Casing vibration signal characteristic extractions and applications in rolling bearing

In allusion to the issue of rolling bearing degradation feature extraction and degradation condition clustering, a logistic chaotic map is introduced to analyze the advantages of C0 complexity and a technique based on a multidimensional degradation feature and Gath–Geva fuzzy clustering algorithmic is proposed. The multidimensional degradation feature includes C0 complexity, root mean square, and curved time parameter which is more in line with the performance degradation process. Gath–Geva fuzzy clustering is introduced to divide different conditions during the degradation process. A rolling bearing lifetime vibration signal from intelligent maintenance system bearing test center was introduced for instance analysis. The results show that C0 complexity is able to describe the degradation process and has advantages in sensitivity and calculation speed. The introduced degradation indicator curved time parameter can reflect the agglomeration character of the degradation condition at time dimension, which is more in line with the performance degradation pattern of mechanical equipment. The Gath–Geva fuzzy clustering algorithmic is able to cluster degradation condition of mechanical equipment such as bearings accurately.

Download Full-text

Weak Fault Detection of Tapered Rolling Bearing Based on Penalty Regularization Approach

Algorithms ◽

10.3390/a11110184 ◽

2018 ◽

Vol 11 (11) ◽

pp. 184 ◽

Cited By ~ 2

Author(s):

Qing Li ◽

Steven Liang

Keyword(s):

Vibration Signal ◽

Singular Values ◽

Rolling Bearing ◽

L1 Norm ◽

Detection Approach ◽

The Matrix ◽

Noisy Observation ◽

Weak Fault ◽

Convex Regularization ◽

Fault Information

Aimed at the issue of estimating the fault component from a noisy observation, a novel detection approach based on augmented Huber non-convex penalty regularization (AHNPR) is proposed. The core objectives of the proposed method are that (1) it estimates non-zero singular values (i.e., fault component) accurately and (2) it maintains the convexity of the proposed objective cost function (OCF) by restricting the parameters of the non-convex regularization. Specifically, the AHNPR model is expressed as the L1-norm minus a generalized Huber function, which avoids the underestimation weakness of the L1-norm regularization. Furthermore, the convexity of the proposed OCF is proved via the non-diagonal characteristic of the matrix BTB, meanwhile, the non-zero singular values of the OCF is solved by the forward–backward splitting (FBS) algorithm. Last, the proposed method is validated by the simulated signal and vibration signals of tapered bearing. The results demonstrate that the proposed approach can identify weak fault information from the raw vibration signal under severe background noise, that the non-convex penalty regularization can induce sparsity of the singular values more effectively than the typical convex penalty (e.g., L1-norm fused lasso optimization (LFLO) method), and that the issue of underestimating sparse coefficients can be improved.

Download Full-text

A fault diagnosis method based on Auxiliary Classifier Generative Adversarial Network for rolling bearing

PLoS ONE ◽

10.1371/journal.pone.0246905 ◽

2021 ◽

Vol 16 (3) ◽

pp. e0246905

Author(s):

Chunming Wu ◽

Zhou Zeng

Keyword(s):

Fault Diagnosis ◽

Noise Pollution ◽

Vibration Signal ◽

Rolling Bearing ◽

Rotating Machinery ◽

Generative Adversarial Network ◽

High Background ◽

Bearing Fault Diagnosis ◽

Adversarial Network ◽

Practical Engineering

Rolling bearing fault diagnosis is one of the challenging tasks and hot research topics in the condition monitoring and fault diagnosis of rotating machinery. However, in practical engineering applications, the working conditions of rotating machinery are various, and it is difficult to extract the effective features of early fault due to the vibration signal accompanied by high background noise pollution, and there are only a small number of fault samples for fault diagnosis, which leads to the significant decline of diagnostic performance. In order to solve above problems, by combining Auxiliary Classifier Generative Adversarial Network (ACGAN) and Stacked Denoising Auto Encoder (SDAE), a novel method is proposed for fault diagnosis. Among them, during the process of training the ACGAN-SDAE, the generator and discriminator are alternately optimized through the adversarial learning mechanism, which makes the model have significant diagnostic accuracy and generalization ability. The experimental results show that our proposed ACGAN-SDAE can maintain a high diagnosis accuracy under small fault samples, and have the best adaptation performance across different load domains and better anti-noise performance.

Download Full-text

Deep Transfer Learning Method Based on 1D-CNN for Bearing Fault Diagnosis

Shock and Vibration ◽

10.1155/2021/6687331 ◽

2021 ◽

Vol 2021 ◽

pp. 1-16

Author(s):

Jun He ◽

Xiang Li ◽

Yong Chen ◽

Danfeng Chen ◽

Jing Guo ◽

...

Keyword(s):

Fault Diagnosis ◽

Transfer Learning ◽

Vibration Signal ◽

Rolling Bearing ◽

Learning Method ◽

Target Domain ◽

Source Domain ◽

Second Order Statistics ◽

Bearing Fault ◽

Bearing Fault Diagnosis

In mechanical fault diagnosis, it is impossible to collect massive labeled samples with the same distribution in real industry. Transfer learning, a promising method, is usually used to address the critical problem. However, as the number of samples increases, the interdomain distribution discrepancy measurement of the existing method has a higher computational complexity, which may make the generalization ability of the method worse. To solve the problem, we propose a deep transfer learning method based on 1D-CNN for rolling bearing fault diagnosis. First, 1-dimension convolutional neural network (1D-CNN), as the basic framework, is used to extract features from vibration signal. The CORrelation ALignment (CORAL) is employed to minimize marginal distribution discrepancy between the source domain and target domain. Then, the cross-entropy loss function and Adam optimizer are used to minimize the classification errors and the second-order statistics of feature distance between the source domain and target domain, respectively. Finally, based on the bearing datasets of Case Western Reserve University and Jiangnan University, seven transfer fault diagnosis comparison experiments are carried out. The results show that our method has better performance.

Download Full-text

Vibration signal modeling of a localized defective rolling bearing under unbalanced force excitations

Journal of Vibroengineering ◽

10.21595/jve.2017.18406 ◽

2017 ◽

Vol 19 (7) ◽

pp. 5009-5019

Author(s):

Qinkai Han ◽

Jun Fan ◽

Wei Cui

Keyword(s):

Vibration Signal ◽

Rolling Bearing ◽

Signal Modeling ◽

Unbalanced Force

Download Full-text

A blind deconvolution algorithm based on backward automatic differentiation and its application to rolling bearing fault diagnosis

Measurement Science and Technology ◽

10.1088/1361-6501/ac3fc7 ◽

2021 ◽

Author(s):

Bo Fang ◽

Hu Jianzhong ◽

Cheng Yang ◽

Yudong Cao ◽

Minping Jia

Keyword(s):

Fault Diagnosis ◽

Automatic Differentiation ◽

Blind Deconvolution ◽

Vibration Signal ◽

Rolling Bearing ◽

Rolling Bearings ◽

Convex Optimization Problem ◽

Bearing Fault Diagnosis ◽

Filter Performance ◽

Calculation Process

Abstract Blind deconvolution (BD) is an effective algorithm for enhancing the impulsive signature of rolling bearings. As a convex optimization problem, the existing BDs have poor optimization performance and cannot effectively enhance the impulsive signature excited by weak faults. Moreover, the existing BDs require manual derivation of the calculation process, which brings great inconvenience to the researcher's personalized design of the maximization criterion. A new BD algorithm based on backward automatic differentiation (BAD) is proposed, which is named BADBD. The calculation process does not require manual derivation so a general solution of BDs based on different maximization criteria is realized. BADBD constructs multiple cascaded filters to filter the raw vibration signal, which makes up for the deficiency of single filter performance. The filter coefficients are determined by Adam algorithm, which improves the optimization performance of the proposed BADBD. BADBD is compared with classic BDs by synthesized and real vibration signals. The results reveal superior capability of BADBD to enhance the impulsive signature and the fault diagnosis performance is significantly better than the classic BDs.

Download Full-text

A New Robust Rolling Bearing Vibration Signal Analysis Method

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering - Advanced Hybrid Information Processing ◽

10.1007/978-3-319-73317-3_17 ◽

2018 ◽

pp. 137-145

Author(s):

Jingchao Li ◽

Yulong Ying ◽

Guoyin Zhang ◽

Zhimin Chen

Keyword(s):

Signal Analysis ◽

Vibration Signal ◽

Rolling Bearing ◽

Analysis Method ◽

Bearing Vibration ◽

Vibration Signal Analysis ◽

Signal Analysis Method

Download Full-text

Real-time fault diagnosis and trend prediction of rolling bearings based on resampling dynamic time warping and time-domain indicator analysis

Insight - Non-Destructive Testing and Condition Monitoring ◽

10.1784/insi.2022.64.1.38 ◽

2022 ◽

Vol 64 (1) ◽

pp. 38-44

Author(s):

Maosheng Gao ◽

Zhiwu Shang ◽

Wanxiang Li ◽

Shiqi Qian ◽

Yan Yu

Keyword(s):

Real Time ◽

Time Domain ◽

Dynamic Time Warping ◽

Prediction Method ◽

Vibration Signal ◽

Rolling Bearing ◽

Time Warping ◽

Trend Prediction ◽

Residual Signal ◽

Dynamic Time

A sudden fault in a rolling bearing (RB) results in a large amount of downtime, which increases the cost of operation and maintenance. In this paper, a real-time diagnosis and trend prediction method for RBs is proposed. In this method, a novel resampling dynamic time warping (RDTW) algorithm is presented and two new time-domain indicators (NTDIRs) called TALAP and TRCKT are defined, which can describe the wear degree and trend of an RB inner ring wear fault (IRWF). TALAP and TRCKT are proposed by comprehensively considering the stability and sensitivity of existing time-domain indicators (TDIRs). First, RDTW is used to align the healthy vibration signal with the fault vibration signal. Then, the residual signal that can be used to monitor the running condition is obtained. TALAP and TRCKT of the residual signal are calculated to judge the degree of wear. When the wear limit is reached, a fault alarm is sent out and the downtime needed for replacement can be accurately indicated. The experimental results show that the method can perform accurate diagnosis and trend prediction of inner ring wear faults of RBs.

Download Full-text

Fault diagnosis of rolling bearing based on resonance-based sparse signal decomposition with optimal Q-factor

Measurement and Control ◽

10.1177/0020294019858181 ◽

2019 ◽

Vol 52 (7-8) ◽

pp. 1111-1121 ◽

Cited By ~ 1

Author(s):

Yan Lu ◽

Juan Du ◽

Xian Tao

Keyword(s):

Energy Operator ◽

Vibration Signal ◽

Rolling Bearing ◽

Signal Decomposition ◽

Sparse Signal ◽

Q Factor ◽

Component Mixture ◽

Model Decomposition ◽

Periodic Impulse ◽

Sparse Signal Decomposition

When a localized defect is induced, the vibration signal of rolling bearing always consists periodic impulse component accompanying with other components such as harmonic interference and noise. However, the incipient impulse component is often submerged under harmonic interference and background noise. To address the aforementioned issue, an improved method based on resonance-based sparse signal decomposition with optimal quality factor ( Q-factor) is proposed in this paper. In this method, the optimal Q-factor is obtained first by genetic algorithm aiming at maximizing kurtosis value of low-resonance component of vibration signal. Then, the vibration signal is decomposed based on resonance-based sparse signal decomposition with optimal Q-factor. Finally, the low-resonance component is analyzed by empirical model decomposition combination with energy operator demodulating; the fault frequency can be achieved evidently. Simulation and application examples show that the proposed method is effective on extracting periodic impulse component from multi-component mixture vibration signal.

Download Full-text

Enhanced deep residual network with multilevel correlation information for fault diagnosis of rotating machinery

Journal of Vibration and Control ◽

10.1177/1077546320949719 ◽

2020 ◽

pp. 107754632094971 ◽

Cited By ~ 1

Author(s):

Shoucong Xiong ◽

Shuai He ◽

Jianping Xuan ◽

Qi Xia ◽

Tielin Shi

Keyword(s):

Deep Learning ◽

Fault Diagnosis ◽

Wavelet Packet ◽

Learning Algorithms ◽

Vibration Signal ◽

Rolling Bearing ◽

Classification Performance ◽

Residual Network ◽

Time Frequency ◽

Correlation Information

Modern machinery becomes more precious with the advance of science, and fault diagnosis is vital for avoiding economical losses or casualties. Among massive diagnosis methods, deep learning algorithms stand out to open an era of intelligent fault diagnosis. Deep residual networks are the state-of-the-art deep learning models which can continuously improve performance by deepening the network structures. However, in vibration-based fault diagnosis, the transient property instability of vibration signal usually calls for time–frequency analysis methods, and the characters of time–frequency matrices are distinct from standard images, which brings some natural limitations for the diagnosis performance of deep learning algorithms. To handle this issue, an enhanced deep residual network named the multilevel correlation stack-deep residual network is proposed in this article. Wavelet packet transform is used to preprocess the sensor signal, and then the proposed multilevel correlation stack-deep residual network uses kernels with different shapes to fully dig various kinds of useful information from any local regions of the processed input. Experiments on two rolling bearing datasets are carried out. Test results show that the multilevel correlation stack-deep residual network exhibits a more satisfactory classification performance than original deep residual networks and other similar methods, revealing significant potentials for realistic fault diagnosis applications.

Download Full-text