Department of Electrical and Electronics Engineering
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Item Vibro acoustic signal analysis in fault finding of bearing using Empirical Mode Decomposition(IEEE, 2013) Gupta, Karunesh KumarBearing 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.Item Comparative study between VMD and EMD in bearing fault diagnosis(IEEE, 2014) Gupta, Karunesh KumarThis 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.Item Multi-Channel Vibration Feature Extraction of Ball Mill Using Synchronized Wavelet Based Multi-Scale Principal Component Analysis(Materials Science, 2015) Gupta, Karunesh KumarThe trait of the ball mill is chaotic in nature due its complex dynamics associated during grinding. Grinding in ball mill generates high-intensity vibration and is too complex on account dependency of multiple variables. In this paper, the vibration signal is acquired using a low power ZigBee based three axes wireless MEMS accelerometer sensor mounted onto the mill shell. Firstly, the exact frequency bands of the mill are identified under variable impact loading using non synchronized and Synchronized Frequency Estimation method (SFE) methods. The synchronization between the mill speed and the sampling rate are put forward by SFE to convert the random non stationary data to quasi stationary data. The actual signal length is calculated using proposed SFE approach and further it is used as window size for wavelet decomposition. Further, to decorrelate the auto-correlated and cross-correlated signal and signal spaces both PCA and Wavelet are used. Finally, the combination of all this techniques, i.e., Synchronized Wavelet Based Multi-Scale Principal Component Analysis (SWMSPCA) is used to extract the vibration feature of the ball mill in the presence of variable density ores i.e., iron ore and limestone.Item Multi-channel vibro-acoustic fault analysis of ball bearing using wavelet based multi-scale principal component analysis(IEEE, 2015) Gupta, Karunesh KumarBall 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 bearingItem Bearing fault analysis using variational mode decomposition(IEEE, 2014) Gupta, Karunesh KumarBearing 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.Item Vibration Feature Extraction and Analysis of Industrial Ball Mill Using MEMS Accelerometer Sensor and Synchronized Data Analysis Technique(Elsevier, 2015) Gupta, Karunesh KumarThe use of advanced technologies such as Micro-electromechanical system (MEMS) sensors and low power wireless communication hold a great promise for optimal performance of industrial wet ball mill. The direct translation of the natural phenomena of the batch mill in a lab setup to a continuous process mill in the industry is quite perplexed in the nature of their intent and operating conditions. In this paper, the vibration signatures are analyzed for industrial wet ball mill using a MEMS accelerometer sensor. The signals are acquired using two wireless accelerometer sensors; mounted at feed and discharge end of the ball mill to validate the grinding status of the copper ore. The vibration spectrum before and after feed are compared to estimate the actual grinding status of the ore inside the mill. A limiting threshold level for the intensity is identified from the spectral analysis to monitor the desired grinding status of the ore. The high frequency (ZigBee) transmission loss due to diffraction is also compensated by the novel arrangement of the sensor transceiver. Finally, Pearson correlation technique is used to analyze the effect of sample length and its dependency with the rpm of the mill in determining the actual vibration signature.