Feature ranking for multi-fault diagnosis of rotating machinery by using random forest and KNN

Sanchez, Rene-Vinicio; Lucero, Pablo; Vasquez, Rafael E.; Cerrada, Mariela; Macancela, Jean-Carlo; Cabrera, Diego

Abstract

Gearboxes and bearings play an important role in industries for motion and torque transmission machines. There-fore, early diagnoses are sought to avoid unplanned shutdowns, catastrophic damage to the machine or human losses; additionally, an appropriate diagnosis contributes to increase productivity and reduce maintenance costs. This paper addresses a methodological framework for the diagnosis of multi-faults in rotating machinery through the use of features rankings. The classification uses K nearest neighbors and random forest, based on the information that comes from the measured vibration signal. Thirty features in time domain are calculated from the vibration signal, twenty-four features commonly used in fault diagnosis in rotating machinery, and six features are used from the field of electromyography. Feature ranking methods such as ReliefF algorithm, Chi-Square, and Information Gain are used to select the ten most relevant features, the same ones that enter the classifiers. Five databases were used to validate the proposed methodological framework. The results show good accuracy in classification for the five databases; furthermore, in all the databases in the first ten features ranked by the three rankings methods are present at least two nonconventional features.

Más información

Título según WOS: ID WOS:000436432400007 Not found in local WOS DB
Título de la Revista: JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
Volumen: 34
Número: 6
Editorial: IOS Press
Fecha de publicación: 2018
Página de inicio: 3463
Página final: 3473
DOI:

10.3233/JIFS-169526

Notas: ISI