Bearing Fault Diagnosis of Roadheader via Cross-modal Kernel Fusion-sphere Space Learning

SU Shuzhi; GUI Yang; MA Tianbing; ZHU Yanmin; WU Kanghui

doi:10.11999/JEIT260494

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SU Shuzhi, GUI Yang, MA Tianbing, ZHU Yanmin, WU Kanghui. Bearing Fault Diagnosis of Roadheader via Cross-modal Kernel Fusion-sphere Space Learning[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260494

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SU Shuzhi, GUI Yang, MA Tianbing, ZHU Yanmin, WU Kanghui. Bearing Fault Diagnosis of Roadheader via Cross-modal Kernel Fusion-sphere Space Learning[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260494

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Bearing Fault Diagnosis of Roadheader via Cross-modal Kernel Fusion-sphere Space Learning

doi: 10.11999/JEIT260494 cstr: 32379.14.JEIT260494

1.
School of Computer Science and Engineering, Anhui University of Science & Technology, Huainan 232001, China
2.
State Key Laboratory of Digital Intelligent Technology for Unmanned Coal Mining, Anhui University of Science & Technology, Huainan 232001, China
3.
School of Mechatronics Engineering, Anhui University of Science & Technology, Huainan 232001, China

Funds: The National Natural Science Foundation of China (52374155, 52574189, 52504161), Anhui Provincial Natural Science Foundation(2308085MF218), Natural Science Research Project of Anhui Educational Committee (2024AH050399)

Received Date: 2026-04-22
Accepted Date: 2026-06-15
Rev Recd Date: 2026-06-05

Available Online: 2026-06-23

Abstract

Abstract

Objective Traditional roadheader bearing fault diagnosis methods often struggle with high-dimensional and nonlinear multi-sensor data. They also fail to effectively perceive cross-modal, multi-scale fault information or integrate local and global structural features. To address these limitations, this paper proposes a Cross-modal Kernel Fusion-sphere Space Learning (CKFSL) method. By perceiving cross-modal multi-scale fault information, CKFSL extracts highly discriminative features from roadheader bearing cross-modal fault samples and improves diagnostic accuracy. Methods CKFSL first maps roadheader bearing cross-modal fault samples into a high-dimensional kernel space through implicit transformation. Dual extremal point anchoring and polar neighbor allocation mechanisms are then used to capture fault sample clusters with similar isomorphic information, forming kernel fusion-spheres. An adaptive binary partitioning strategy is designed according to the geometric span of internal fault samples. This strategy tightens isomorphic boundaries, constructs micro-neighbor kernel fusion-spheres, and achieves highly isomorphic manifold aggregation at the microscopic scale. A micro-neighbor kernel fusion-sphere space is further formed to re-evaluate local isomorphism (Fig. 1). To characterize wide-area topological correlations, a wide-area topological isomorphism constraint is proposed. This constraint constructs a wide-area dynamic isomorphism graph among micro-neighbor kernel fusion-spheres (Fig. 1). Finally, an objective optimization function is formulated within the space learning framework. It integrates local manifold isomorphism and wide-area topological correlations of roadheader bearing cross-modal fault samples, as shown in the CKFSL diagnostic flowchart (Fig. 2). The analytical solution for spatial projection is theoretically derived to obtain discriminative cross-modal kernel fusion-sphere space isomorphic features from roadheader bearing cross-modal fault samples. Results and Discussions CKFSL is first validated on the self-built AUST roadheader bearing cross-modal fault dataset, with the experimental platform shown in Fig. 3. The average recognition rates obtained with increasing numbers of training fault samples are shown in Fig. 4. On the AUST dataset, CKFSL achieves a recognition rate of 99.49% with only 70 training fault samples and reaches 100% as the number of training fault samples increases. Table 1 summarizes the standard deviations under different training fault sample sizes. The results show that CKFSL has the lowest standard deviation and stronger robustness than the other seven comparison algorithms. Three-dimensional fault feature distributions are shown in Fig. 5. The results confirm that CKFSL effectively separates highly overlapping fault samples into different clusters and reduces the boundary confusion observed in the comparison algorithms. To verify generalization capability, CKFSL is further evaluated on the public Paderborn dataset, with the experimental setup shown in Fig. 6. As shown in Fig. 7 and Fig. 8, CKFSL achieves a 100% average recognition rate across four complex fault categories. It also outperforms the comparison algorithms, which have difficulty exceeding an 85% recognition rate for the F4 fault category. Conclusions CKFSL effectively addresses the inability of traditional roadheader bearing fault diagnosis methods to perceive complex multi-scale fault information. By using the wide-area dynamic isomorphism graph learned in the micro-neighbor kernel fusion-sphere space, CKFSL integrates local manifold isomorphism with wide-area topological correlations of roadheader bearing cross-modal fault samples. This process enables CKFSL to extract highly discriminative cross-modal kernel fusion-sphere space isomorphic features. It improves the accuracy of roadheader bearing fault diagnosis and supports the reliability and continuous operation of roadheaders.
- Roadheader bearing,
- Fault diagnosis,
- Space learning,
- Multi-scale fault perception.

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References(22)

References

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