1
|
Xu M, Zheng C, Sun K, Xu L, Qiao Z, Lai Z. Stochastic Resonance with Parameter Estimation for Enhancing Unknown Compound Fault Detection of Bearings. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23083860. [PMID: 37112201 PMCID: PMC10145098 DOI: 10.3390/s23083860] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 04/03/2023] [Accepted: 04/05/2023] [Indexed: 06/12/2023]
Abstract
Although stochastic resonance (SR) has been widely used to enhance weak fault signatures in machinery and has obtained remarkable achievements in engineering application, the parameter optimization of the existing SR-based methods requires the quantification indicators dependent on prior knowledge of the defects to be detected; for example, the widely used signal-to-noise ratio easily results in a false SR and decreases the detection performance of SR further. These indicators dependent on prior knowledge would not be suitable for real-world fault diagnosis of machinery where their structure parameters are unknown or are not able to be obtained. Therefore, it is necessary for us to design a type of SR method with parameter estimation, and such a method can estimate these parameters of SR adaptively by virtue of the signals to be processed or detected in place of the prior knowledge of the machinery. In this method, the triggered SR condition in second-order nonlinear systems and the synergic relationship among weak periodic signals, background noise and nonlinear systems can be considered to decide parameter estimation for enhancing unknown weak fault characteristics of machinery. Bearing fault experiments were performed to demonstrate the feasibility of the proposed method. The experimental results indicate that the proposed method is able to enhance weak fault characteristics and diagnose weak compound faults of bearings at an early stage without prior knowledge and any quantification indicators, and it presents the same detection performance as the SR methods based on prior knowledge. Furthermore, the proposed method is more simple and less time-consuming than other SR methods based on prior knowledge where a large number of parameters need to be optimized. Moreover, the proposed method is superior to the fast kurtogram method for early fault detection of bearings.
Collapse
Affiliation(s)
- Min Xu
- Ningbo Cigarette Factory, China Tobacco Zhejiang Industry Co., Ltd., Ningbo 315040, China
| | - Chao Zheng
- Ningbo Cigarette Factory, China Tobacco Zhejiang Industry Co., Ltd., Ningbo 315040, China
| | - Kelei Sun
- Ningbo Cigarette Factory, China Tobacco Zhejiang Industry Co., Ltd., Ningbo 315040, China
| | - Li Xu
- Ningbo Cigarette Factory, China Tobacco Zhejiang Industry Co., Ltd., Ningbo 315040, China
| | - Zijian Qiao
- School of Mechanical Engineering and Mechanics, Ningbo University, Ningbo 315211, China
- Yangjiang Offshore Wind Power Laboratory, Yangjiang 529500, China
- State Key Laboratory of Performance Monitoring and Protecting of Rail Transit Infrastructure, East China Jiaotong University, Nanchang 330013, China
- Zhejiang Provincial Key Laboratory of Part Rolling Technology, Ningbo 315211, China
| | - Zhihui Lai
- Shenzhen Key Laboratory of High Performance Nontraditional Manufacturing, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China
| |
Collapse
|
2
|
Li C, Li S, Wang H, Gu F, Ball AD. Attention-based deep meta-transfer learning for few-shot fine-grained fault diagnosis. Knowl Based Syst 2023. [DOI: 10.1016/j.knosys.2023.110345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
|
3
|
Wang Z, Yang J, Guo Y, Gong T, Shan Z. Nonstationary feature extraction based on stochastic resonance and its application in rolling bearing fault diagnosis under strong noise background. THE REVIEW OF SCIENTIFIC INSTRUMENTS 2023; 94:015110. [PMID: 36725570 DOI: 10.1063/5.0121593] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 12/09/2022] [Indexed: 06/18/2023]
Abstract
When the load and speed of rotating machinery change, the vibration signal of rolling bearing presents an obvious nonstationary characteristic. Stochastic resonance (SR) mainly is convenient to analyze the stationary feature of vibration signals with high signal-to-noise ratio. However, it is difficult for SR to extract the nonstationary feature of rolling bearings under strong noise background. For one thing, the frequency change of nonstationary signals makes the occurrence of SR very difficult. For another, the features of rolling bearings are large parameters and further prevent the SR method from performing well. Therefore, combined with order analysis (OA), adaptive frequency-shift SR is presented in this paper. To solve the problem of frequency change, OA is used to convert the nonstationary feature into stationary feature, which resamples the nonstationary signal in the time domain to stationary signal in the angular domain. To solve the other problem, the frequency-shift method based on Fourier transform is adopted to move the fault feature frequency to low frequency, and thus SR is more likely to occur under small parameter conditions. The simulated and experimental results indicate that not only the amplitude of fault feature but also the signal-to-noise ratio is significantly improved. These demonstrate that the fault features of rolling bearing in variable speed conditions are extracted successfully.
Collapse
Affiliation(s)
- Zhile Wang
- Key Laboratory of Mine Mechanical and Electrical Equipment, School of Mechatronic Engineering, China University of Mining and Technology, Jiangsu, Xuzhou 221116, People's Republic of China
| | - Jianhua Yang
- Key Laboratory of Mine Mechanical and Electrical Equipment, School of Mechatronic Engineering, China University of Mining and Technology, Jiangsu, Xuzhou 221116, People's Republic of China
| | - Yu Guo
- Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Yunnan, Kunming 650500, People's Republic of China
| | - Tao Gong
- Key Laboratory of Mine Mechanical and Electrical Equipment, School of Mechatronic Engineering, China University of Mining and Technology, Jiangsu, Xuzhou 221116, People's Republic of China
| | - Zhen Shan
- Key Laboratory of Mine Mechanical and Electrical Equipment, School of Mechatronic Engineering, China University of Mining and Technology, Jiangsu, Xuzhou 221116, People's Republic of China
| |
Collapse
|
4
|
Guo X, Liu X, Królczyk G, Sulowicz M, Glowacz A, Gardoni P, Li Z. Damage Detection for Conveyor Belt Surface Based on Conditional Cycle Generative Adversarial Network. SENSORS 2022; 22:s22093485. [PMID: 35591175 PMCID: PMC9101271 DOI: 10.3390/s22093485] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 04/23/2022] [Accepted: 04/27/2022] [Indexed: 12/05/2022]
Abstract
The belt conveyor is an essential piece of equipment in coal mining for coal transportation, and its stable operation is key to efficient production. Belt surface of the conveyor is vulnerable to foreign bodies which can be extremely destructive. In the past decades, much research and numerous approaches to inspect belt status have been proposed, and machine learning-based non-destructive testing (NDT) methods are becoming more and more popular. Deep learning (DL), as a branch of machine learning (ML), has been widely applied in data mining, natural language processing, pattern recognition, image processing, etc. Generative adversarial networks (GAN) are one of the deep learning methods based on generative models and have been proved to be of great potential. In this paper, a novel multi-classification conditional CycleGAN (MCC-CycleGAN) method is proposed to generate and discriminate surface images of damages of conveyor belt. A novel architecture of improved CycleGAN is designed to enhance the classification performance using a limited capacity images dataset. Experimental results show that the proposed deep learning network can generate realistic belt surface images with defects and efficiently classify different damaged images of the conveyor belt surface.
Collapse
Affiliation(s)
- Xiaoqiang Guo
- School of Mechatronic Engineering, China University of Mining & Technology, Xuzhou 211006, China;
| | - Xinhua Liu
- School of Mechatronic Engineering, China University of Mining & Technology, Xuzhou 211006, China;
- Correspondence:
| | - Grzegorz Królczyk
- Faculty of Mechanical Engineering, Opole University of Technology, 45-758 Opole, Poland; (G.K.); (Z.L.)
| | - Maciej Sulowicz
- Department of Electrical Engineering, Cracow University of Technology, 31-155 Cracow, Poland; (M.S.); (A.G.)
| | - Adam Glowacz
- Department of Electrical Engineering, Cracow University of Technology, 31-155 Cracow, Poland; (M.S.); (A.G.)
| | - Paolo Gardoni
- Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA;
| | - Zhixiong Li
- Faculty of Mechanical Engineering, Opole University of Technology, 45-758 Opole, Poland; (G.K.); (Z.L.)
- Yonsei Frontier Lab, Yonsei University, Seoul 03722, Korea
| |
Collapse
|
5
|
Miriya Thanthrige USKP, Jung P, Sezgin A. Deep Unfolding of Iteratively Reweighted ADMM for Wireless RF Sensing. SENSORS (BASEL, SWITZERLAND) 2022; 22:3065. [PMID: 35459049 PMCID: PMC9028850 DOI: 10.3390/s22083065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 03/31/2022] [Accepted: 04/13/2022] [Indexed: 06/14/2023]
Abstract
We address the detection of material defects, which are inside a layered material structure using compressive sensing-based multiple-input and multiple-output (MIMO) wireless radar. Here, strong clutter due to the reflection of the layered structure's surface often makes the detection of the defects challenging. Thus, sophisticated signal separation methods are required for improved defect detection. In many scenarios, the number of defects that we are interested in is limited, and the signaling response of the layered structure can be modeled as a low-rank structure. Therefore, we propose joint rank and sparsity minimization for defect detection. In particular, we propose a non-convex approach based on the iteratively reweighted nuclear and ℓ1-norm (a double-reweighted approach) to obtain a higher accuracy compared to the conventional nuclear norm and ℓ1-norm minimization. To this end, an iterative algorithm is designed to estimate the low-rank and sparse contributions. Further, we propose deep learning-based parameter tuning of the algorithm (i.e., algorithm unfolding) to improve the accuracy and the speed of convergence of the algorithm. Our numerical results show that the proposed approach outperforms the conventional approaches in terms of mean squared errors of the recovered low-rank and sparse components and the speed of convergence.
Collapse
Affiliation(s)
| | - Peter Jung
- Institute of Communications and Information Theory, Technical University Berlin, 10587 Berlin, Germany;
- Data Science in Earth Observation, Technical University of Munich, 82024 Munich, Germany
| | - Aydin Sezgin
- Institute of Digital Communication Systems, Ruhr University Bochum, 44801 Bochum, Germany;
| |
Collapse
|
6
|
Rai A, Ahmad Z, Hasan MJ, Kim JM. A Novel Pipeline Leak Detection Technique Based on Acoustic Emission Features and Two-Sample Kolmogorov-Smirnov Test. SENSORS 2021; 21:s21248247. [PMID: 34960342 PMCID: PMC8708146 DOI: 10.3390/s21248247] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 12/01/2021] [Accepted: 12/08/2021] [Indexed: 11/16/2022]
Abstract
Pipeline leakage remains a challenge in various industries. Acoustic emission (AE) technology has recently shown great potential for leak diagnosis. Many AE features, such as root mean square (RMS), peak value, standard deviation, mean value, and entropy, have been suggested to detect leaks. However, background noise in AE signals makes these features ineffective. The present paper proposes a pipeline leak detection technique based on acoustic emission event (AEE) features and a Kolmogorov–Smirnov (KS) test. The AEE features, namely, peak amplitude, energy, rise-time, decay time, and counts, are inherent properties of AE signals and therefore more suitable for recognizing leak attributes. Surprisingly, the AEE features have received negligible attention. According to the proposed technique, the AEE features are first extracted from the AE signals. For this purpose, a sliding window was used with an adaptive threshold so that the properties of both burst- and continuous-type emissions can be retained. The AEE features form distribution that change its shape when the pipeline condition changes from normal to leakage. The AEE feature distributions for leak and healthy conditions were discriminated using the two-sample KS test, and a pipeline leak indicator (PLI) was obtained. The experimental results demonstrate that the developed PLI accurately distinguishes the leak and no-leak conditions without any prior leak information and it performs better than the traditional features such as mean, variance, RMS, and kurtosis.
Collapse
Affiliation(s)
- Akhand Rai
- School of Engineering and Applied Science, Ahmedabad University, Ahmedabad 380009, Gujarat, India;
| | - Zahoor Ahmad
- Department of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan 44610, Korea; (Z.A.); (M.J.H.)
| | - Md Junayed Hasan
- Department of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan 44610, Korea; (Z.A.); (M.J.H.)
| | - Jong-Myon Kim
- Department of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan 44610, Korea; (Z.A.); (M.J.H.)
- PD Technology Co., Ulsan 44610, Korea
- Correspondence: ; Tel.: +82-52-259-2217
| |
Collapse
|