Due to the importance and dangerousness of large-scale production processes, the accuracy and reliability of fault diagnosis approaches are critical for safe operation. In this paper, a robust fault diagnosis approach is proposed to realize the reliable classification while ensuring high accuracy. The feature importance distribution is proposed to select appropriate dimension reduction methods, and the real data structure is remained by the same-scale standardization and same-criterion dimension reduction. By means of the whole procedure optimization for datasets and classifiers, the performances of traditional Support Vector Machine and Naive Bayes can achieve the level of ensemble learning. Next the parallel classifier which consists of different classification theories is able to improve the reliability of final prediction. Experimental results show that the proposed approach outperforms the traditional approaches with accuracy that exceeds 92% for Tennessee Eastman benchmark (18 faults) and exceeds 87% for a real-world three-phase flow process (2 faults).

由于大规模生产过程的重要性和危险性，故障诊断方法的准确性和可靠性对于安全操作至关重要。本文提出了一种鲁棒的故障诊断方法，以在确保高精度的同时实现可靠的分类。提出了特征重要性分布以选择适当的降维方法，并通过相同规模的标准化和相同准则的降维保留了真实的数据结构。通过对数据集和分类器进行全过程优化，传统的支持向量机和朴素贝叶斯算法的性能可以达到整体学习的水平。接下来，由不同分类理论组成的并行分类器能够提高最终预测的可靠性。