The health assessment of the valve clearance is a key link to realize the failure prediction and health management of the valve mechanism. To accurately evaluate the state of valve clearance, this article proposes a diesel engine valve clearance degradation feature extraction method based on modified complete ensemble empirical mode decomposition with adaptive noise and discriminant correlation analysis feature fusion algorithm. First, we use modified complete ensemble empirical mode decomposition with adaptive noise to adaptively filter the cylinder head vibration signal. Then, power spectrum entropy and improved hierarchical dispersion entropy are proposed as degenerate feature entropy. To improve the sensitivity of the degraded feature entropy to the degraded state, the discriminant correlation analysis algorithm is used to fuse the two types of feature entropy to obtain fused degraded feature entropy. Finally, the degenerate fusion features are input into the least squares support vector machine to realize the health status assessment of the valve mechanism. Through the verification of test data, the results show that the proposed method can effectively evaluate the health state of the valve clearance of diesel engines.

气门间隙的健康评估是实现气门机构故障预测和健康管理的关键环节。为了准确评估气门间隙的状态，本文提出了一种基于自适应噪声和判别相关分析特征融合算法的改进的完全集成经验模态分解的柴油机气门间隙退化特征提取方法。首先，我们使用带有自适应噪声的改进的完整整体经验模式分解来自适应地过滤汽缸盖振动信号。然后，提出了功率谱熵和改进的层次色散熵作为退化特征熵。为了提高退化特征熵对退化状态的敏感性，判别相关分析算法用于融合两种类型的特征熵以获得融合的退化特征熵。最后，将退化的融合特征输入最小二乘支持向量机，以实现瓣膜机构的健康状态评估。通过对测试数据的验证，结果表明该方法可以有效地评估柴油机气门间隙的健康状态。