Empirical Evaluation of Normalized Temperature-Scaled Cross-Entropy Pre-training in a Complete Ensemble Empirical Mode Decomposition with Adaptive Noise–Convolutional Neural Network–Bidirectional Long Short-Term Memory–Cross-Attention Framework for Unmanned Surface Vehicle Diesel Engine Fault Diagnosis

Abstract

Reliable diagnosis of unmanned surface vehicle (USV) diesel-engine faults requires representations that preserve multiscale vibration content and crank-angle-dependent context. Therefore, a CEEMDAN-CNN-BiLSTM-Cross-Attention framework is constructed, and NT-Xent pre-training is evaluated without presuming that it provides a performance benefit. CEEMDAN generates eight multiscale input channels, a CNN encodes local vibration transients, and the BiLSTM represents within-cycle context. The Cross-Attention uses CNN features as queries and BiLSTM features as keys and values. Six health states recorded on a laboratory diesel-engine platform at 1800 r/min and 80% load are used for experiments. The acquisition order defines the training, validation, and test subsets containing 2100, 450, and 450 cycles, respectively. Across five paired seeds, the Cross-Attention model with NT-Xent pre-training achieve 99.16 ± 0.40% accuracy and 99.15 ± 0.40% macro-F1 on clean test data. Five-fold acquisition-ordered block evaluation yield 99.07 ± 0.32% for both accuracy and macro-F1. Matched protocols, paired statistics, nested ablations, reduced-data tests, and additive-noise tests are used to separate overall framework performance from incremental component effects. NT-Xent pre-training increase clean-test accuracy by +0.09 percentage points relative to supervised training alone (95% CI; -0.95 to 1.13; p = 0.825). The Cross-Attention model scores 0.49 percentage points lower than the without-attention model (p = 0.207). Ordinary instance-level NT-Xent also performs worse at training-data fractions of 1%, 5%, and 10%. Accuracy is fell to 17.56 ± 1.14% at 12 dB, which is close to the chance level for six classes. The framework therefore accurately classifies clean test cycles under a fixed-operating condition , but neither the Cross-Attention model nor NT-Xent pre-training produces a reliable incremental benefit. These findings define the validated scope of the framework. Its performance under different operating conditions, across independent sessions and engines, and in real marine environments remains to be tested independently.

Conflict of Interest Statement

The authors declare no conflicts of interest.