Abstract
Electric motors have been widely used as fundamental elements for driving kinematic chains on mechatronic systems, which are very important components for the proper operation of several industrial applications. Although electric motors are very robust and efficient machines, they are prone to suffer from different faults. One of the most frequent causes of failure is due to a degradation on the bearings. This fault has commonly been diagnosed at advanced stages by means of vibration and current signals. Since low-amplitude fault-related signals are typically obtained, the diagnosis of faults at incipient stages turns out to be a challenging task. In this context, it is desired to develop non-invasive techniques able to diagnose bearing faults at early stages, enabling to achieve adequate maintenance actions. This paper presents a non-invasive gradual wear diagnosis method for bearing outer-race faults. The proposal relies on the application of a linear discriminant analysis (LDA) to statistical and Katz’s fractal dimension features obtained from stray flux signals, and then an automatic classification is performed by means of a feed-forward neural network (FFNN). The results obtained demonstrates the effectiveness of the proposed method, which is validated on a kinematic chain (composed by a 0.746 KW induction motor, a belt and pulleys transmission system and an alternator as a load) under several operation conditions: healthy condition, 1 mm, 2 mm, 3 mm, 4 mm, and 5 mm hole diameter on the bearing outer race, and 60 Hz, 50 Hz, 15 Hz and 5 Hz power supply frequencies
Highlights
Induction motors (IM) are relevant devices that have been extensively used for driving kinematic chains on a diverse number of mechatronic systems
It is worth noting that a final diagnosis can not be carried out by the direct evaluation of a single time-domain features (TF) value, since an undesired false indication may be obtained as an overlapping among different fault severities is obtained
It can be clearly seen that the combination of σ, k, Katz’s fractal dimension (KFD), SFRMS, SFSRM, 5th M, 6th M, and SMR performs best for discriminating the faults when the driving motor is powered at 60 Hz under a direct line connection
Summary
Induction motors (IM) are relevant devices that have been extensively used for driving kinematic chains on a diverse number of mechatronic systems This situation makes them essential machines for several industrial applications, which are primordial for the economy of many developed countries [1]. The most common reported failures linked to bearings are generally due to excessive loads, insufficient lubrication, external contamination, improper installation, and electrical arcing. This situation can lead to an unacceptable performance, and undesirable vibrations with an imminent failure or defects of individual components, such as the rolling element, inner race, and outer race. According to [4], outer race faults is one of the most common causes of bearing failures, which have been failing from electrical arcing because of stray currents, and that recently have been increased since the advent of variable frequency drives (VFD)
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