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A novel approach for the compression of mechanical vibration signals is presented in this paper. The method relies on a simple and flexible decomposition into a large number of subbands, implemented by an orthogonal transform. Compression is achieved by a uniform adaptive quantization of each subband. The method is tested on a large number of real vibration signals issued by plane engines. High compression ratios can be achieved, while keeping a good quality of the reconstructed signal. It is also shown that compression has little impact on the detection of some commonly encountered defects of the plane engine.

A smartphone is a low-cost pocket wireless multichannel multiphysical data acquisition system: the use of such a device for noise and vibration analysis is a challenging task. To what extent is it possible to carry out relevant analysis from it? The Survishno conference, held in Lyon in July 2019, proposed a contest to participants based on this subject. Two challenges were proposed, wherein each a mute video showing an object moving/excited at different frequencies was provided. Due to the frequencies set and the video sampling characteristics, special effects occurred and are visible on both videos. From the first video, participants were asked to estimate the Instantaneous Angular Speed (IAS) of a rotating fan. From the second video, they were asked to perform the modal analysis of a cantilever beam. This paper gathers the interesting ideas proposed by the contestants and proposes a global method to solve these two problems. One major point of the paper might be the advantageous use of the rolling shutter effect, a well-known artefact of smartphone videos, to perform advanced mechanical analyses: the consideration of the unavoidable slight phase shift between the acquisition of each pixel opens up the possibility to perform a dynamic analysis at frequencies that are much higher than the video frame rate.

This paper proposes a vibration-based diagnostic methodology for aircraft bearings based on a joint first- and second-order cyclostationary analysis. The idea is to track the first- and second-order content over a predefined operating speed range, instead of ignoring the later or performing the diagnosis on an arbitrary stationary speed. The methodology applies to relatively long vibration signals recorded under strong speed variations, using a sliding window over which fluctuations are low. First, we obtain the time-evolution of the spectral statistics by computing the so-called instantaneous power and coherence spectra reflecting the first and second order content, respectively. Then, we design a time-to-speed transform based on fuzzy logic to transform the previously obtained time-cyclic maps into speed-cyclic maps, expressing the spectral statistics as functions of a predefined operating speed grid of interest. Last, we demonstrate the proposed methodology on a real vibration signal captured from an accessory gearbox of a CFM56 aircraft engine, with multiple bearing faults.

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