermore, these sensors are highly selective, sensitive, economical and user-friendly with quick response. This paper comprehensively reviews and discuss role of emerging techniques in detection of important water quality parameters i.e Dissolved Oxygen, Turbidity, pH, E-Coli, Effective chlorination, Biochemical Oxygen Demand (B.O.D) and fluoride. In addition, also explains why modern water quality parameters sensing techniques are preferable option for detection of above mentioned parameters. A dedicated part of this paper also discusses the significant advantages and limitations of new available techniques.

Bearing fault analysis using variational mode decomposition

Bearing health analysis plays a significant role in industry to improve reliability and performance of critical processes by alarming the faults at early stages. Conventional techniques do no guarantee to detect the faults at early stages because the low energy bearing frequencies get suppressed by stern noise and higher vibrations. The Fast Fourier Transform fails to analyse the transient and non-stationary signals directly. This paper performs the signal analysis on vibration data of ball bearing using Variational mode decomposition (VMD). Firstly, the intrinsic mode functions are extracted using VMD followed by Fast Fourier Transform, and finally the status of bearing is analyzed to be faulty or impeccable. This paper, stress on VMD rather than on EMD, due to its qualities in the detection of close tone vibration signatures and takes less computation time.

Active Power Filter Control Algorithm using Wavelets

This paper presents a wavelet transform (WT) based technique to extract fundamental frequency component from a nonsinusoidal and unbalanced load current in a three phase system. The fundamental frequency component is extracted using multiresolution analysis (MRA). The remaining harmonics can be used by the active filter for compensation. Simulation result obtained for a rectifier load current shows the usefulness of the proposed method.

Characterization of wireless accelerometer sensor and its industrial applications

The basic idea of this paper is to characterize wireless MEMS capacitive accelerometer sensor based on their field of applications. The selection of accelerometers are difficult for certain applications, that demands the sensor to be mount on rotating platform, higher value of g, sensitivity, and wide bandwidth of operation. Whenever higher sensitivity is chosen, the short fall is in the range of g and the bandwidth of operation. This is a serious issue with the sensor as far as industrial applications i.e., ball mill and sag mills are concerned. There is a misconception of using higher value of g (approximately around 500 g) with lower sensitivity in ball mill that is justified in this paper. Generally, the internal frequency of vibration of the ball mill is unknown, and the vibration due to impact during grinding is also random due to non uniformity in the grinding action inside the mill. For such an application, random selection of sensors can mislead the data acquisition and interpretation process. The perplexity of the application demands the characterization of accelerometer, when they are mounted on rotating platform. In this paper the sensor is characterized in mechanical testing lab using lathe machine and later on the same sensor is subjected to measure vibration of the industrial ball mill. Further, the data is transmitted using Zigbee (IEEE 802.15.4), and the RF signal losses during rotation and transmission are also taken care to avoid the high frequency losses due to multiple reflections. Finally, the vibration signatures obtained during experimental phases are analyzed using Fast Fourier Transform (FFT) to characterize the sensor at different operating speeds of the lathe machine.

Vibro-Acoustic Fault Analysis of Bearing Using FFT, EMD, EEMD and CEEMDAN and Their Implications

This paper analyses the vibro-acoustic characteristics of the bearing using FFT (Fast Fourier Transform), EMD (Empirical Mode Decomposition), EEMD (Ensemble EMD) and CEEMDAN (Complete EEMD with Adaptive Noise) algorithms. The main objective is to find out the best algorithm that avoids mode mixing problems while decomposing the signal and also enhance the feature extraction. It is observed that even though acoustic and vibration can be used for the fault detection in the bearing, duo follow differently interns of their statistical distributions. The feature of the bearing is acquired using acoustic and vibration sensors and analyzed using non-linear and non-stationary signal processing techniques. The statistical distribution of the data plays a major role in truly extracting the components using signal processing techniques. All the algorithms are data driven, as per the conditional events of the system, these algorithms efficiency increases or decreases. Here, the vibro-acoustic feature of the normally distributed acoustic and vibration signature are extracted effectively using CEEMDAN with least computational time and efficient signal extraction.

GFDM performance in terms of BER, PAPR and OOB and comparison to OFDM system

Generalized frequency division multiplexing is a multicarrier modulation technique which can be foreseen as a potential alternative for upcoming wireless networks. GFDM attractive features include reduced out-of-band radiation(OOB) and low peak-to-average ratio(PAPR), which are the crucial shortcomings of OFDM used in present day wireless communication networks. This paper gives detailed description of GFDM system model and further studies and validates through simulations, the performance of GFDM in terms of OOB, PAPR and Bit Error Rate (BER) and compares the results obtained with OFDM system.