The vibration signals for rotating machines are generally polluted by excessive noise and can lose the fault information at the early development phase. In this paper, an improved denoising technique is proposed for early faults diagnosis of rolling bearing based on the complete ensemble empirical mode decomposition (CEEMD) and adaptive thresholding (ATD) method. Firstly, the bearing vibration signals are decomposed into a set of various intrinsic mode functions (IMFs) using CEEMD algorithm. The IMFs grouping and selection are formed based upon the correlation coefficient value. The noise-predominant IMFs are subjected to adaptive thresholding for denoising and then added to the low-frequency IMFs for signal reconstruction. The effectiveness of the proposed method denoised signals are measured based on kurtosis value and the envelope spectrum analysis. The presented method results on experimental datasets illustrate that the proposed approach is an effective denoising technique for early fault detection in the rolling bearing.
Vibration Analysis is one of the most effective methods used for the condition monitoring of rolling element bearings. The early failure of bearing is mainly due to the presence of solid particles in the grease lubricants. The condition of lubrication in the bearing is an essential parameter to meet the various demanding conditions of the system. This paper aims to analyze the effect of lubricant contamination by solid particles on the dynamic behavior of rolling bearing and to classify them using a support vector machine (SVM) and deep learning algorithm. Experimental tests have been performed with 50 and 100 mg of sand dust particles added to the ball bearings to contaminate the grease lubricant at full load conditions. Vibration signals were analyzed in terms of RMS, kurtosis, skewness, and peak to peak for fault type classification using SVM. In deep learning, the raw vibration signals are converted into a spectrogram image and fed to convolution neural networks (CNN) for fault classification. The results indicate that both SVM and deep learning techniques are effective for fault classification under the influence of lubricant contamination.
We known that different multipliers consume most of the power in DSP computations, FIR filters. Hence, it is very important factor for modern DSP systems to built low-power multipliers to minimize the power dissipation. In this paper, we presents high speed & low power Row Column bypass multiplier design methodology that inserts more number of zeros in the multiplicand thereby bypass the number of zero in row & Column as well as reduce power consumption. The bypassing of zero activity of the component used in the process of multiplication, depends on the input bit data. This means if the input bit data is zero, corresponding row and column of adders need not be addition & transfer bit in next row and column adder circuit. If multiplicand having more zeros, higher power reduction can be achieved. At last stage of Row & column bypass multiplier having ripple carry adder which are increase time to generate carry bit to transfer next adder circuit. To reduce this problem by using Carry bypass adder in place of ripple carry adder, then new modification of Row &column multiplier having high speed in comparison to simple row & column bypass multiplier, , the experimental results show that our proposed multiplier reduces power dissipation & High speed overhead on the average for 4x4, 8x8 and 16x16 multiplier.
The vibration signals for rotating machines are generally polluted by excessive noise and can lose the fault information at the early development phase. In this paper, an improved denoising technique is proposed for early faults diagnosis of rolling bearing based on the complete ensemble empirical mode decomposition (CEEMD) and adaptive thresholding (ATD) method. Firstly, the bearing vibration signals are decomposed into a set of various intrinsic mode functions (IMFs) using CEEMD algorithm. The IMFs grouping and selection are formed based upon the correlation coefficient value. The noise-predominant IMFs are subjected to adaptive thresholding for denoising and then added to the low-frequency IMFs for signal reconstruction. The effectiveness of the proposed method denoised signals are measured based on kurtosis value and the envelope spectrum analysis. The presented method results on experimental datasets illustrate that the proposed approach is an effective denoising technique for early fault detection in the rolling bearing.
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