Abstract: Fault detection in rotating machinery is important for optimizing maintenance chores and avoiding severe damages to other parts. Signal processing based fault detection is usually performed by considering classical techniques for alternative representation of significant signals in time domain, frequency domain or time-frequency domain. An approach based on dictionary learning for sparse representations of vibration signals aiming at gearbox fault detection and classification is proposed. A gearbox signal dataset with 900 records considering the normal case and nine fault classes is analyzed. A dictionary is learned by using a training set of signals from the normal case. This dictionary is…used for obtaining the sparse representation of signals in the test set and the norm metric is used to measure the residual from the sparse representation. The extracted features are useful for machine learning based fault detection. The analysis is performed considering different load conditions. ANOVA statistical analysis shows that there are significant differences between features in the normal case and each of the faulty classes, and best ranked features form well separated clusters. An experiment of fault classification is developed using a support vector machine for multi-class classification of faults. The accuracy obtained is 95.1% in the cross-validation testing.
Show more
Abstract: Gearboxes and bearings play an important role in industries for motion and torque transmission machines. Therefore, 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.
Show more
Keywords: Feature ranking, multi-fault diagnosis, rotating machinery, time features