Monitoring complex industrial plants is a very important task in order to ensure the
management, reliability, safety and maintenance of the desired product quality. Early …

This paper presents a modified reconstruction-based contributions for sensor fault diagnosis
in continuous time-varying processes. The proposed fault diagnosis method is based on …

In this paper, a representation learning based adaptive monitoring method for multimode
processes is proposed, in which mode identification and fault detection are integrated with …

Auto-associative neural network (AANN) is a typical nonlinear principal component analysis
method, which is widely used in industry for fault diagnosis purposes, especially in nonlinear …

This study evaluates the use of Raman spectroscopy with a multivariate curve resolution–
alternating least squares (MCR-ALS) analysis to monitor the adulteration and purity of …

Independent component analysis (ICA) has been widely used in non-Gaussian industrial
process monitoring. However, the stability of performance and determination of dominant …

Recently, k‐nearest neighbor rules (kNN) have drawn increasing attention for fault detection
of multimodal industrial processes. However, the traditional kNN method performs poorly for …

Most data-driven process monitoring approaches consider the fault detection as a binary
classification issue: normal or abnormal. All deviations from the nominal operating condition …

作为一种经典的方法, 主成分分析(PCA) 在多元统计过程监控领域得到了广泛的应用. 然而,
主成分分析及其各种改进方法仅从原始数据中提取了一层特征, 缺乏对深层次特征的提取 …

Fault detection and diagnosis schemes based on data-driven statistical modelling are highly
dependent on an accurate and exhaustive feature extraction procedure to deliver a superior …