optimized Neural Network of Predictive Maintenance for Air Booster Compressor (ABC) Motor Failure

Providing Fault Detection from Sensor Data in Complex Machines That Build the Smart City

“…Another common approach is the analysis of electrical variables [4,11,[13][14][15][20][21][22]. In them, the values of the stator's motor currents and voltages are mainly monitored.…”

“…These are the most commonly used types of measurements. However, others appear as complementary measures, such as temperature measurements [13][14][15]17,21] or pressure measurements [21], which are being used in very specific cases. A temperature increase makes possible to detect electrical and mechanical failures, since in the event of excessive friction or high electrical currents the elements tend to overheat.…”

“…To extract the most relevant characteristics and reduce the volume of data used, it is necessary to perform raw data filtering or pre-processing. In the case of predictive maintenance systems, preprocessing methods can be separated into three major groups according to whether they are, in the time domain [13,14,[20][21][22], in the frequency domain [4,11,[15][16][17]19,23] or in the time-frequency domain [12,18].…”

“…In the temporal domain, an attempt is made to reduce the number of data by filtering outliers and erroneous data [14,21]. Normalization [13,21,22] becomes relevant due to the use of various variables (mechanical, electrical, temperatures, pressures, etc.) that can take values on different scales.…”

“…that can take values on different scales. Once the data have been adjusted, they can be used as they are [21,22], or other indicators can be extracted from the parameters. In this second case, statistical indicators (such as maximum, minimum, mean, median, standard deviation, variance, gradients, kurtosis, skewness or crest factor [14,24]) or of another type (such as the principal components [13]) can be used.…”