Hierarchical cognize framework for the multi-fault diagnosis of the interconnected system based on domain knowledge and data fusion. (1st May 2022)

• Record Type: Journal Article
• Authors: Zhang, Tong; Tao, Laifa; Wang, Xiaoding; Zhang, Cong; Li, Shangyu; Hao, Jie; Lu, Chen; Suo, Mingliang
• Is Part Of: Expert systems with applications. Volume 193(2022)
• Publication Date: 2022-05-01
• Subjects: Hierarchical cognize framework (HCF) -- Multi-fault diagnosis (MFD) -- Experience fused self-adaption Gaussian mixture model (EFSA-GMM) -- Fault coding -- Domain knowledge and data fusion -- Sensor data; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.116503
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A novel 3-level multi-fault cognitive framework is proposed. Three mechanisms of knowledge-data fusion in diagnosis are summarized. The complex mappings among data, sensors, and fault modes are clearly depicted. The verification cases show that the proposed HCF can achieve better results. Abstract: An interconnected system (ICS) is a complex industry system with multiple sensors, multiple tasks, and massive interaction. It is also of great importance for conducting the fault diagnosis technology research. Multi-fault diagnosis (MFD) is an urgent problem in engineering, while the complex mapping relationships among the system sensors, data patterns in single sensors, and fault modes in ICSs bringing severe challenges. The faults of ICS are similar to human disease in multiple dimensions. Enlightening the understanding of diseases in medicine guides us: hierarchical cognition and knowledge-data-fusion are important systematic ideas. Inspired by these, we propose a hierarchical cognize framework (HCF), which covers the cognition of sensors, data patterns in single sensors, and data climates. Subsequently, we propose a fuzzy neighbourhood three-way decision (FN3WD), experience fused self-adaptation Gaussian-mixture-model (EFSA-GMM), and coding-with-knowledge-discrimination (CWKD) to construct an HCF. To comprehensively verify the HCF, we successfully apply the HCF to the MFD of a satellite power system. Classic models of two-mainstream strategies are introduced as comparisons, specifically, MC-DCNN, MC-SVM, ML-DCNN, and ML-SVM. Compared to the comparative models, the HCF performs an increase of 12.35%, 7.72%, 6.90%, and 8.10% at least in accuracy, precision, recall, and F1-score, respectively, in 10 times cross-validation. Benefitting from the fusion of knowledge, the HCF has cognitive advantages in obtaining a high accuracy and precision diagnosis results. Meanwhile, the time consumption of the HCF is approximately 130 s, which is considerably reduced by as much as 50% compared with the deep learning models.