Multi-Scale Feature Fusion and Ensemble Learning for Liquid Rocket Engine Fault Diagnosis

Abstract

Limited labeled data and overlapping multisensor signatures make liquid rocket engine fault classification challenging, particularly when feature fusion introduces redundant information. This study proposes a multi-scale feature fusion framework with a calibrated convex ensemble (CCE). A 684-dimensional multi-window statistical representation is fused with a 32-dimensional temporal bottleneck learned through multi-scale convolution, channel attention, and bidirectional long short-term memory. Random-forest-based recursive feature elimination selects features within training partitions, while calibrated predictions from four heterogeneous classifiers are aggregated using simplex-constrained weights. Evaluation focuses on 1106 simulated sequences representing nine known faults in XJTU-REF; the three healthy sequences are excluded from the primary task. Five repetitions of five-fold nested cross-validation yield an accuracy of 90.16 ± 1.04%, a macro-F1 score of 89.84 ± 1.08%, and a Matthews correlation coefficient of 0.891 ± 0.012 (mean ± standard deviation across outer-test folds). Accuracy exceeds that of the random-forest baseline using mean features from the final 20% of each sequence by 2.45 percentage points. Ablations support the complementary roles of the two representations and feature selection, while calibration reduces expected calibration error from 7.18% to 3.76% relative to the uncalibrated ensemble. Leave-one-unit-out accuracy is 86.26 ± 1.21% across three simulated units, indicating lower performance on unseen units than under sample-level validation. These findings support complementary feature fusion with dimensionality control and calibrated aggregation for closed-set fault classification, while healthy-state recognition and real-engine applicability require further validation.