@Article{10798587.2016.1261955,
AUTHOR = {Jun Ye},
TITLE = {Fault Diagnoses of Hydraulic Turbine Using the Dimension Root Similarity Measure  of Single-valued Neutrosophic Sets},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {24},
YEAR = {2018},
NUMBER = {1},
PAGES = {1--8},
URL = {http://www.techscience.com/iasc/v24n1/39719},
ISSN = {2326-005X},
ABSTRACT = {This paper proposes a dimension root distance and its similarity measure of single-valued neutrosophic 
sets (SVNSs), and then develops the fault diagnosis method of hydraulic turbine by using the dimension 
root similarity measure of SVNSs. By the similarity measures between the fault diagnosis patterns 
and a testing sample with single-valued neutrosophic information and the relation indices, we can 
determine the main fault type and the ranking order of various vibration faults for predicting some 
possible fault trend. Then, the comparison of the fault diagnoses of hydraulic turbine based of the 
proposed dimension root similarity measure and the existing cotangent similarity measure of SVNSs 
is provided to demonstrate the effectiveness and rationality of the proposed fault diagnosis method. 
The fault diagnosis results of hydraulic turbine show that the proposed fault diagnosis method not 
only gives the main fault types of hydraulic turbine, but also provides useful information for multifault analyses and future possible fault trends. The developed fault diagnosis method is effective and 
reasonable in the fault diagnosis of hydraulic turbine under single-valued neutrosophic environment.},
DOI = {10.1080/10798587.2016.1261955}
}