In the era of Industry 4.0, the ease of access to precise measurements in real-time and the existence of machine-learning (ML) techniques will play a vital role in building practical tools to isolate inefficiencies in energy-intensive processes. This paper aims at developing an abnormal event diagnosis (AED) tool based on ML techniques for monitoring the operation of industrial processes. This tool makes it easier for operators to accomplish their tasks and to make quick and accurate decisions to ensure highly efficient processes. One of the most popular ML techniques for AED is the multivariate statistical control (MSC) method; it only requires the dataset of the normal operating conditions (NOC) to detect and identify the variables that contribute to abnormal events (AEs). Despite the popularity of MSC, it is challenging to select the appropriate method for detecting and isolating all possible abnormalities a complex industrial process can experience. To address this limitation and improve efficiency, we have developed a generic methodology that integrates different ML techniques into a unified multiagent based approach, the selected ML techniques are supposed to be built using only the normal operating condition. For the sake of demonstration, we chose a combination of two ML methods: principal component analysis and k-nearest neighbors (k-NN). The k-NN was integrated into the proposed multiagent to take into account the nonlinearity and multimodality that frequently occur in industrial processes. In addition, we modified a k-NN method proposed in the literature to reduce computation time during real-time detection and isolation. Finally, the proposed methodology was successfully validated to monitor the energy efficiency of a reboiler located in a thermomechanical pulp mill.

在工业 4.0 时代，实时精确测量的便捷性和机器学习 (ML) 技术的存在将在构建实用工具以隔离能源密集型流程中的低效率方面发挥至关重要的作用。本文旨在开发一种基于 ML 技术的异常事件诊断 (AED) 工具，用于监控工业过程的运行。该工具使操作员可以更轻松地完成任务并做出快速准确的决策，以确保高效的流程。AED 最流行的 ML 技术之一是多元统计控制 (MSC) 方法；它只需要正常操作条件 (NOC) 的数据集来检测和识别导致异常事件 (AE) 的变量。尽管 MSC 很受欢迎，选择合适的方法来检测和隔离复杂工业过程可能遇到的所有可能的异常是一项挑战。为了解决这个限制并提高效率，我们开发了一种通用方法，将不同的 ML 技术集成到一个统一的基于多智能体的方法中，选定的 ML 技术应该仅使用正常操作条件来构建。为了演示，我们选择了两种 ML 方法的组合：主成分分析和 k-最近邻 (k-NN)。k-NN 被集成到所提出的多智能体中，以考虑到工业过程中经常出现的非线性和多模态。此外，我们修改了文献中提出的 k-NN 方法，以减少实时检测和隔离期间的计算时间。最后，