A Neutrosophic Set Based Fault Diagnosis Method Based on Multi-Stage Fault Template Data

Abstract

Fault diagnosis is an important issue in various fields and aims to detect and identify the faults of systems, products, and processes. The cause of a fault is complicated due to the uncertainty of the actual environment. Nevertheless, it is difficult to consider uncertain factors adequately with many traditional methods. In addition, the same fault may show multiple features and the same feature might be caused by different faults. In this paper, a neutrosophic set based fault diagnosis method based on multi-stage fault template data is proposed to solve this problem. For an unknown fault sample whose fault type is unknown and needs to be diagnosed, the neutrosophic set based on multi-stage fault template data is generated, and then the generated neutrosophic set is fused via the simplified neutrosophic weighted averaging (SNWA) operator. Afterwards, the fault diagnosis results can be determined by the application of defuzzification method for a defuzzying neutrosophic set. Most kinds of uncertain problems in the process of fault diagnosis, including uncertain information and inconsistent information, could be handled well with the integration of multi-stage fault template data and the neutrosophic set. Finally, the practicality and effectiveness of the proposed method are demonstrated via an illustrative example.

Single-Valued Neutrosophic Set Correlation Coefficient and Its Application in Fault Diagnosis

“…named as triangular fuzzy number [9] of this data set D, as shown in Figure 1: [9] of this data set D, as shown in Figure 1: It can be seen from the geometric interpretation of the triangular fuzzy number in the figure above:…”

“…With the development of automation technology, these mechanical machines gradually came into the stage of fully automated control operation [1][2][3][4][5]. In this way, people's hands are comparatively free, and machines are more intelligent and comprehensive; however, this kind of full automation greatly increases the probability of mechanical equipment failure as well [6][7][8][9][10][11]. If the mechanical equipment has faults, the quality of the manufactured products will not pass the standard, which will affect the economic benefits of the enterprise [12][13][14][15]; additionally, it will bring potential danger to personal safety [16][17][18][19].…”

An Improved Multisensor Data Fusion Method and Its Application in Fault Diagnosis

“…At the same time, the information obtained from different sensors may be uncertain, fuzzy, or even conflict. Since so many scenarios need the fusion of multisensors information [4], [5], it is very important to make reasonable decision for the problems like how to cope with the uncertainty [6]- [8], how to handle the inconsistent information [9]- [11], as well as how to make a reasonable decision [12], [13]. In actual applications, even attributer eduction is required to deal with large and uncertain data which is linked to multiple relevant data sources [14]- [16].…”

Multi-Attribute Decision Making Method Based on Aggregated Neutrosophic Set

“…For instance, C = (0.7, 0.5, 0.2) is a SVNS, in which the truth-membership t = 0.7, the indeterminacy i = 0.5 and the falsity-membership f = 0.2. Because SVNS are easy to express the inaccurate information, SVNSs are widely used in actual situations, such as in medicine [41], image processing [42], multi-criteria decision-making [43][44][45][46][47], fault diagnosis [41,48,49], etc.…”