Diagnosing Faults of Pneumatic Soft Actuators Based on Multimodal Spatiotemporal Features and Ensemble Learning

Duan, Tao; Lv, Yi; Wang, Liyuan; Li, Haifan; Yi, Teng; He, Yigang; Lv, Zhongming

doi:10.3390/machines13080749

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Diagnosing Faults of Pneumatic Soft Actuators Based on Multimodal Spatiotemporal Features and Ensemble Learning

by

Tao Duan

^1,2,

Yi Lv

¹,

Liyuan Wang

²,

Haifan Li

¹,

Teng Yi

¹,

Yigang He

² and

Zhongming Lv

^2,*

¹

Hubei Technology Innovation Center for Smart Hydropower, Wuhan 430000, China

²

School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China

^*

Author to whom correspondence should be addressed.

Machines 2025, 13(8), 749; https://doi.org/10.3390/machines13080749

Submission received: 8 July 2025 / Revised: 19 August 2025 / Accepted: 20 August 2025 / Published: 21 August 2025

(This article belongs to the Section Machines Testing and Maintenance)

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Abstract

Soft robots demonstrate significant advantages in applications within complex environments due to their unique material properties and structural designs. However, they also face challenges in fault diagnosis, such as nonlinearity, time variability, and the difficulty of precise modeling. To address these issues, this paper proposes a fault diagnosis method based on multimodal spatiotemporal features and ensemble learning. First, a sliding-window Kalman filter is utilized to eliminate noise interference from multi-source signals, constructing separate temporal and spatial representation spaces. Subsequently, an adaptive weight strategy for feature fusion is applied to train a heterogeneous decision tree model, followed by a dynamic weighted voting mechanism based on confidence levels to obtain diagnostic results. This method optimizes the feature extraction and fusion process in stages, combined with a dynamic ensemble strategy. Experimental results indicate a significant improvement in diagnostic accuracy and model robustness, achieving precise identification of faults in soft robots.

Keywords: soft robotics; spatiotemporal features; ensemble learning; fault diagnosis; multimodal features

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MDPI and ACS Style

Duan, T.; Lv, Y.; Wang, L.; Li, H.; Yi, T.; He, Y.; Lv, Z. Diagnosing Faults of Pneumatic Soft Actuators Based on Multimodal Spatiotemporal Features and Ensemble Learning. Machines 2025, 13, 749. https://doi.org/10.3390/machines13080749

AMA Style

Duan T, Lv Y, Wang L, Li H, Yi T, He Y, Lv Z. Diagnosing Faults of Pneumatic Soft Actuators Based on Multimodal Spatiotemporal Features and Ensemble Learning. Machines. 2025; 13(8):749. https://doi.org/10.3390/machines13080749

Chicago/Turabian Style

Duan, Tao, Yi Lv, Liyuan Wang, Haifan Li, Teng Yi, Yigang He, and Zhongming Lv. 2025. "Diagnosing Faults of Pneumatic Soft Actuators Based on Multimodal Spatiotemporal Features and Ensemble Learning" Machines 13, no. 8: 749. https://doi.org/10.3390/machines13080749

APA Style

Duan, T., Lv, Y., Wang, L., Li, H., Yi, T., He, Y., & Lv, Z. (2025). Diagnosing Faults of Pneumatic Soft Actuators Based on Multimodal Spatiotemporal Features and Ensemble Learning. Machines, 13(8), 749. https://doi.org/10.3390/machines13080749

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Diagnosing Faults of Pneumatic Soft Actuators Based on Multimodal Spatiotemporal Features and Ensemble Learning

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