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Author

Shengnan Tang

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Review Open access Sep 2026

Artificial Intelligence Powered End-to-End Agricultural Machinery: Intelligent Transformation of Design and Maintenance Systems

Reliable condition monitoring and fault diagnosis in agricultural machinery depend on interpreting sensor measurements under variable field conditions. Noise, occlusion, changing loads, and sensor degradation can obscure relevant features, while complementary measurements offer opportunities to reduce information gaps....

Sheng-Nan Tang, Xu-Hua Chen, Yong Zhu · 0 citations
Review Open access Aug 2026

Explainable Artificial Intelligence in Rotating Machinery Fault Diagnosis: A Comprehensive Review and Emerging Trends

Examining explainable artificial intelligence for rotating machinery fault diagnosis classifies existing methods into ante hoc and post hoc approaches according to their integration with model architectures according to physical interpretability, applicable fault scenarios, explanation quality, computational overhead,...

Sheng-Nan Tang, Zeng-Yu Ren, Lei-Qi Zheng et al. · 0 citations

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