ABSTRACT Sensor signals acquired in the industrial process contain rich information that can be analyzed to detect system anomalies and facilitate effective monitoring of the process. In many processes, multiple signals are acquired by different sensor channels (i.e. multi-channel data) which have high-dimensional and complex cross-correlation structures. When analyzing such signal data, two main issues must be resolved: (1) feature extraction of multi-channel data to reduce the data dimensionality and improve signal analysis efficiency, and (2) the sensor fusion to achieve better monitoring of the process. It is crucial to develop a method that considers the interrelationships between different sensor channels. This paper proposes an improved multilinear feature extraction method with a feature selection strategy to improve the separability of profile data. The proposed method is applied directly to the high-dimensional multi-channel data. Features are extracted and combined with multivariate control charts to monitor multi-channel data. The effectiveness of the proposed method in quick detection of process changes is demonstrated with both the Monte Carlo simulation and a real-world case study. The real multi-channel data in the case study are recorded in a multi-operation forging process for the propose of process monitoring and fault detection.

中文翻译：

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基于传感器融合的锻造过程多通道数据特征提取与过程监控

摘要 在工业过程中获取的传感器信号包含丰富的信息，可以对其进行分析以检测系统异常并促进对过程的有效监控。在许多过程中，多个信号由不同的传感器通道（即多通道数据）获取，这些通道具有高维复杂的互相关结构。在分析此类信号数据时，必须解决两个主要问题：（1）多通道数据的特征提取，以降低数据维数，提高信号分析效率；（2）传感器融合，实现更好的过程监控。开发一种考虑不同传感器通道之间相互关系的方法至关重要。本文提出了一种改进的多线性特征提取方法，采用特征选择策略来提高剖面数据的可分离性。所提出的方法直接应用于高维多通道数据。提取特征并结合多变量控制图来监控多通道数据。蒙特卡罗模拟和实际案例研究证明了所提出的方法在快速检测过程变化方面的有效性。案例研究中真实的多通道数据记录在多操作锻造过程中，以提出过程监控和故障检测。蒙特卡罗模拟和实际案例研究证明了所提出的方法在快速检测过程变化方面的有效性。案例研究中的真实多通道数据记录在多操作锻造过程中，以提出过程监控和故障检测。蒙特卡罗模拟和实际案例研究证明了所提出的方法在快速检测过程变化方面的有效性。案例研究中的真实多通道数据记录在多操作锻造过程中，以提出过程监控和故障检测。