Incipient fault detection is growing as a challenging and hot topic in industrial and academic areas. It is essential to avoid slight unpermitted changes of a system state that can be aggravated and lead to severe security issues. The main challenge of this problem lies in the fact that tiny changes in the early stage can be blurred with noise and create confusion leading to poor detection performance of typical fault detection methods. To detect subtle deviations buried in noise and cope with the non-Gaussian distributed data condition while keeping with the time series information, a sensitive fault detection methodology combining a specifically tuned Local Mahalanobis Distance (LMD) algorithm and an Empirical Probability Density (EPD) estimation technique is proposed. More specifically, first, a healthy domain estimation is proposed to compute the local Mahalanobis distance with optimally tuned characteristics. To approximate a healthy domain, this work proposes a down-sampling algorithm for anchors generation and a parameter estimation method optimally tuned and based on Generalized Extreme Value distribution (GEV) for the domain margin selection. Subsequently, the EPD cumulative sum technique is applied to the LMD result for improving the detection sensitivity further. The performance analysis based on simulation data shows that our proposal is effective to non-Gaussian data and sensitive for incipient fault detection. A case study based on the Continuous-flow Stirred Tank Reactor (CSTR) further validates the effectiveness of our proposal and highlights its benefit by comparing it with state-of-the-art-based solutions in terms of detection delay, detection probability, false alarm probability, and area under the receiver operating characteristic curve (AUC).

早期故障检测正在成为工业和学术领域的一个具有挑战性的热门话题。必须避免系统状态的轻微未经允许的更改，这可能会加剧并导致严重的安全问题。这个问题的主要挑战在于早期阶段的微小变化可能会被噪声模糊并造成混淆，从而导致典型故障检测方法的检测性能不佳。为了检测隐藏在噪声中的细微偏差并在保持时间序列信息的同时处理非高斯分布式数据条件，一种灵敏的故障检测方法结合了专门调整的局部马氏距离 (LMD) 算法和经验概率密度 (EPD) 估计技术提出。更具体地说，首先，提出了一种健康域估计来计算具有最佳调谐特性的局部马氏距离。为了近似一个健康的域，这项工作提出了一种用于锚生成的下采样算法和一种优化调整并基于广义极值分布（GEV）的参数估计方法，用于域边缘选择。随后，将EPD累积求和技术应用于LMD结果以进一步提高检测灵敏度。基于仿真数据的性能分析表明，我们的建议对非高斯数据有效，对早期故障检测敏感。