The nearest neighbor selection of multivariate statistical projection analysis methods assumes locally constant probabilities. However, ignoring the non-uniform distributed characteristic of data causes information redundancy in data-intensive regions and insufficient information in data-sparse regions, leading to detection performance decline. In this study, a new weighted distance named Cam weighted distance is used to reselect the neighbors and consequently overcome the aforementioned limitation. An nonlinear industrial fault detection method based on KGLPP-Cam is developed. The proposed method can preserve not only global and local information but also orientation and adaptive scale to obtain the information of neighbors according to different surroundings. $T^2$ and $SPE$ statistics are calculated for fault detection. A change ratio function is constructed to select sensitive principal components adaptively and better describe the sensitivity of different projection directions for processing change information. The proposed method is examined through a numerical example and TE process.

多元统计投影分析方法的最近邻选择假设局部概率恒定。然而，忽略数据的非均匀分布特性会导致数据密集区域的信息冗余和数据稀疏区域的信息不足，导致检测性能下降。在这项研究中，一个名为 Cam 加权距离的新加权距离用于重新选择邻居，从而克服上述限制。开发了一种基于KGLPP-Cam的非线性工业故障检测方法。所提出的方法不仅可以保留全局和局部信息，还可以保留方向和自适应尺度，以根据不同的环境获取邻居的信息。${吨}^2$ 和 $秒磷乙$统计数据用于故障检测。构建变化比函数，自适应选择敏感主成分，更好地描述不同投影方向对变化信息处理的敏感性。通过数值示例和 TE 过程检验了所提出的方法。