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

Citation:

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

Accepted Date: 2026-06-15
Rev Recd Date: 2026-06-15

Available Online: 2026-06-23

Abstract

Abstract

Objective Traditional roadheader bearing fault diagnosis methods often struggle with high-dimensional and non-linear multi-sensor data, failing to effectively perceive cross-modal, multi-scale fault information or integrate local and global structural features. To address these limitations, this paper proposes a novel Cross-modal Kernel Fusion-sphere Space Learning (CKFSL) method. By perceiving cross-modal multi-scale fault information, the proposed method extracts highly discriminative features from roadheader bearing cross-modal fault samples, improving the accuracy of roadheader bearing fault diagnosis. Methods The CKFSL method first maps roadheader bearing cross-modal fault samples into a high-dimensional kernel space via implicit transformation. It employs dual extremal point anchoring and polar neighbor allocation mechanisms to capture clusters of fault samples with similar isomorphic information, forming kernel fusion-spheres. Subsequently, an adaptive binary partitioning strategy is designed based on the geometric span of internal fault samples to tighten isomorphic boundaries, constructing micro-neighbor kernel fusion-spheres and achieving highly isomorphic manifold aggregation at a 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, which 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, which integrates local manifold isomorphism and wide-area topological correlations of roadheader bearing cross-modal fault samples, as illustrated 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 The proposed 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 with increasing training fault samples are illustrated in Fig. 4. On the AUST dataset, CKFSL achieves a 99.49% recognition rate with only 70 training fault samples, reaching 100% as the number of training fault samples increases. Table 1 summarizes the standard deviations under different training fault sample sizes, demonstrating that CKFSL maintains the lowest standard deviation and superior robustness compared to the other seven algorithms. Furthermore, three-dimensional fault feature distributions are presented in Fig. 5, confirming that CKFSL effectively partitions highly overlapping fault samples into distinct clusters, overcoming the boundary confusion of compared algorithms. To verify the generalization capability, the proposed CKFSL method is further evaluated on the public Paderborn dataset, with the experimental setup shown in Fig. 6. As depicted in Fig. 7 and Fig. 8, CKFSL achieves 100% average recognition accuracy across four complex fault categories, significantly outperforming comparative methods that struggle to surpass an 85% recognition rate for the F4 fault category. Conclusions The proposed CKFSL effectively overcomes the inability of traditional roadheader bearing fault diagnosis methods to perceive complex multi-scale fault information. By utilizing the wide-area dynamic isomorphism graph learned within the micro-neighbor kernel fusion-sphere space, CKFSL integrates local manifold isomorphism and wide-area topological correlations of roadheader bearing cross-modal fault samples. This process enables the CKFSL to extract highly discriminative cross-modal kernel fusion-sphere space isomorphic features, thereby improving the accuracy of roadheader bearing fault diagnosis and ensuring the reliability and continuous operability of the roadheader.
- Roadheader bearing,
- Fault diagnosis,
- Space learning,
- Multi-scale fault perception.

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References(22)

References

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