Abstract
Statistical data analysis for fault diagnosis in mechanical systems is a fundamental tool, for instance, in applied mechanical engineering. In order to capture a feasible data set, a well designed electronic instrumentation and excitation system signal stages are mandatory. Hence, one objective of this paper is to develop a low cost vibration sensor based on an inductive LC-tank oscillator (a resonant inductive-capacitive electronic circuit carefully designed to produce an harmonic electrical signal), and then to tune an effective excitation system signal to our experimental platform. This platform uses a propelled drone motor mounted on a beam structure to emulate a propelled rotating machine. Essentially, two data set were acquired. One for the healthy behaviour of the developed system, and the other for a programmed faulty scenario. This defective case was realized by introducing a small mechanical fault in one blade extreme of the mechanical propelled system. To note, this faulty scenario is almost impossible to deduce by just seen the raw data. The other objective of this paper is to analyze the obtained data sets by utilizing a statistical data analysis tool. Then, by employing box-plot diagrams, the healthy and faulty cases become evidenced. Finally, and due to we are proposing a low-cost academic experimental platform for fault diagnosis based on data analysis, our platform’s toll was around 120 euros. Hence, this platform results applicable to teach data analysis from dynamical systems.
Highlights
Propelled rotating machines are widely employed in many engineering applications such as wind turbines [Luo et al, 2014], drones [Floreano and Wood, 2015; Belyavskyi et al, 2017], ship drivers [Bosschers et al, 2008], rotating tools [Glowacz, 2018], and so on
5 Conclusions In this paper a low-cost experimental platform was proposed for fault diagnosis in propelled rotating machines
We think that by using this platform, engineering students can practice on acquiring and analyzing data for fault diagnosis in rotating machines based on statistical methods
Summary
Propelled rotating machines are widely employed in many engineering applications such as wind turbines [Luo et al, 2014], drones [Floreano and Wood, 2015; Belyavskyi et al, 2017], ship drivers [Bosschers et al, 2008], rotating tools [Glowacz, 2018], and so on. A fault diagnosis based on data analysis has been proposed in [Vidal et al, 2015] by invoking a controller compensation design. This fault scheme is just oriented to detect faults on the pitch-blade actuator device, as other techniques do [Ruiz et al, 2018; Vidal et al, 2014; Badihi and Zhang, 2018; Chen et al, 2013a; Tutiven et al, 2018]. In [Hernandez-M. et al, 2017] analyzes the application of a time-frequency technique for detection of an unbalance fault in a Wind turbine, etc
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