Multi-Feature Relational Modeling and Conditional-Memory-Augmented Anomaly Detection for Multi-Cylinder Diesel Engines Under Variable Operating Conditions

When multi-cylinder diesel engines operate under variable speed and load conditions, the distribution of normal vibration responses drifts with the operating conditions. This makes it difficult for diagnostic models to distinguish normal operating-condition fluctuations from genuine fault deviations, leading to false alarms or missed detections. Meanwhile, fault samples are usually limited in practical applications. To address these problems, this study proposes an anomaly detection method based on multi-feature relational modeling and conditional-memory augmentation. The method performs the cycle-wise alignment of multi-point vibration signals according to the firing phase of each cylinder. It integrates local waveform morphology, impact energy, and energy-centroid information in the non-uniform angular domain to construct a raw–relative dual relational representation. It further uses speed conditions to modulate latent features and employs a sparse normal memory to constrain reconstruction sources, enabling the model to learn normal relational patterns under different operating conditions using only normal samples. Tests involving misfire, intake-valve clearance anomaly, and exhaust-valve clearance anomaly were conducted on a TBD234V12 diesel-engine test bench. The proposed method achieved an accuracy, true positive rate (TPR), F1-score, and area under the receiver operating characteristic curve (AUROC) of 97.44%, 98.98%, 98.30%, and 98.88%, respectively, with a false-positive rate (FPR) of 7.21% under the sample-level alarm definition. The results show that the method reduces the interference of operating-condition-induced normal-pattern drift with anomaly determination and improves the accuracy of fault warning within the range of the operating conditions covered in this study.