Remaining Useful Life Prediction of Rolling Bearings Based on Segmented Relative Phase Space Warping and Particle Filter

Liu, Hengyu; Yuan, Rui; Lv, Yong; Li, Hewenxuan; Gedikli, Ersegun Deniz; Song, Gangbing

doi:10.1109/tim.2022.3214623

Cited by 18 publications

(14 citation statements)

References 51 publications

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“…Faulty rolling bearings can result in substantial economic losses for factories and pose a significant threat to the safety of workers [1,2]. In light of these concerns, achieving fast and precise rolling bearing fault diagnosis is of utmost practical significance to maintain the uninterrupted operation of mechanical equipment [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

A novel fault diagnosis approach of rolling bearing using intrinsic feature extraction and CBAM-enhanced InceptionNet

Yuan

et al. 2023

Meas. Sci. Technol.

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Rolling bearings play a crucial role as components in mechanical equipment. Malfunctioning rolling bearings can disrupt the normal operation of the equipment and pose safety hazards. Traditional deep learning (DL)-based methods for diagnosing faults in rolling bearings present several issues, such as insufficient feature information of fault samples, high model complexity and low accuracy. To overcome these challenges, this paper introduces an intelligent approach for rolling bearing fault diagnosis using intrinsic feature extraction (IFE) and convolutional block attention module (CBAM)-enhanced InceptionNet. In our researches, variational mode decomposition (VMD) is adopted to decompose the original signal into multiple band-limited intrinsic mode functions (BLIMFs). In the decomposition process of VMD, the number of decomposition layers k is determined by center frequency method and the optimal BLIMF is chosen based on minimum envelope entropy. Subsequently, the continuous wavelet transform (CWT) is employed to transform the optimal BLIMF into time-frequency images. Finally, the obtained time-frequency images are fed into the proposed CBAM-enhanced InceptionNet for fault state diagnosis. Experiments on two different datasets prove that the method has stable and reliable accuracy. Comparative experiments have demonstrated that this method can reduce network model parameters and improve diagnosis efficiency while achieving high accuracy.

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Section: Introductionmentioning

confidence: 99%

A novel fault diagnosis approach of rolling bearing using intrinsic feature extraction and CBAM-enhanced InceptionNet

Yuan

et al. 2023

Meas. Sci. Technol.

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“…With the development of neural network technology, deep learning is widely employed in structural health monitoring (SHM) and other fields [21][22][23]. It learns and represents data spontaneously and reduces human intervention.…”

Section: Introductionmentioning

confidence: 99%

An efficient robotic-assisted bolt-ball joint looseness monitoring approach using CBAM-enhanced lightweight ResNet

Li,

Yuan,

et al. 2023

Smart Mater. Struct.

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Bolt-ball joints are widely used in space structures, and their looseness may lead to major safety accidents. The current bolt monitoring methods based on deep learning usually have high computational complexity, and it is difficult to guarantee its computational efficiency under practical scenario. To mitigate this problem, here in this paper, an efficient robotic-assisted bolt-ball joint looseness monitoring approach using convolutional block attention module (CBAM)-enhanced Lightweight ResNet is proposed. Firstly, the robotic-assisted tapping method is applied to bolt-ball joints to generate audio signals, which are constructed into time-frequency maps by continuous wavelet transform (CWT). Secondly, the original ResNet is improved as a lightweight network, which successfully reduces model complexity, and employs time-frequency maps as input. Then, CBAM is introduced to capture global information and focus on the critical feature. Thus, the efficiency of feature extraction is significantly improved. Finally, by the overall optimized structure, a CBAM-enhanced Lightweight ResNet model is established to monitor the bolt-ball joints looseness state accurately. Experimental results demonstrate the high efficiency while maintaining very lightweight structure of the proposed approach, verifying the effectiveness and superiority of the robot-assisted approach using CBAM-enhanced lightweight ResNet over other methods.

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“…36 Several modifications are proposed to improve the damage tracking quality for mechanical applications by incorporating autoregressive modeling, 37 comprehensive mutual information, 38 and particle filters. 39 However, the PSW algorithm lacks a solid theoretical foundation and the corresponding sampling theory. PSW already requires many input parameters that make damage monitoring unreliable when using nonoptimal parameter sets.…”

Section: Introductionmentioning

confidence: 99%

Geometry-informed phase space warping for reliable fatigue damage monitoring

Chelidze

2023

Structural Health Monitoring

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This paper presents a new fatigue damage detection and monitoring approach using a geometry-informed implementation of phase space warping (PSW). The proposed method is based on continuous-time PSW theory and geometric constructs, which clarifies the relationship between the deformation of the reconstructed phase flow and the underlying damage evolution. A discrete-time approximation to the continuous-time theory is established for practical applications. The practical geometry-informed PSW (GIPSW) algorithm is developed with integrated geometry-informed heuristics and global sensitivity analysis to monitor fatigue damage evolution accurately. The proposed method is validated through numerical experiments simulating nonlinear systems with varying fatigue damage dynamics, exhibiting distinct response complexities. The results show that the GIPSW improves the monitoring accuracy by at least 41.4% and can achieve maximally four-orders-of-magnitude-lower monitoring error compared with the conventional PSW algorithm. The GIPSW is also applied in physical experiments exploring raster-angle-affected fatigue damage dynamics in 3D-printed materials. The estimated hidden-fatigue damage-time history reveals distinct crack propagation rates differentiated by the raster angles and can be used for damage prognosis and modeling the fatigue mechanisms. The critical inflection points identified in the incremental damage-time histories detect the crack growth phase transitions as early as 0.17 of the total time to failure, which can be used for early damage awareness.

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Remaining Useful Life Prediction of Rolling Bearings Based on Segmented Relative Phase Space Warping and Particle Filter

Cited by 18 publications

References 51 publications

A novel fault diagnosis approach of rolling bearing using intrinsic feature extraction and CBAM-enhanced InceptionNet

A novel fault diagnosis approach of rolling bearing using intrinsic feature extraction and CBAM-enhanced InceptionNet

An efficient robotic-assisted bolt-ball joint looseness monitoring approach using CBAM-enhanced lightweight ResNet

Geometry-informed phase space warping for reliable fatigue damage monitoring

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