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Vibration-condition monitoring aims to detect bearing damages of rotating machinery’s incipient failures mainly through time–frequency methods because of their efficient analysis of nonstationary signals. However, by having failures with impulse behavior, short-term events have a tendency to be diluted under variable-speed conditions, while information on frequency changes tends to be lost. Here, we introduce an approach to highlighting bearing impulsive failures by measuring short-term spectral components to deal with variable-speed vibrations. The short-term estimator employs two sliding windows: a small one that measures the instantaneous amplitude level and tracks impulsive components and a large interval that evaluates the average background amplitude. Aiming to characterize cyclo-non-stationary processes with impulsive behavior, the emphasizing high-order-based estimator based on the principle of spectral entropy is introduced. For evaluation, both visual inspection and classifier performance are assessed, contrasting the spectral-entropy estimator with the widely used spectral-kurtosis approach for dealing with impulsive signals. The validation of short-time/-angle spectral analysis performed on three datasets at variable speed showed that the proposed spectral-entropy estimator is a promising indicator for emphasizing bearing failures with impulse behavior.

International audience; 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 introduces a method based on short periodograms, combining, envelope analysis, and spectral kurtosis (SK). In order to highlight an underlying impulsive spectral component of a vibration signal, to do so, the proposed method takes into account the well-known welch’s method of periodograms applied to a non-overlapping filtered signal segment using the SK. An SK short-time approach is considered, due to, a high variable speed even in angle domain has an underlying non-stationarity caused by the fact that the transfer function is angle variant. In the end, it is obtained a noiseless envelope spectrum, averaging the filtered periodograms of each envelope of the non-overlapped signal segments. To prove the effectiveness of the proposed method, it is tested on a highly non-stationary vibration signal measured from an aircraft engine under a run-up test. As a result, we can identify a bearing failure embedded highly non-stationary noise. Besides as the proposed method makes use of short periodograms is fast, reliable, and entirely non-parametric, making it readily applicable to highlight any underlying impulsive behaviour in a vibration signal.

Vibration and acoustic analysis actively support the nondestructive and noninvasive fault diagnostics of rotating machines at early stages. Nonetheless, the acoustic signal is less used because of its vulnerability to external interferences, hindering an efficient and robust analysis for condition monitoring (CM). This paper presents a novel methodology to characterize different failure signatures from rotating machines using either acoustic or vibration signals. Firstly, the signal is decomposed into several narrow-band spectral components applying different filter bank methods such as empirical mode decomposition, wavelet packet transform, and Fourier-based filtering. Secondly, a feature set is built using a proposed similarity measure termed cumulative spectral density index and used to estimate the mutual statistical dependence between each bandwidth-limited component and the raw signal. Finally, a classification scheme is carried out to distinguish the different types of faults. The methodology is tested in two laboratory experiments, including turbine blade degradation and rolling element bearing faults. The robustness of our approach is validated contaminating the signal with several levels of additive white Gaussian noise, obtaining high-performance outcomes that make the usage of vibration, acoustic, and vibroacoustic measurements in different applications comparable. As a result, the proposed fault detection based on filter bank similarity features is a promising methodology to implement in CM of rotating machinery, even using measurements with low signal-to-noise ratio.

International audience; This paper presents the content and outcomes of the Safran contest organized during the International Conference Surveillance 8, October 20–21, 2015, at the Roanne Institute of Technology, France. The contest dealt with the diagnosis of a civil aircraft engine based on vibration data measured in a transient operating mode and provided by Safran. Based on two independent exercises, the contest offered the possibility to benchmark current diagnostic methods on real data supplemented with several challenges. Outcomes of seven competing teams are reported and discussed. The object of the paper is twofold. It first aims at giving a picture of the current state-of-the-art in vibration-based diagnosis of rolling-element bearings in nonstationary operating conditions. Second, it aims at providing the scientific community with a benchmark and some baseline solutions. In this respect, the data used in the contest are made available as supplementary material.

Mostly, a vibration signal from rotating machines comprises stationary and non-stationary components, whose description should be as accurate as possible to infer the internal and external forces that affect the system behavior. Moreover, either component can provide diverse but relevant information about the machine health. Thus, bearing faults foster the non-stationary component that is characterized by time-varying statistical moments, periodically changing through the time (or cyclostationary signals). Therefore, the problem to detect a bearing fault signal is usually addressed to separate the deterministic and the stochastic components of the vibration signal to make clear the damage characteristics. To this end, we present the novel order tracking (OT) method that decomposes the non-stationary vibration signal into narrow-band spectral components, aiming to enhance the cyclostationary characteristics. Moreover, a similarity measure is computed between the envelopes of the raw signal and each component, allowing to quantify the cyclic behavior of signal components. Since the proposed method acts as a narrow-band filter, a comparison with the spectral kurtosis (SK) is performed using a rolling element bearing dataset that includes an inner race, outer race, and rolling element defects. Specifically, the Case Western Reserve University data is carried out aiming to improve the diagnosis of bearing failures that are categorized in a recent work using the benchmark methods. As a result, the proposed blind extraction method allows capturing the cyclostationary behavior hidden in the signal and improves the identification of the bearing faults when the signal is noisy.

Order tracking (OT) is a technique for vibration analysis under non-stationary operating conditions, which considers the speed or load is time-varying in order to obtain machine health indicators. Nonetheless, information about the rotational speed implies a priori knowledge about the machine. Therefore, in this paper is proposed a blind instantaneous rotational speed extraction methodology that extracts a set of instantaneous frequencies (IF), from a filter bank applied to the original signal, using the Hilbert transform. The signal is transformed into order domain using velocity synchronous discrete Fourier transform (VSDFT), taking each estimated IF as a possible reference shaft speed. Then, the kurtosis is used as a non-stationarity criterion aiming to select a suitable instantaneous reference shaft speed. The methodology is tested in a simulated signal and a test rig which exhibit non-stationary behavior. As a result the closest component to the rotational speed is extracted without priori knowledge of the machine.