Search

We propose an optical thickness measurement device based on electro-optic dual-optical frequency combs (dual-OFCs). Optical frequency combs (OFCs) are light sources that provide an optical signal consisting of many equidistant monochromatic tones. In this paper, we present an agile dual-OFC architecture with adjustable frequency separation of the comb modes and total frequency span, allowing high-resolution measurements of the thickness of transparent thick samples. This architecture is based on a single continuous-wave laser diode and external electro-optic devices to implement the dual-comb sensor, allowing easy control of the optical spectrum of the interrogation source (dual-OFC). As it is characteristic of dual-OFC systems, the optical transmittance function of the sample (etalon) is directly translated to the radiofrequency domain, where detection, demodulation, and processing of signals are performed. The shift in the complexity of implementation from the optical to the electronic domain yields many advantages, as acquisition and signal processing are made independently on the optical characteristics of the sample (thickness). This work was supported by the Spanish Ministry of Economy and Competitiveness under the RTC-2015-4205-4 Salicar and TEC-2014-52147-R Mossi grants.

Partial discharges (PD) detection is a widely extended technique for the diagnosis of electrical equipment. Ultra-high frequency (UHF) detection techniques appear as the best choice if the goal is to detect PD online and to locate devices with insulation problems in substations and overhead lines. The location of PD is based on the determination of the difference of the time of arrival of electromagnetic pulses radiated by a source of PD to an array of antennas distributed around the monitored area. However, when measuring electromagnetic pulses radiated by PD activity many interfering signals, such as those coming from television (TV), global positioning system (GPS), wireless communication signals and others coming from electrical equipment distort the waveform detected by the sensors. Under these circumstances, the application of traditional techniques to estimate the time differences may fail. In this paper, the use Blind Source Separation (BSS) techniques applied to pairs of UHF sensors is proposed to extract the information of the difference of the time of arrival of the electromagnetic pulses radiated by a source of PD. The paper is focused on the application of the algorithm and the description of an experimental setup for controlled generation and detection of PD to verify the performance of the proposed technique.

Optical frequency combs (OFCs) generated through the modulation of continuous-wave lasers offer higher power per component, simple comb spacing control, and lower complexity and cost than traditional OFCs. By these reasons, they are an interesting optical source for spectrometers. In this letter, a dual-comb architecture based on two OFCs generated from the same laser using optical phase modulators is presented. The system features a new placement for the spectroscopic sample that increases the versatility, robustness, and stability of the instrument without requiring any reconfiguration for carrying out different types of measurement. Besides this, a self-referenced detection setup based on digital phase sensitive multitone detection improves the SNR of the previous approaches offering more flexibility in its configuration and high data output rate. The proposed architecture is validated by measuring a ro-vibrational absorption feature of HCN at 1544.51 nm and characterizing the spectral response of a fiber-coupled Fabry–Perot filter.

The goal of an automatic monitoring system of partial discharges (PDs), based on acoustic emission (AE) detection, is the identification of the type of source of PD and its localization. In the event that multiple deterioration processes are present in the electrical equipment, more than one PD source may be active and their AE signals may overlap on the sensors. This overlapping effect modifies the temporal and frequency characteristics of the measured signals compared to the characteristics of the signals from a single PD source and thus, automatic classification becomes very difficult. In this paper we have proposed applying blind signal separation (BSS) techniques to recover the signals from each source, therefore separating each temporal and frequency characteristic. We have tested the proposed algorithm: firstly using synthetic mixed signals from two types of PD sources and secondly using real signals from a test bench specifically designed to control the position, time and amplitude of the AEs.