Incipient Fault Detection of Full Ceramic Ball Bearing Based on Modified Observer

Shi, Huaitao; Hou, Maxiao; Wu, Yuhou; Li, Baicheng

doi:10.1007/s12555-021-0167-0

Cited by 10 publications

(11 citation statements)

References 34 publications

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“…Because CNN is suitable for image classification, the model first converts a one-dimensional signal into a two-dimensional image, which can achieve 100% accuracy by using the bearing data set of Case Western Reserve University in the United States. It also has strong robustness and antinoise ability [ 8 ]. Petrov et al combine the historical data of wind turbine collected by the SCADA system with real-time data [ 9 ].…”

Section: Literature Reviewmentioning

confidence: 99%

“…. , p k ], if the target category is y i � c, then p c � 1 and other items are 0, and then the final objective function can be expressed as formula (8), where the expression 1 c � yi 􏼈 􏼉 indicates that if the condition in parentheses is true, then take 1; otherwise, it is 0. to train the model, and the testing process is used to measure the capabilities of the model. In addition, the design and configuration of ultratrivial systems also need to be improved through experimentation and debugging.…”

Section: Convolutional Neural Network Eorymentioning

confidence: 99%

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Fault Diagnosis of Wind Turbine Based on Convolution Neural Network Algorithm

Xiao

Wang

2022

Computational Intelligence and Neuroscience

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Relying on expert diagnosis, it solves the problem of fan failure efficiency and meets the needs of automatic inspection and intelligent operation monitoring of fans. In order to make up for the deficiency of intelligent diagnosis of bearing fault based on vibration signal detection, signal transformation, and convolution neural network identification and improve the ability of intelligent diagnosis, this study designs a deep convolution neural network model and diagnosis algorithm with three pairs of convolution pooling layers and two full connection layers. The experimental verification of the proposed method is carried out based on the public data set, and the effects of three different signal transformation methods based on vibration signal through vibration gray map, short-time Fourier transform time-frequency map, and continuous wavelet transform time-frequency map on the accuracy of diagnosis model are compared and analyzed. A very accurate guarantee is received, close to 100%. The final experimental results demonstrate the effectiveness of the information on the accuracy of diagnostic testing and provide new ideas for the verification and testing of wind turbine wind energy. Compared with other machine learning algorithms, the real-time recognition of machine learning based on time-domain statistical features is lower than that of convolutional neural network methods. The effect of the scale of the trained model on the accuracy of the algorithm is discussed. A sample ratio of 50% and 70% was found to be appropriate.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Convolutional Neural Network Eorymentioning

confidence: 99%

Fault Diagnosis of Wind Turbine Based on Convolution Neural Network Algorithm

Xiao

Wang

2022

Computational Intelligence and Neuroscience

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“…Therefore, accurate fault diagnosis is essential to prevent undue operational interruptions and minimize economic losses [1]. Shi et al [2] introduced a discrete-time model for ceramic bearings considering incipient faults, and proposed a modified observer with enhanced design freedom compared to traditional Luenberger observers. Gao et al [3] developed an ultra-high-speed hybrid ceramic rolling element triboelectric bearing to enable the real-time monitoring of dynamic behavior and stability.…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault Diagnosis Based on Parameter-Optimized Variational Mode Extraction and an Improved One-Dimensional Convolutional Neural Network

Zhang,

Tao

2024

Applied Sciences

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When faults occur in rolling bearings, vibration signals exhibit sensitivity to periodic impact components, susceptibility to complex background noise, and non-stationary and nonlinear characteristics. Consequently, using traditional signal processing methods to effectively identify bearing faults presents significant challenges. To facilitate the accurate fault diagnosis of bearings in noisy conditions, we propose an intelligent fault diagnosis method using the Archimedes optimization algorithm (AOA), coupled with a one-dimensional multi-scale residual convolutional neural network (1D-MRCNN), to optimize the variational mode extraction (VME) parameters. First, we introduce a weighted correlated kurtosis (WCK) indicator, formulated using the correlation coefficient and correlated kurtosis as the objective function, to optimize the VME’s center frequency ω and penalty factor α, enabling targeted signal extraction. Second, deep learning techniques are employed to construct the 1D-MRCNN. The neural network then processes the extracted signal for feature extraction and automated fault-type identification. Our simulation results show that the WCK objective function effectively isolates impact components under fault conditions, and our experimental validation confirms that the proposed method accurately identifies diverse fault types across multiple noise levels.

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“…Xu et al [32] established a two-dimensional thermal coupling model of bearings based on ANSYS and investigated the characteristics of the influence of rotational speed, lubricant temperature, and flow rate on the displacement of the inner and outer rings and raceway stress. Some studies have been conducted in recent years to analyze the dynamics [33,34], fault diagnosis [35][36][37][38], and condition monitoring [39,40] of the double-row tapered roller bearings (TRBs). However, these studies have not considered complex working environments.…”

Section: Introductionmentioning

confidence: 99%

Vibration-Based Detection of Axlebox Bearing Considering Inner and Outer Ring Raceway Defects

Liu,

Zhang,

Wang

et al. 2024

Lubricants

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The occurrence of an axlebox bearing ring raceway defect is an inevitable and commonly observed phenomenon in railway wheels. It not only leads to surface damage but also poses the potential threat of further damage and degradation, thereby increasing the risks associated with running safety and maintenance costs. Hence, it becomes imperative to detect raceway defects at an early stage to mitigate safety hazards and reduce maintenance efforts. In this study, the focus lies in investigating the effectiveness of vibration-based detection techniques for identifying raceway defects in high-speed train axlebox bearing systems. To achieve this, a dynamic model that accurately represents the coupling dynamics between the vehicle and the track is developed. This model incorporates various dynamic factors, such as traction transmission, gear transmission, and track geometry irregularities. By using the comprehensive dynamic model, the dynamic responses of the axlebox can be accurately calculated. The proposed methodology primarily revolves around analysing the vertical vibrations of the axlebox caused by raceway defects in both the time and frequency domains. Additionally, an envelope analysis using a developed band-pass filter is also employed. The results obtained from this study clearly demonstrate the successful detection of raceway defects in a more realistic vehicle model, thereby providing an efficient approach for the detection of axlebox bearing raceway defects. Consequently, this research contributes significantly to the field of high-speed train systems and paves the way for enhanced safety and maintenance practices.

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Incipient Fault Detection of Full Ceramic Ball Bearing Based on Modified Observer

Cited by 10 publications

References 34 publications

Fault Diagnosis of Wind Turbine Based on Convolution Neural Network Algorithm

Fault Diagnosis of Wind Turbine Based on Convolution Neural Network Algorithm

Bearing Fault Diagnosis Based on Parameter-Optimized Variational Mode Extraction and an Improved One-Dimensional Convolutional Neural Network

Vibration-Based Detection of Axlebox Bearing Considering Inner and Outer Ring Raceway Defects

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