Feature Extraction for Data-Driven Remaining Useful Life Prediction of Rolling Bearings

Fang

IET Generation Trans & Dist

et al. 2021

The information of dissolved gas in transformer oil can reflect the potential fault in oil immersed power transformer. In order to improve the accuracy of transformer fault diagnosis, a transformer fault diagnosis model based on IFA-LPboost-CART is proposed here. First, a LPboost-CART model is established. The classification and regression tree (CART) are used as the weak classifiers, and the linear programming boosting (LPboost) ensemble learning method is used to adjust the weight of each weak classifier to construct a strong classifier. Then the improved firefly algorithm (IFA) is adopted to optimize the number of CART and the maximum number of splits of CART in LPboost-CART to obtain the IFA-LPboost-CART model. The experimental results show that, compared with the existing methods, such as CART and support vector machine (SVM), the proposed IFA-LPboost-CART model has higher fault diagnosis accuracy, which can provide technical support for transformer fault diagnosis.

Section: Introductionmentioning

confidence: 99%

Research on transformer fault diagnosis: Based on improved firefly algorithm optimized LPboost–classification and regression tree

Fang

IET Generation Trans & Dist

et al. 2021

International Journal of Rotating Machinery

“…Wavelet packet analysis is a more detailed analysis and reconstruction method of signal from wavelet analysis. In wavelet analysis, the signal is actually decomposed into low-frequency rough parts and high-frequency details, and then only the low-frequency details are decomposed for the second time, instead of the high-frequency parts [46,47]. But in practice, we are only interested in some undetermined time (point) or frequency domain (point) signal, only need to extract the information of these specific time and frequency points.…”

Section: The Principle Of the Wavelet Packet Denoising Algorithmmentioning

confidence: 99%

Multisource Fault Signal Separation of Rotating Machinery Based on Wavelet Packet and Fast Independent Component Analysis

Miao

Zhao

Jia

et al. 2021

The vibration signal of rotating machinery compound faults acquired in actual fields has the characteristics of complex noise sources, the strong background noise, and the nonlinearity, causing the traditional blind source separation algorithm not be suitable for the blind separation of rotating machinery coupling fault. According to these problems, an extraction method of multisource fault signals based on wavelet packet analysis (WPA) and fast independent component analysis (FastICA) was proposed. Firstly, according to the characteristic of the vibration signal of rotating machinery, an effective denoising method of wavelet packet based on average threshold is presented and described to reduce the vibration signal noise. In the method, the thresholds of every node of the best wavelet packet basis are acquired and averaged, and then the average value is used as a global threshold to quantize the decomposition coefficient of every node. Secondly, the mixed signals were separated by using the improved FastICA algorithm. Finally, the results of simulations and real rotating machinery vibration signals analysis show that the method can extract the rotating machinery fault characteristics, verifying the effectiveness of the proposed algorithm.

“…Once the rotating machines break down, it will not only reduce the production efficiency of the industrial production process and cause economic losses but also threaten the safety of human production and work. Rolling bearing is one of the key parts of rotating machinery [1,2]. Incomplete statistics indicate that 30% of rotating machinery failures are caused by bearing failures [3,4].…”

Section: Introductionmentioning

confidence: 99%

Rolling Bearing Fault Diagnosis Using Improved Deep Residual Shrinkage Networks

Chen

et al. 2021

Shock and Vibration

To improve feature learning ability and accurately diagnose the faults of rolling bearings under a strong background noise environment, we present a new shrinkage function named leaky thresholding to replace the soft thresholding in the deep residual shrinkage networks (DRSNs). In this work, we discover that such improved deep residual shrinkage networks (IDRSNs) can be realized by using a group searching method to optimize the slope value of leaky thresholding, and leaky thresholding in the IDRSNs can more effectively eliminate the noise of signal features. We highlight that our techniques can significantly improve the performance on various fundamental tasks. Experimental results show that IDRSNs achieve better fault diagnosis results on noised vibration signals compared with DRSNs. Moreover, we also provide a normalized processing to further improve the fault diagnosing accuracy of rolling bearing under a strong background noise environment.