Rolling bearing fault diagnosis method based on SSAE and softmax classifier with improved K-fold cross-validation

Wang, Junxiang; Zhan, Changshu; Yu, Di; Zhao, Qiancheng; Xie, Zhijie

doi:10.1088/1361-6501/ac7635

Cited by 12 publications

(6 citation statements)

References 41 publications

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“…Then, they used the k-means clustering algorithm to classify the loose particle diameter of the spacecraft. The combination of data feature optimisation (or feature engineering) with the machine learning method has not only been restricted to loose particle detection but also found in various research domains, making notable contributions [26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Research on a synchronised classification method for loose particle detection signals of aerospace-sealed electronic components

Li,

Zhai,

Wang

et al. 2024

Meas. Sci. Technol.

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Aerospace-sealed electronic components exhibit favourable anti-interference capability and high reliability and are widely utilised in satellites, rockets, and missiles. Loose particle detection is crucial to ensure high reliability. However, the classification problem of loose particle detection signals based on the Particle Impact Noise Detection (PIND) method has been a challenge for the high reliability of aerospace-sealed electronic components. To address this issue, this paper systematically proposes a synchronised classification method based on PIND and validates its feasibility. The proposed approach combines a self-developed empirical model (referred to as Algorithm 1) with a classical machine learning model (referred to as Algorithm 2). The key findings are as follows: this method has a recognition rate for loose particle signals of 91.86%. Compared to a single algorithmic mode, the overall speed improves by 300%. The maximal recognition accuracy rates for component signals, mixed signals, and excessive signals are 90.03%, 81.04%, and 95.17%, respectively. The test results demonstrate that the method effectively balances the accuracy and speed of loose particle detection and leverages the complementary advantages of the two algorithms, thereby addressing the multi-classification issue of loose particle detection signals.

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Section: Introductionmentioning

confidence: 99%

Research on a synchronised classification method for loose particle detection signals of aerospace-sealed electronic components

Li,

Zhai,

Wang

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…To evaluate the performance of the proposed method, accuracy, precision (P), recall (R), and F1-score were selected as the evaluation indices [ 29 , 30 ]. Accuracy represents the ratio of the number of correctly classified samples to the total number of samples, and its calculation formula is:

…”

Section: Analysis Of Experimental Resultsmentioning

confidence: 99%

Escalator Foundation Bolt Loosening Fault Recognition Based on Empirical Wavelet Transform and Multi-Scale Gray-Gradient Co-Occurrence Matrix

Wang

2023

Sensors

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An escalator is an essential large-scale public transport equipment; once it fails, this inevitably affects the operation of the escalator and even leads to safety concerns, or perhaps accidents. As an important structural part of the escalator, the foundation of the main engine can cause the operation of the escalator to become abnormal when its fixing bolts become loose. Aiming to reduce the difficulty of extracting the fault features of the footing bolt when it loosens, a fault feature extraction method is proposed in this paper based on empirical wavelet transform (EWT) and the gray-gradient co-occurrence matrix (GGCM). Firstly, the Teager energy operator and multi-scale peak determination are used to improve the spectral partitioning ability of EWT, and the improved EWT is used to decompose the original foundation vibration signal into a series of empirical mode functions (EMFs). Then, the gray-gradient co-occurrence matrix of each EMF is constructed, and six texture features of the gray-gradient co-occurrence matrix are calculated as the fault feature vectors of this EMF. Finally, the fault features of all EMFs are fused, and the degree of the loosening of the escalator foundation bolt is identified using the fused multi-scale feature vector and BiLSTM. The experimental results show that the proposed method based on EWT and GGCM feature extraction can diagnose the loosening degree of foundation bolts more effectively and has a certain engineering application value.

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“…In order to establish a correspondence between feature data and fault categories, the softmax algorithm is employed to normalize the output values, ensuring that they adhere to a probability distribution. The mathematical expression for softmax [49] is as follows:…”

Section: Fault Classification Based On Softmaxmentioning

confidence: 99%

A hybrid fault diagnosis method for rolling bearings based on GGRU-1DCNN with AdaBN algorithm under multiple load conditions

Sun,

Zhu,

Xiao

et al. 2024

Meas. Sci. Technol.

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The fault diagnosis of rolling bearings is a critical aspect of rotating machinery, as it significantly contributes to the overall operational safety of the mechanical equipment. In the practical engineering environment, the complex and variable working conditions, along with the presence of overlapping noise, contribute to intricate frequency information in the acquired signals and their highly time-dependent characteristics, which makes it difficult to extract the available fault features hidden in the signal. Based on this, a hybrid fault diagnosis method named GGRU-1DCNN-AdaBN is introduced, which combines improved gap-gated recurrent unit network(GGRU), one-dimensional convolutional neural network(1DCNN), and adaptive batch normalization(AdaBN). The proposed approach involves several parts to enhance fault diagnosis accuracy in vibration signals under constant load conditions and variable load conditions. Firstly, the end-layer structure of the traditional GRU is replaced with a one-dimensional global average pooling layer (1D-GAP) to aggregate the influence components of defects and reduce model training parameters. Secondly, the fusion of different types of frequency and sequence features is achieved by combining 1DCNN, addressing the limitation of a single network's feature extraction capability and the loss of temporal features in a cascaded hybrid model. Subsequently, the fused features are input into a softmax multi-classifier to obtain fault type identification results. Lastly, the GGRU-1DCNN method is further improved by incorporating the AdaBN algorithm, enhancing the model's domain adaptive capability under variable load conditions and noisy environments. The method is validated using datasets obtained from Case Western Reserve University (CWRU), aero-engine bearings, Xi'an Jiaotong University, and the Changxing Sumyoung Technology (XJTU-SY). The findings suggest that the proposed method demonstrates superior accuracy and robustness in fault diagnosis, as well as excellent generalization capability and universal applicability.

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Rolling bearing fault diagnosis method based on SSAE and softmax classifier with improved K-fold cross-validation

Cited by 12 publications

References 41 publications

Research on a synchronised classification method for loose particle detection signals of aerospace-sealed electronic components

Research on a synchronised classification method for loose particle detection signals of aerospace-sealed electronic components

Escalator Foundation Bolt Loosening Fault Recognition Based on Empirical Wavelet Transform and Multi-Scale Gray-Gradient Co-Occurrence Matrix

A hybrid fault diagnosis method for rolling bearings based on GGRU-1DCNN with AdaBN algorithm under multiple load conditions

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