Abstract
For fault diagnosis and predictive maintenance of rotating machinery, the phase errors generated by the integration processing of a vibration signal are an essential investigation subject. Phase errors affect the solution of mechanical systems with multiple vibration sources and also the information transmitted through the vibration that is used for fault diagnosis. This work proposes the use of phase plane, recurrence plot (RP), and cross recurrence plot (CRP) to evaluate phase shift error effects on the solution of multiple asynchronous and simple periodic functions, and on the smoothing of a Gaussian peak with white noise. Noisy peaks were smoothed twice with the triangular method and with a different number of points. The analysis of the asynchronous periodic functions and the smoothing indicated that a small phase shift changes the phase plane and the RP pattern. These changes can affect not only the accuracy of machinery fault diagnosis but also prediction for the application of timely maintenance actions.
Highlights
The most common rotating machinery is represented by an arrangement of a motor or turbine, a gear box, and one piece of equipment that transforms the dynamic torque into work
The results show that the proposed method is useful for the analysis of the phase shift errors in both cases
When the spectral analysis derives from the integration of the acceleration of a realtime spectrum, instead of a simulated one, many phase shift errors are due to data acquisition and signal conditioning, and most of them are generated by the integration methods
Summary
The diagnosis of failures through the analysis of vibrations in rotating machinery is a technique that has proven to be very useful and reliable in the industry because it helps improve the performance and service life of machines. The main methods to integrate acceleration are based on the time domain and the frequency domain. The integration in the time domain is carried out by integrating the acceleration signal directly. During this integration, several errors are accumulated due to integration method, noise, phase shift from the signal processing, and unknown initial conditions of speed and position. The Fourier transformations applied to the signal generate truncation errors, additional to those already mentioned above in the time domain. In any case, working with these errors is a challenging task for researchers in many areas of study in the fields of engineering and, predictive maintenance
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