A noise reduction method of rolling bearing based on empirical wavelet transform and adaptive time frequency peak filtering

In engineering practice, the collection of equipment vibration signals is prone to interference from the external environment, resulting in abnormal data and imbalanced data in different states. Traditional support vector machine (SVM), support matrix machine (SMM) and other methods have advantages in balancing sample classification, but have limitations in obtaining low rank information, making it difficult to perform classification tasks under data imbalance. Therefore, a novel classification method that targets matrices as the input, called flexible dynamic matrix machine (FDMM), is proposed in this paper. First, FDMM establishes a regularization term using a flexible low-rank operator and sparse constrain, which can better take into account matrix structure information. Then, the upper bound of the loss function is truncated, reducing the impact of the loss on the construction of the decision hyperplane. Finally, the recognition performance of imbalanced data is improved by adjusting the game values of different categories of samples through dynamic adjustment function. Experimental results demonstrate that superior classification accuracy and generalization performance can be achieved with the FDMM method when applied to two roller bearing datasets.

“…(1) The vibration signals of the roller bearings are obtained by measuring them with sensors and collecting them using a data acquisition system [33].…”

Section: Experimental Analysismentioning

confidence: 99%

Research on roller bearing fault diagnosis method based on flexible dynamic adjustable strategy under data imbalance

Pan,

Li,

Yan

et al. 2024

“…Since the optimization algorithm has randomness, the result of each iteration will be different, so the optimal parameter combination [α, K] is obtained by averaging the results after 20 times of optimization in this paper. Among them, the value range of penalty factor α is [0,3000], the value range of decomposition modulus K is [3,8] and is an integer, and the maximum number of iterations is 20. The optimization results are shown in table 3.…”

Section: Parameter Optimization Based On Ssa-vmdmentioning

confidence: 99%

“…[7] applies signal sparse decomposition to reduce noise in vibration signals, yet the method requires an overcomplete dictionary, leading to excessive computational complexity. A vibration signal noise reduction method based on empirical wavelet transform and adaptive time-frequency peak filtering has been proposed in the [8]. However, the effectiveness of wavelet noise reduction is highly dependent on the selection of wavelet base, number of decomposition layers, and other parameters.…”

Section: Introductionmentioning

confidence: 99%

Research on circuit breaker operating mechanism feature extraction method combining ICEEMDAN-MRSVD denoising and VMD-PSE

Yan,

Zhuang,

2024

The vibration signal associated with the operating process of circuit breakers(CBs) includes a detailed operating status in the formation of the operating mechanism. To effectively extract the characteristic information of vibration effectively for diagnosis and analysis, a new feature extraction method for the CBs operating mechanism is proposed. First, a new denoising method, the improved complete ensemble empirical mode decomposition with adaptive noise-multi-resolution singular value decomposition (ICEEMDAN-MRSVD), is introduced, which can effectively remove the influence of noise on faults. Then, a quantitative method is proposed to extract the characteristic information of the CB, i.e. the variational mode decomposition (VMD)-power spectrum entropy (PSE) is proposed. By using this method, the difference of CB vibration signals in different fault states can be quantified. Through comparative analysis of different recognition models, experiments show that the support vector machine model based on ICEEMDAN-MRSVD noise reduction and VMD-PSE features has a high recognition accuracy of 98.61%, which has high application value.

“…Rotating machinery has been widely employed in various fields, such as aerospace, wind turbine, and high-speed rail transportation [1,2]. Equipment in these fields typically operates under harsh conditions characterized by high speed, heavy load, and changing operating parameters [3], which may cause abnormal situations or even catastrophic failures [4].…”

Section: Introductionmentioning

confidence: 99%

Quantitative condition assessment method for rotating machinery using fuzzy neural network

Lu,

Zhang,

Cui

et al. 2024

Self Cite

Health condition assessment of rotating machinery has been a persistent challenge. Traditional condition assessment methods often rely on single features, limiting their application to comprehensively measure the health condition of rotating machinery. This study introduced a quantitative condition assessment method for rotating machinery using fuzzy neural network (FNN). Initially, multi-domain features of signals from rotating machinery are extracted to achieve comprehensive representation of signals in the feature space. To eliminate redundant information of various features, a feature dimensionality reduction method is explored based on variance variation and stacked auto-encoder (SAE). Afterward, a normalized health indicator is constructed by integrating the optimized features through FNN, and it can indicate the current conditions of rotating machinery. Furthermore, an early anomaly alarm strategy based on 3σ criterion is designed for rotating machinery. The abnormal signal will be recognized automatically when it exceeds the predetermined thresholds. Last, the effectiveness of the proposed method is validated on IMS bearing dataset and XJTU-SY bearing dataset. The results show that the proposed method can effectively obtain the quantitative indicators that reflect the operation conditions of rotating machinery and can accurately detect the early abnormal signals.