Preventing downtimes in machinery operation is becoming fundamental in industrial standards. The most common strategy to avoid costly production stoppages is the preventive maintenance, combining it with reactive maintenance in detected malfunctions. Preventive maintenance can reduce costs, increase uptime and help maintaining the quality of the produced goods.
The fingerprint analysis concept will help in the implementation of preventive maintenance []. The asset in a good health state is monitored as a set of pre-defined operating conditions. The monitorization can be triggered during the whole asset’s life-time in the same pre-defined operating conditions. As a result, a reference value is taken which accounts for normality. This value will be compared with measurements made throughout the life. The goal is to be able to detect and determine the appearance of abnormalities.
Similarly, the fixed cycle features test assesses the machine performance degradation in a fixed cycle. In this case the concept is focused on machinery that works in close loops with comparable conditions. The machines parameters are measured during the working cycle, setting the baseline, and compared between them in the search of abnormalities that may point out any potential faults.
Both concepts, fingerprint and fixed cycle feature test concept are applied to gearboxes. They are crucial elements in industrial machinery, conventionally monitored using accelerometers. Which have a significative cost and can be hard to install in place to provide useful information. Motor current signature analysis overcomes the inconveniences of accelerometers. This analysis technique provides a non-intrusive method, and it is based in readily available signals. Changes in the current signal are related with variations of the speed and/or external load of the electric motor. Thus, the health state of the gearbox connected to the motor can be examined through an exhaustive analysis of the input currents [].
A specially designed test bench is used in which gears in different health status are tested. The measured signals are analyzed using discrete wavelet decomposition, in different decomposition levels and with different mother wavelets. Additionally, a dual-level time synchronous averaging analysis is performed on the same signal to compare the performance of the two methods. From both analyses, the relevant features of the signals are extracted, cataloged and classified using diverse methods.
The results obtained allow to differentiate the different type of defects on the gears. Allowing to detect the different fault conditions and enabling the assessment of the health state of the gearbox using only the motor current signal.
 Ferreiro, S., Konde, E., Fernández, S., & Prado, A. (2016, June). INDUSTRY 4.0: Predictive Intelligent Maintenance for Production Equipment. In European Conference of the Prognostics and Health Management Society, no (pp. 1-8).
 Liao, L., & Lee, J. (2009). A novel method for machine performance degradation assessment based on fixed cycle features test. Journal of Sound and Vibration, 326(3-5), 894-908.
 Arellano-Padilla, J., Sumner, M., Gerada, C., & Jing, L. (2009, September). A novel approach to gearbox condition monitoring by using drive rectifier input currents. In Power Electronics and Applications, 2009. EPE'09. 13th European Conference on (pp. 1-10). IEEE.
How to Cite
Wavelet, TSA, Condition Monitoring, MCSA
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