Kota Solomon Raju

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.

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.

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.

Computational Acceleration of Real-Time Kernel-Based Tracking System

Object tracking in real-time is one of the applications of video processing, where the required computational cost is high due to intensive high data processing. In order to solve these problems, t...

Hardware software partitioning of task graph using genetic algorithm

One of the addressable problem in the hardware software co-design is partitioning of functionality on CPU and ASIC/FPGA. The partitioning phase requires the decision for mapping and scheduling of application given as task graphs on a given CPU/ASIC combination. Hardware software partitioning is one of the critical steps to decide which components can be implemented in hardware and which ones implemented in software so that overall system is optimized. Based on a task graph model, this paper presents the optimum solution of problem using genetic algorithm techniques. Experimental results demonstrate that this method can achieve optimized partitioning in terms of cost and delay. A trade off between cost and delay has also been achieved to get the best possible solution.

Real Time Object Tracking: Simulation and Implementation on FPGA Based Soft Processor

Adaptive systems are being easy to design using reconfiguration facility on Field programmable gate arrays (FPGAs). In this paper, Kernel based Mean shift algorithm is used for tracking a moving object. First it is simulated on Matlab and then implemented on microblaze soft processor based FPGA board. Tracking is observed for two similar objects crossing each other moving with uniform speed in a stored video as well as real time video. Object tracking, when it comes to implement on pure software (SW) in real time becomes difficult task due to certain limitations of SW. This paper shows how the mean shift algorithm is implemented on Xilinx Spartan 6 FPGA board using EDK. Once the complete algorithm is implemented on microblaze soft processor then some of the mathematical functions of algorithm are calculated on hardware to use HW-SW co-designing methodology to enhance the performance of the system.

Parameterized placement algorithm of WSN for structural health monitoring

WSN is an application specific network. There is no general optimized solution for WSN problems. It depends upon application, resource availability. Structural health monitoring (SHM) using wireless sensor networks has drawn considerable attention in recent years. It is an active area of research that can autonomously and proactively assess the structural integrity of buildings, bridges, tunnels, turbines and nuclear reactors etc. using Wireless Sensor Network. This paper basically focuses on application dependent parameters that effect algorithms for SHM applications. The parameters which are effect at Routing layer have been discussed for SHM applications considering network design model parameters. An efficient algorithm has been proposed to prolong the network lifetime and mathematical calculations are showing that LFT is increasing and energy consumption is decreasing for a given sensor node by optimizing the distance.