Ion Candel

A vector approach to transient signal processing

The detection and characterization of burst signals are challenging tasks for time-frequency analysis, due to their very short duration. This paper investigates in this context the recurrence plot analysis (RPA) method, from which it derives the vector samples processing (VeSP) concept. The paper shows that VeSP is a generic framework that unifies signal processing concepts like histogram and autocorrelation, which it also generalizes and extends. Results of VeSP based tools are provided, concerning detection of transient signals, noise reduction, and frequency estimation.

Optimization of partial discharge detection in high voltage cables based on advanced signal processing techniques

In this paper we approach the challenges of partial discharges (PD) detection in high voltage cables using signal processing techniques based on time frequency methods combined with Recurrence Plot Analysis (RPA) and high order spectrum analysis (HOSA). Detection of PD poses many problems in terms of speed of calculation, selection criteria and multitude of causes which lead to the occurrence of PD. In order to overcome these drawbacks, we have developed an algorithm which uses the spectrogram to perform a fast detection of parts from the signal which are susceptible of PD activity. The second stage calculates for each zone a detection curve using the HOSA concept of bispectrum and RPA. The latter has been applied in many non-linear systems in order to characterize the process on the basis of the recurrence matrix obtained from a time series given by the system.

Recent Advances in Non-stationary Signal Processing Based on the Concept of Recurrence Plot Analysis

This work concerns the analysis of non-stationary signals using Recurrence Plot Analysis concept. Non-stationary signals are present in real-life phenomena such as underwater mammal’s vocalizations, human speech, ultrasonic monitoring, detection of electrical discharges, transients, wireless communications, etc. This is why a large number of approaches for non-stationary signal analysis are developed such as wavelet analysis, higher order statistics, or quadratic time-frequency analysis. Following the context, the methods defined around the concept of Recurrence Plot Analysis (RPA) constitute an interesting way of analyzing non-stationary signals and, particularly, the transient ones. Starting from the phase space and the recurrence matrix, new approaches [the angular distance, recurrence-based autocorrelation function (ACF), average-magnitude difference function (AMDF) and time-distributed recurrence (TDR)] are introduced in order to extract information about the non-stationary signals, specific to different applications. Comparisons with existing analysis methods are presented, proving the interest and the potential of the RPA-based approaches.

Compensation of position offset of acoustic transducers using compressive sensing concept

This paper presents a new technique for acoustic transducers position offset compensation, based on compressive sensing reconstruction in the warped domain. In underwater acoustics, the transducers relative position is important for applications involving direction of arrival estimation, localization or source detection. When the transmitter-receiver transducers configuration is inappropriate, the received signals informational content is not the same as the one of the emitted signal. In the case of applications such as underwater objects tracking, the experimental setup constraints and the water flow operational conditions lead to the perturbation of the emitted and received signals propagation. We use compressive sensing reconstruction of the received signal, in the warped domain, in order to recover its missing spectral information due to waves propagation. Tests were conducted in a reduced scale experimental facility, in order to prove the interest of using the signal compressive sensing recovery for the signals time of arrival estimation and to quantify the improvement introduced by this signal processing method. The results show that the time of arrival estimation can be considerably improved after the received signals samples recovery, with the matched filter response improvement.

Detection of cavitation vortex in hydraulic turbines using acoustic techniques

Cavitation phenomena are known for their destructive capacity in hydraulic machineries and are caused by the pressure decrease followed by an implosion when the cavitation bubbles find an adverse pressure gradient. A helical vortex appears in the turbine diffuser cone at partial flow rate operation and can be cavitating in its core. Cavity volumes and vortex frequencies vary with the under-pressure level. If the vortex frequency comes close to one of the eigen frequencies of the turbine, a resonance phenomenon may occur, the unsteady fluctuations can be amplified and lead to important turbine and hydraulic circuit damage. Conventional cavitation vortex detection techniques are based on passive devices (pressure sensors or accelerometers). Limited sensor bandwidths and low frequency response limit the vortex detection and characterization information provided by the passive techniques. In order to go beyond these techniques and develop a new active one that will remove these drawbacks, previous work in the field has shown that techniques based on acoustic signals using adapted signal content to a particular hydraulic situation, can be more robust and accurate. The cavitation vortex effects in the water flow profile downstream hydraulic turbines runner are responsible for signal content modifications. Basic signal techniques use narrow band signals traveling inside the flow from an emitting transducer to a receiving one (active sensors). Emissions of wide band signals in the flow during the apparition and development of the vortex embeds changes in the received signals. Signal processing methods are used to estimate the cavitation apparition and evolution. Tests done in a reduced scale facility showed that due to the increasing flow rate, the signal -- vortex interaction is seen as modifications on the received signals high order statistics and bandwidth. Wide band acoustic transducers have a higher dynamic range over mechanical elements; the systems reaction time is reduced, resulting in a faster detection of the unwanted effects. The paper will present an example of this new investigation technique on a vortex generator in the test facility that belongs to ICPE- CA.

Using wide band signals for obstacle path correction in acoustic scintillation flow meters

This paper deals with the challenges of water flow measurement in hydroelectric plants using acoustic scintillation. One of the drawbacks of ultrasonic signal propagation through water is that measurements require relatively clean water conditions. In water with significant amount of impurities, the level of transmitted signals may not be sufficient for an accurate flow measurement. In terms of signal processing, the effect of impurities is represented by a random loss or fading of received signals. In order to improve the measurement, the wide band signals can be used in order to improve the quality factor of the signals by adaptive filtering using a static mode reference. Results show that, in real configurations, the interference due to the obstacles can be reduced.

Applications of Transient Signal Analysis Using the Concept of Recurrence Plot Analysis

Transient signals are universally characterized by a short duration and a broad spectrum which are often present in various phenomena such as sudden acoustic pressure changes, seismic waves, electrical discharges, etc. In order to efficiently monitor the systems where they happen, it is very important that the signals generated by transient phenomena be detected, located and characterized. This significantly helps to better understand their effects in the given application context. This chapter presents new tools derived from the concept of Recurrence Plot Analysis (RPA) and applied on three real applications. Two of the applications concern the detection, localization and characterization of the electrical partial discharges (measured from photovoltaic panels and on electrical cables, respectively). Another application refers to the quantification of the water hammer effect using two acoustic sensors placed on a pipe line.

Water hammer effect characterization using an acoustic signal processing approach

The paper presents an acoustic signal processing approach for the surveillance of a water pipe and the characterization of the water hammer effect. The experiment is based on ultrasonic transceivers and the validation is made with pressure sensors. The major objective is to quantify the pressure variation into the pipe using a non-intrusive method. Therefore, the speed of the transient was determined making use of the time-of-flight (TOF) which was obtained using the Recurrence Plot Analysis (RPA). Several comparisons between the signal processing approach and the classical techniques highlight the advantage of the non-intrusive method.

Robust sparse representation for adaptive sensing of turbulent phenomena

In this study, the authors propose a method for turbulence characterisation by using sparse representation of a channels impulse response. They consider the case of moving vortices created naturally or artificially that do not conserve their physical properties when observed at two distinct positions in space. The existing amplitude-based techniques fail to provide an accurate representation when the physical properties of the dynamic turbulence are altered. A two stages approach is proposed in this study. The first one deals with the design of robust waveforms for sensing of turbulent phenomena. The second stage consists of sparsely representing the decomposition of the turbulences impulse response, based on a physically driven decomposition basis. The tests conducted in a reduced scale experimental facility show, on real data, the efficiency of the turbulence tracking. They compare several types of signals and show that the wideband signals are best suited for the application, achieving a high resolution combined with excellent results in terms of robustness.

Electromagnetic and acoustic bimodality for the detection and localization of electrical arc faults

Electrical arc faults pose an important problem to electrical installations worldwide, be it production facilities or distribution systems. In this context, it is easy to assess the economic repercussions of such a fault, when power supply is cut off downstream of its location, while also realizing that an early detection of the on-site smaller scale faults would be of great benefit. This articles serves as a review of the current state-of-the-art work that has been carried out on the subject of detection and localization of electrical arc faults, by exploiting the bimodality of this phenomenon, which generates simultaneously electromagnetic and acoustic waves, propagating in a free space path. En experimental setup has been defined, to demonstrate principles stated in previous works by the authors, and signal processing methods have been used in order to determine the DTOA (difference-of-time-of-arrival) of the acoustic signals, which allows localization of the transient fault. In the end there is a discussion regarding the results and further works, which aims to validate this approach in more real-life applications.