Time-Efficient Neural Architecture Search for Autonomous Underwater Vehicle Fault Diagnosis

• Published in: IEEE transactions on instrumentation and measurement Vol. 72; p. 1
• Main Authors: Pei, Shicheng, Wang, Huan, Han, Te
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Autonomous underwater vehicle; Autonomous underwater vehicles; Computer architecture; Data points; Diagnostic systems; Fault diagnosis; Feature extraction; Graphics processing units; Machine learning; Neural architecture search; Neural networks; Oceans; Searching; Sensors; Training
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2023.3327477

Autonomous underwater vehicle (AUV) can replace human to operate in complex underwater environment, so it must have the ability of self-fault diagnosis. Existing deep learning-based diagnostic methods have achieved excellent performance, but designing effective neural network structures is a time-consuming and difficult task. Although neural network architecture search (NAS) can automatically search effective neural network structures in a certain search space, NAS algorithms are usually slow and expensive. Therefore, this paper introduces a time-efficient NAS-based AUV fault diagnosis framework (TENAS-FD). TENAS-FD constructs a novel scoring algorithm that effectively gives a metric to characterize the performance of an untrained network. This metric is given based on the overlapping activation between data-points in the untrained network with different inputs. This allows TENAS-FD to search for superior network architectures in seconds on a single GPU. Experiments were conducted on a real AUV dataset and showed that TENAS-FD can quickly obtain excellent network architectures for AUV fault diagnosis and has better diagnostic performance compared to hand-designing models.