Instrumentation & Measurement Magazine 24-7 - 68

Fig. 7. Bearing faults. (a) Ball fault; (b) Inner race fault; (c) Outer race fault; (d) Loss of lubrication and rusty fault.
severe problem that occurs
is related to the slip in (2).
If the load is low, the slip
generated is not enough
to separate the fault frequency
from the power
frequency, and as a result,
this one is not detected due
to the leakage present in
the FFT, for which it is essential
to consider a correct
sampling frequency and
the number of points to
use in the Fourier transform.
In the case of signals
in the transient state, the
Short Time Fourier Transform
(STFT) has been used
[14], which allows the analysis
of signals that vary
over time. Another technique
commonly used in
the literature is the discrete
wavelet transform
(DWT), which allows a
time-frequency analysis.
bits, with a sampling frequency range from 1 kHz to 100 MHz.
This block can also include a signal coupling, an analog filter
to avoid aliasing, and a filter to reduce high-frequency noise. In
some cases, the sensor is connected directly to an oscilloscope
and, a spectrum analyzer, or a commercial card is used that includes
everything described above.
Processing System
The next block in the methodology corresponds to the processing
system, which can be a microprocessor, digital signal
processor (DSP), graphics processing unit (GPU), a programmable
field gate array (FPGA), Raspberry Pi, Arduino, or a PC,
and this will depend on the complexity of the processing algorithm,
system resources, processing speed and the solution
required by the industry.
Processing Algorithms
The signal processing algorithm for the extraction of signal
characteristics has been the most developed and studied in
recent work. The studied conditions are for steady-state or
during the starting transient, under mechanical load or no load
and even variable load, for low speed or high speed, different
levels of damage, prediction and prognosis, and combined
damage or multiple damage. Based on MCSA and MVSA, several
processing algorithms have been developed. One of the
most used is the Fast Fourier Transform (FFT) [1], [4], [5], [11].
The FFT allows switching from time domain to frequency domain,
thus finding the spurious frequencies of the fault in
(1) and (4), all of this during the motor's steady-state condition,
which rules out its use during the starting transient. A
68
The Hilbert transform and the Empirical Mode Decomposition
[10] are other techniques used to analyze the signal envelope,
mainly with vibrations. There are other more complex techniques
such as Multiple Signal Classification (MUSIC) [15] or
the sparse representation [5], [8] that are used to extract characteristics
of the signal. In addition to the techniques mentioned
are those related to machine learning, such as Artificial Neural
Networks (ANN), Convolutional Neural Network (CNN),
and Deep Learning.
Classifiers
The classifier section may or may not be separated from the
feature extraction algorithm. For example, in Deep learning,
it is possible to obtain the classification result without making
use of the extracted features. The classifier's complexity
will depend on the feature vector provided by the previous
block, that is, how efficient the feature extraction was. It will
also depend on the total of possible outputs, for example,
healthy motor or faulty motor, a binary decision or multiple
options such as healthy motor, broken rotor bar motor,
broken rotor half-bar motor, damaged bearing motor, shortcircuit
motor and broken bar, to mention a few. There are
several classifiers in the literature, for example, Support Vector
Machine (SVM), k-Nearest Neighbor, ANN, evolutionary
algorithms, genetic algorithms, among other machine learning
algorithms.
Table 1 shows a methodology performance of recent applied
methodologies. This table is taken from [5]. Please refer
to this paper to see the complete list of references used in the
table.
IEEE Instrumentation & Measurement Magazine
October 2021

Instrumentation & Measurement Magazine 24-7

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