# Research on Data-Driven Performance Assessment and Fault Early Warning of Marine Diesel Engine
## 3 Performance Degradation Trend Analysis and Fault Early Warning of Marine Diesel Main Engines
### Performance Degradation Analysis and Fault Early Warning
#### 3.4.3 Fault Early Warning  
Fault early warning aims to provide alerts before a failure occurs. In practical navigation, if a warning is only issued after a significant change in the performance degradation trend, it may be too late. This paper proposes a fault early warning technique based on time-domain analysis. The core idea is to analyze the performance degradation curve of the ship's main engine, identify the time of the first abrupt change in the curve as the fault warning time, and analyze the cause of the anomaly to prevent potential failures.  
The cumulative anomaly curve method and Yamamoto detection method were employed for abrupt change identification in the Mahalanobis distance time series. The effectiveness of this technique was validated through experimental verification. As shown in Figure 10, between late February and March 2021, the outlet temperature of the jacket cooling water for cylinder #2 exhibited abnormal fluctuations, accompanied by a significant increase in performance variation. However, from late January to early February 2021, the performance degradation curve of the marine diesel engine already showed the first abrupt change, while the \"outlet temperature of the cylinder jacket cooling water for cylinder #2\" only slightly deviated from the normal mean without showing any significant anomaly. Therefore, by identifying the first abrupt change in the performance degradation curve during this period, fault warning can be effectively triggered.  
Implementing time-domain analysis for precise abrupt change identification involves a two-stage methodology: preliminary temporal range estimation via the cumulative anomaly method, followed by Yamamoto-based exact localization. The cumulative anomaly  
Figure 10: Comparison of overall performance with the outlet temperature of jacket cooling water for cylinder #2.  
Figure 9: Trend of the performance parameter “outlet temperature of jacket cooling water for cylinder #2”.  
curve provides visual detection of degradation trend transitions. As shown in Figure 11, the Mahalanobis distances exhibit progressive growth over time, accompanied by decreasing cumulative anomaly values--a pattern consistent with typical marine diesel main engine degradation. However, a marked inflection in the cumulative anomaly curve occurred in late January 2021, indicating an abrupt change in the degradation trajectory. This initial inflection phase was temporally bounded between January 23 and 26 February 2021.  
Subsequent refinement using the Yamamoto method entailed SNR calculations for performance sequences within this critical window, with results fitted to a validation curve (Figures 12 and 13). The detection threshold (SNR > 1.0) was first breached at 18:10 on 25 January 2021, establishing this timestamp as \"The definitive onset of degradation curve destabilization; The optimal fault early-warning trigger point\".  
Figure 11: Cumulative anomaly curve.  
Figure 12: Yamamoto test curve.  
Figure 13: Enlarged view of the abrupt change point in the Yamamoto test curve.