Incipient Fault Monitoring of Mechanical Equipment Based on LNS‐RQKECA Under Multiple Load Cases

Traditional online monitoring systems often rely on high‐quality data samples or external expert knowledge, and their model accuracy degrades significantly when equipment states deviate from expected conditions over time. In the preresearch stage of mechanical equipment, researchers face not only data scarcity and severe class imbalance but also performance degradation caused by nonstationary processes and varying operational conditions. To address these challenges, this paper proposes a novel fusion‐enhanced model based on local neighborhood standardization and recursive quadratic kernel entropy component analysis. The proposed LNS‐RQKECA framework constructs a data‐driven vibration signal analysis model for early fault detection in mechanical equipment under multiple load conditions, relying solely on historical data from normal operation without requiring external experience or prior knowledge. The local neighborhood standardization technique transforms the multiload problem into a single‐modal problem, enabling effective load‐case fusion. To counteract the feature dilution effect induced by sample fusion, the proposed quadratic kernel entropy component analysis employs advanced kernel‐based entropy optimization to enhance feature discriminability through deeper data mining. Additionally, a recursive updating mechanism ensures adaptive model adjustment, allowing the system to handle nonstationary load conditions dynamically. This proposed model is validated using both public datasets and real‐world operational data. Experimental results demonstrate that under multiload conditions, the proposed model achieves strong generalization capability and nonstationary process adaptability with minimal training data. Compared with the traditional method, the false missing rate and false alarm rate are reduced by 12% and 15%, respectively.