Abstract
Fault diagnosis is an important task for the normal operation and maintenance of equipment. In many real situations, the diagnosis data cannot provide deterministic values and are usually imprecise or uncertain. Thus, interval-valued fuzzy sets (IVFSs) are very suitable for expressing imprecise or uncertain fault information in real problems. However, existing literature scarcely deals with fault diagnosis problems, such as gasoline engines and steam turbines with IVFSs. However, the similarity measure is one of the important tools in fault diagnoses. Therefore, this paper proposes a new similarity measure of IVFSs based on logarithmic function and its fault diagnosis method for the first time. By the logarithmic similarity measure between the fault knowledge and some diagnosis-testing samples with interval-valued fuzzy information and its relation indices, we can determine the fault type and ranking order of faults corresponding to the relation indices. Then, the misfire fault diagnosis of the gasoline engine and the vibrational fault diagnosis of a turbine are presented to demonstrate the simplicity and effectiveness of the proposed diagnosis method. The fault diagnosis results of gasoline engine and steam turbine show that the proposed diagnosis method not only gives the main fault types of the gasoline engine and steam turbine but also provides useful information for multi-fault analyses and predicting future fault trends. Hence, the logarithmic similarity measure and its fault diagnosis method are main contributions in this study and they provide a useful new way for the fault diagnosis with interval-valued fuzzy information.
Highlights
The technique of fault diagnoses has produced substantial economic benefits since various fault diagnosis methods have been developed and applied in engineering areas
This paper proposed a logarithmic similarity measure (LSM) between interval-valued fuzzy sets (IVFSs) and its fault diagnosis method
The proposed fault diagnosis method was applied to the misfire fault diagnosis of the gasoline engine and the vibrational fault diagnosis of the steam turbine under an interval-valued fuzzy environment
Summary
The technique of fault diagnoses has produced substantial economic benefits since various fault diagnosis methods have been developed and applied in engineering areas. The cosine and tangent similarity measures of SVNSs [19,20] have been proposed and applied to the misfire fault diagnosis of the gasoline engine and the vibrational fault diagnosis of steam turbine under single-valued neutrosophic environment. Since IVFSs are more suitable for the expression of fault information in fault diagnosis problems, such as the misfire fault diagnosis of gasoline engine and the vibrational fault diagnosis of steam turbine with interval-valued fuzzy information [14,16,17].
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