Showing papers on "Demodulation published in 2022"

Compound Fault Diagnosis Using Optimized MCKD and Sparse Representation for Rolling Bearings

Abstract: The effective separation of fault characteristic components is the core of compound fault diagnosis of rolling bearings. The intelligent optimization algorithm has better global optimization performance and fast convergence speed. Aiming at the problem of poor diagnosis effect caused by mutual interference between multiple fault responses, a novel compound fault diagnosis method based on optimized maximum correlation kurtosis deconvolution (MCKD) and sparse representation, namely MDSRCFD, is proposed in this article. For the MCKD, because it is very difficult to set reasonable parameter combination values, artificial fish school (AFS) with global search capability and strong robustness is fully utilized to optimize the key parameters of MCKD to achieve the best deconvolution and fault feature separation. Aiming at the problem that orthogonal matching pursuit (OMP) is difficult to be solved in sparse representation, an artificial bee colony (ABC) with global optimization ability and faster convergence speed is employed to solve OMP to obtain the approximate best atom and realize the reconstruction of signal transient components. The envelope demodulation analysis method is applied to realize feature extraction and fault diagnosis. The simulation and practical application results show that the proposed MDSRCFD can effectively separate and extract the compound fault characteristics of rolling bearings, which can realize the accurate compound fault diagnosis.

Advances in phase-sensitive optical time-domain reflectometry

Abstract: Phase-sensitive optical time-domain reflectometry (Φ-OTDR) has attracted numerous attention due to its superior performance in detecting the weak perturbations along the fiber. Relying on the ultra-sensitivity of light phase to the tiny deformation of optical fiber, Φ-OTDR has been treated as a powerful technique with a wide range of applications. It is fundamental to extract the phase of scattering light wave accurately and the methods include coherent detection, I/Q demodulation, 3 by 3 coupler, dual probe pulses, and so on. Meanwhile, researchers have also made great efforts to improve the performance of Φ-OTDR. The frequency response range, the measurement accuracy, the sensing distance, the spatial resolution, and the accuracy of event discrimination, all have been enhanced by various techniques. Furthermore, lots of researches on the applications in various kinds of fields have been carried out, where certain modifications and techniques have been developed. Therefore, Φ-OTDR remains as a booming technique in both researches and applications.

Physics and applications of Raman distributed optical fiber sensing

Abstract: Abstract Raman distributed optical fiber sensing has been demonstrated to be a mature and versatile scheme that presents great flexibility and effectivity for the distributed temperature measurement of a wide range of engineering applications over other established techniques. The past decades have witnessed its rapid development and extensive applicability ranging from scientific researches to industrial manufacturing. However, there are four theoretical or technical bottlenecks in traditional Raman distributed optical fiber sensing: (i) The difference in the Raman optical attenuation, a low signal-to-noise ratio (SNR) of the system and the fixed error of the Raman demodulation equation restrict the temperature measurement accuracy of the system. {ii) The sensing distance and spatial resolution cannot be reconciled. (iii) There is a contradiction between the SNR and measurement time of the system. (iv) Raman distributed optical fiber sensing cannot perform dual-parameter detection. Based on the above theoretical and technical bottlenecks, advances in performance enhancements and typical applications of Raman distributed optical fiber sensing are reviewed in this paper. Integration of this optical system technology with knowledge based, that is, demodulation technology etc. can further the performance and accuracy of these systems.

Enhancement of Remote Vital Sign Monitoring Detection Accuracy Using Multiple-Input Multiple-Output 77 GHz FMCW Radar

Abstract: Remote non-contact monitoring of human vital signs has recently received lots of attention due to the advancement and availability of millimeter wave (mmWave) radars. These sensors are significantly reduced in size, but still face serious electromagnetic (EM) propagation loss and signal obstructions resulting in lower signal-to-noise ratios (SNR). As mmWave received signals also have higher sensitivity to body motions, these effects typically degrade the accuracy of heart rate (HR) detection. To overcome this challenge, MIMO configuration can be used to improve the SNR level by taking advantage of its channel diversity. We use here a Frequency Modulated Continuous Wave (FMCW) radar from Texas Instruments (TI) at 77 GHz to collect data from 192 channels. Additionally, vital sign information is extracted using Arctangent Demodulation (AD) and Maximal Ratio Combining (MRC) combined with an adapted-wavelet Continuous Wavelet Transform (CWT) are utilized to demonstrate improvement of HR estimation accuracy.

Physics and applications of Raman distributed optical fiber sensing

Abstract: Abstract Raman distributed optical fiber sensing has been demonstrated to be a mature and versatile scheme that presents great flexibility and effectivity for the distributed temperature measurement of a wide range of engineering applications over other established techniques. The past decades have witnessed its rapid development and extensive applicability ranging from scientific researches to industrial manufacturing. However, there are four theoretical or technical bottlenecks in traditional Raman distributed optical fiber sensing: (i) The difference in the Raman optical attenuation, a low signal-to-noise ratio (SNR) of the system and the fixed error of the Raman demodulation equation restrict the temperature measurement accuracy of the system. {ii) The sensing distance and spatial resolution cannot be reconciled. (iii) There is a contradiction between the SNR and measurement time of the system. (iv) Raman distributed optical fiber sensing cannot perform dual-parameter detection. Based on the above theoretical and technical bottlenecks, advances in performance enhancements and typical applications of Raman distributed optical fiber sensing are reviewed in this paper. Integration of this optical system technology with knowledge based, that is, demodulation technology etc. can further the performance and accuracy of these systems.

Cost-effective improvement of the performance of AWG-based FBG wavelength interrogation via a cascaded neural network.

Abstract: Fiber Bragg grating (FBG) sensors have been widely applied in various applications, especially for structural health monitoring. Low cost, wide range, and low error are necessary for an excellent performance FBG sensor signal demodulation system. Yet the improvement of performance is commonly accompanied by costly and complex systems. A high-performance, low-cost wavelength interrogation method for FBG sensors was introduced in this paper. The information from the FBG sensor signal was extracted by the array waveguide grating (AWG) and fed into the proposed cascaded neural network. The proposed network was constructed by cascading a convolutional neural network and a residual backpropagation neural network. We demonstrate that our network yields a vastly significant performance improvement in AWG-based wavelength interrogation over that given by other machine learning models and validate it in experiments. The proposed network cost-effectively widens the wavelength interrogation range of the demodulation system and optimizes the wavelength interrogation error substantially, also making the system scalable.

Ga₂O₃-Based Solar-Blind Position-Sensitive Detector for Noncontact Measurement and Optoelectronic Demodulation

Abstract: As a kind of photodetector, position-sensitive-detectors (PSDs) have been widely used in noncontact photoelectric positioning and measurement. However, fabrications and applications of solar-blind PSDs remain yet to be harnessed. Herein, we demonstrate a solar-blind PSD developed from a graphene/Ga2O3 Schottky junction with a 25-nanometer-thick Ga2O3 film, in which the absorption of the nanometer-thick Ga2O3 is enhanced by multibeam interference. The graphene/Ga2O3 junction exhibits a responsivity of 48.5 mA/W and a rise/decay time of 0.8/99.8 μs at zero bias. Moreover, the position of the solar-blind spot can be determined by the output signals of the PSD. Using the device as a sensor of noncontact test systems, we demonstrate its application in measurement of angular, displacement, and light trajectory. In addition, the position-sensitive outputs have been used to demodulate optical signals into electrical signals. The results may prospect the application of solar-blind PSDs in measurement, tracking, communication, and so on.

Improved Hilbert–Huang transform with soft sifting stopping criterion and its application to fault diagnosis of wheelset bearings

Abstract: Vibration signals from rotating machineries are usually of multi-component and modulated signals. Hilbert-Huang transform (HHT), hereby referring to the combination of empirical mode decomposition (EMD) and normalized Hilbert transform (NHT), is an effective method to extract useful information from the multi-component and modulated signals. However, sifting stopping criterion (SSC) that is crucial to the HHT performance has not been well explored for this sift-driven method in the past decades. This paper proposes the soft SSC, which can ease the mode-mixing problem in signal decomposition through the EMD and improve demodulation performance in signal demodulation. The soft SSC can adapt to input signals and determine the optimal iteration number of a sifting process by tracking this sifting process. Extensive simulations show that the soft SSC can enhance the performance of the HHT in signal decomposition, signal demodulation, and the estimation of the instantaneous amplitude and frequency over the existing state-of-the-art SSCs. Finally, the improved HHT with the soft SSC is demonstrated on the fault diagnosis of wheelset bearings.

High-performance temperature and pressure dual-parameter sensor based on a polymer-coated tapered optical fiber.

Abstract: Based on the polymer encapsulation method, a compact structure and high-sensitivity temperature and pressure dual parametric sensor was developed in this paper by wrapping an optical microfiber coupler (OMC) in polydimethylsiloxane (PDMS). Benefiting from the stable chemical properties and good optical field control ability of PDMS, the sensor showed good stability and repeatability. The dependence of the sensor sensitivity on wavelength, temperature, and pressure was experimentally investigated. The results showed that the temperature and pressure sensitivity could reach -2.283 nm/°C and 3.301 nm/Mpa in the C-band range. To overcome the cross-sensitivity of sensor temperature and pressure, a sensitivity matrix was established to realize dual-parameter simultaneous demodulation. In addition, the pressure repeatability of the sensor was tested. Based on this, the sensitivity matrix was further calibrated to reduce the error and improve the accuracy of demodulation. Finally, we also designed a protective shell for the sensor to meet the requirements of practical marine applications. Compared with other existing types of optical fiber sensors, this sensor has the advantages of simple fabrication, high sensitivity, and environmental adaptability, and has great potential for application in the field of marine environmental monitoring.

Weighted envelope spectrum based on the spectral coherence for bearing diagnosis

Abstract: The key idea behind demodulation analysis for bearing diagnosis is to determine the fault-induced frequency band and directly detect the potential bearing fault characteristic frequency (FCF) in the demodulated spectrum. Till now, most demodulation methods are based on the optimal selection of only one informative frequency band. However, the unwanted in-band noise will be retained or some fault information may be ignored in the case of the discrete resonant frequency band or multiple informative frequency bands. To address the issue, a FCF-oriented criterion is proposed to determine all the informative frequency bands rather than only one specified frequency band. A new weighting vector is obtained to control the contribution of each spectral frequency in the demodulated spectrum. Subsequently, a weighted envelope spectrum (WES) is introduced by integrating the spectral correlation over the full spectral frequency band and assigning the new weighting vector on each spectral frequency. In this way, all frequency components with fault information are enhanced while other components are inhibited. Furthermore, expanded to the diagnosis of compound-fault, the FCF-oriented criterion can provide the different weighting vectors relevant to the different potential faults, and the separated fault features can be identified directly in the generated WESs. Finally, the advantages of WES over the traditional methods are testified by the simulated signal and experimental data.

Quantitative demodulation of distributed low-frequency vibration based on phase-shifted dual-pulse phase-sensitive OTDR with direct detection.

Abstract: Phase-sensitive optical time-domain reflectometry (Φ-OTDR) has been proposed for distributed vibration sensing purpose over recent years. Emerging applications, including seismic and hydroacoustic wave detection, demand accurate low-frequency vibration reconstruction capability. We propose to use the direct-detection Φ-OTDR configuration to achieve quantitative demodulation of external low-frequency vibrations by phase-shifted dual-pulse probes. Simultaneous pulsing and phase shifting modulation is realized with a single acousto-optic modulator to generate such probes, relaxing the need for an additional optical phase modulator. In the experiments, vibrations with frequency as low as 0.5 Hz are successfully reconstructed with 10 m spatial resolution and 35 dB signal-to-noise ratio. Excellent linearity and repeatability are demonstrated between the optical phase demodulation results and the applied vibration amplitudes. The proposed method is capable of quantitative demodulation of low-frequency vibrations with a cost-effective system configuration and high computation efficiency, showing potential for commercial applications of distributed seismic or hydroacoustic wave acquisition.

End-to-End Learning for OFDM: From Neural Receivers to Pilotless Communication

Abstract: The benefits of end-to-end learning has been demonstrated over AWGN channels but has not yet been quantified over realistic wireless channel models. This work aims to fill this gap by exploring the gains of end-to-end learning over a frequency- and time-selective fading channel using OFDM. With imperfect channel knowledge at the receiver, the shaping gains observed on AWGN channels vanish. Nonetheless, we identify two other sources of performance improvements. The first comes from a neural network-based receiver operating over a large number of subcarriers and OFDM symbols which allows to reduce the number of orthogonal pilots without loss of BER. The second comes from entirely eliminating orthogonal pilots by jointly learning a neural receiver together with either superimposed pilots (SIPs), combined with conventional QAM, or an optimized constellation. The learned constellation works for a wide range of signal-to-noise ratios, Doppler and delay spreads, has zero mean and does hence not contain any form of SIP. Both schemes achieve the same BER as the pilot-based baseline with 7% higher throughput. Thus, we believe that a jointly learned transmitter and receiver are a very interesting component for beyond-5G communication systems which could remove the need and associated overhead for demodulation reference signals.

Spectral Demodulation of Fiber Bragg Grating Sensor Based on Deep Convolutional Neural Networks

Abstract: This paper presents a new method of demodulating the spectrum of fiber Bragg grating (FBG) based sensors by employing deep convolutional neural networks (DCNN). As a proof of demonstration, FBG-based temperature sensor was utilized to conduct temperature measurement and over 1700 samples of the spectral raw data were recorded to train and validate the DCNN model. Using such method, the temperature information can be directly extracted from the experimentally obtained FBG spectra without any peak tracking algorithms. Since it makes full use of the information containing the full spectrum rather than only the central wavelength, it overcomes the limit of traditional fitting method and could improve the measurement accuracy of FBG effectively, which can reach 99.95% and its mean square error (MSE) is just 0.1080 °C, an order of magnitude less than that achieved by the traditional maximum peak method. The proposed method could reduce the need of high-performance hardware of equipment, whose accuracy can still maintain a high level when the sampling rate is reduced. Additionally, the universality of the method was experimentally demonstrated through the accurate demodulation of tilted FBG spectrum, and the relevant measurand can be retrieved directly from the entire spectrum instead of detecting the change of particular peaks. The proposed approach provides a cost-effective solution for the FBG based sensing system, and is promising for establishing sensing networks to implement smart monitoring.

Rotor Position Estimation Method of Brushless Electrically Excited Synchronous Starter/Generator Based on Multistage Structure

Abstract: This article proposes a rotor position estimation method for brushless electrically excited synchronous starter/generator (BEESSG) at low speed region. Based on the multistage characteristics of BEESSG, two high frequency pulsating voltages with different frequencies are injected into phase $\alpha$ and phase $\beta$ of stator of the main machine simultaneously. The rotor position is estimated by the high frequency induced signals extracted from the stator windings of the main exciter through a position estimator. Low-pass filters are replaced by an auxiliary square wave sampling method to demodulate the rotor position from high frequency induced signals. Due to the amplitude unbalance of demodulated position related signals. A novel first quadrant definite integral method is proposed in this article for position compensation. Furthermore, the effectiveness and feasibility of the proposed method are verified by experimental results.

OFDM Radar and Communication Joint System Using Opto-Electronic Oscillator With Phase Noise Degradation Analysis and Mitigation

Abstract: Orthogonal frequency division multiplexing (OFDM) signal is a superior dual-functional waveform for the integration of radar sensing and communication in intelligent transportation. But the sensitivity to phase noise is a serious issue introducing interference and causing performance degradation during demodulation. In this paper, we explore the essential mechanism of the action and generation of phase noise through theoretical analysis, where the OFDM demodulation process and power spectrum density (PSD) of phase noise is discussed in the frequency domain, and draw the conclusion that high-speed phase jitter will cause unrecoverable deterioration of OFDM demodulation. Therefore, a photonics-aided radar and communication integrated system based on Optoelectronic oscillator (OEO) is proposed. The positive feedback oscillation with long energy storage time make the phase noise pattern of OEO just suitable to against the phase noise sensitivity of OFDM. A proof-of-concept experiment is demonstrated at 24 GHz with 2 GHz bandwidth to verify the radar sensing and communication function. A two-dimensional radar imaging with a range resolution of 0.075 m and velocity resolution of 4.4 km/h, a communication capacity of 6.4 Gbps is obtained. A quantitative performance comparison is also carried out. By using an ordinary microwave source and OEO separately, the demodulation constellation and error vector magnitude (EVM) under different subcarrier spacing is measured and compared. The result is corresponding to our analysis with the EVM decreasing from 12.5% to 4.7% under subcarrier spacing of 125 kHz.

Enhancement of bandwidth in spin-exchange relaxation-free (SERF) magnetometers with amplitude-modulated light

Abstract: We demonstrate the bandwidth enhancement of an all-optical spin-exchange relaxation-free (SERF) magnetometer based on amplitude-modulated (AM) light. Alkali metal atoms are modulated directly by the pump beam instead of the modulation field or radio frequency field. The first harmonic demodulation of an AM SERF magnetometer with a modulation intensity of 15 kHz results in a high bandwidth of over 11 kHz with a sensitivity of [Formula: see text] at 30 Hz and [Formula: see text] at 10 kHz. Meanwhile, the AM SERF magnetometer with DC demodulation presents the same sensitivity as a traditional DC SERF magnetometer ([Formula: see text] at 30 Hz). The presented technique for modulating the amplitude of the pump beam allows AM SERF magnetometers to enter the domain of high-bandwidth magnetometers and opens the door to many areas that are inaccessible to conventional magnetometers.

Instantaneous Frequency Synchronized Generalized Stepwise Demodulation Transform for Bearing Fault Diagnosis

Abstract: Bearings are a key component of rotating machines, and their fault diagnosis is critical for safe operation of rotating machines. Since bearings often work under variable speed conditions and their vibrations contain rich information of health conditions, time–frequency analysis (TFA) of vibration signals has been shown to be an effective way to perform bearing fault diagnosis. However, applications of traditional TFA methods for analyzing vibrations from bearings are often constrained by limited time variability and smearing effects. This article proposes an instantaneous frequency (IF) synchronized-generalized stepwise demodulation transform (IFS-GSDT) method for TFA of nonstationary vibration signals. Demodulators of the proposed IFS-GSDT method are first derived as functions of inclined angles formed by IF lines of windowed signals; thus, IF preestimation is no longer required. A spectral kurtosis-guided strategy is then developed to determine optimal inclined angles. To effectively tackle multicomponent signals, the proposed IFS-GSDT method explores a new linear transforming kernel that synchronizes the demodulators to all signal components, and an iteration procedure can be avoided. The proposed method also allows for the signal to be reconstructed when the window length under analysis is fixed. The effectiveness of the proposed method is validated using simulations and measured vibration data. Comparisons between the proposed method and other popular TFA methods are also conducted to demonstrate the superior characteristics of the proposed method.

Sensitivity enhanced fiber optic hydrophone based on an extrinsic Fabry-Perot interferometer for low-frequency underwater acoustic sensing.

Abstract: A miniaturized fiber optic hydrophone (FOH) based on a composite metal diaphragm with an air back cavity and a high finesse extrinsic Fabry-Perot interferometric (EFPI) scheme for low-frequency underwater acoustic sensing is proposed and experimentally demonstrated in this paper. A composite metal diaphragm is used to improve the stability of the hydrophone. A balance channel is used to equilibrate the hydrostatic pressure and maintain an air cavity, which improves the mechanical sensitivity. In addition, a white light interferometry (WLI) phase demodulation is used to demodulate the high finesse interferometer consisted of the fiber collimator end face and the diaphragm, which improves the phase sensitivity. Experimental results show that the enhanced phase sensitivity of the hydrophone is about -122.5 dB re 1 rad/µPa @ 200 Hz and the sensitivity fluctuation is below 2.5 dB between 3 Hz and 400 Hz, while the minimal detectable pressure (MDP) is 63.7 µPa/Hz1/2 @ 400 Hz. Due to its miniaturized structure and high sensitivity, the FOH may have an enormous potential in underwater target detection.

Matching Synchroextracting Transform for Mechanical Fault Diagnosis Under Variable-Speed Conditions

Abstract: Time–frequency (TF) analysis (TFA) technique has been widely used to the analysis of rotating machine vibration. However, vibration signal from practical sources contains complicated components and noise, so the fault diagnosis to variable-speed machinery is full of challenges. In this study, inspired by the demodulated synchrosqueezing transform (DSST), the synchroextracting transform (SET) is extended to a prior instantaneous frequency (IF) based method, named matching synchroextracting transform (MSET). To achieve the fault diagnosis using MSET, the follow-up works mainly include two parts. First, a demodulation filtering strategy is developed for multicomponent signal separation. Second, the order analysis based multiple IFs estimation idea and the second-order difference operator based IF smoothing scheme are introduced to obtain the reliable initial IFs. The effectiveness of the proposed technique is verified via some simulation studies. Finally, the proposed technique is successfully applied to the fault diagnosis of rolling bearing and planetary gearbox.

Cost-Effective Quasi-Parallel Sensing Instrumentation for Industrial Chemical Species Tomography

Abstract: Chemical species tomography (CST) has been widely applied for the imaging of critical gas-phase parameters in industrial processes. To acquire high-fidelity images, CST is typically implemented by the line-of-sight wavelength modulation spectroscopy measurements from multiple laser beams. In this article, we present a novel quasi-parallel sensing technique and electronic circuits for industrial CST. Although the acquisition and processing of these multiple beams using a fully parallel data acquisition and signal processing system can achieve maximized temporal response in CST, it leads to a highly complex and power-consuming instrumentation with electronics-caused inconsistency between the sampled beams, in addition to a significant burden on data transfer infrastructure. To address these issues, the digitization and demodulation of the multibeam signals in the proposed quasi-parallel sensing technique are multiplexed over the high-frequency modulation within a wavelength scan. Our development not only maintains the temporal response of the fully parallel sensing scheme but also facilitates the cost-effective implementation of industrial CST with very low complexity and reduced load on data transfer compared with the fully parallel sensing technique. The proposed technique was analytically proofed and then numerically examined by noise-contaminated CST simulations. Finally, the designed electronics was experimentally validated using a lab-scale CST system with 32 laser beams.

Bearing multi-fault diagnosis with iterative generalized demodulation guided by enhanced rotational frequency matching under time-varying speed conditions.

Abstract: The rotational frequency (RF) is an important information for multi-fault features detection of rolling bearing under varying speed conditions. In the traditional methods, such as the computed order analysis (COA) and the time-frequency analysis (TFA), the RF should be measured using an encoder or extracted by a complex algorithm, which bring challenge to bearing fault diagnosis. In order to address this issue, a novel iterative generalized demodulation (IGD) based method guided by the instantaneous fault characteristic frequency (IFCF) extraction and enhanced instantaneous rotational frequency (IRF) matching is proposed in this paper. Specifically, the resonance frequency band excited by bearing fault is first obtained by the band-pass filter, and its envelope time-frequency​ representation (TFR) is calculated using the Hilbert transform and the short-time Fourier transform (STFT). Second, the IFCF is extracted using the harmonic summation-based peak search algorithm from the envelope TFR. Third, the time-varying RF ridge is transformed into a line paralleling to the time axis using the IGD with the phase function (PF). The PF is calculated by the IFCF function and fault characteristic coefficient (FCC). Lastly, the iterative generalized demodulation spectrum (IGDS) is obtained using the fast Fourier transform (FFT) for identifying fault type corresponding to the extracted IFCF. Based on obtained fault type and FCC ratios, new PFs and frequency points (FPs) are calculated for detecting other faults. Both simulated and experimental results validate that multi-fault features of rolling bearing under time-varying rotational speeds can be effectively identified without RF measurement and extraction.

Cost-Effective Quasi-Parallel Sensing Instrumentation for Industrial Chemical Species Tomography

Abstract: Chemical species tomography (CST) has been widely applied for the imaging of critical gas-phase parameters in industrial processes. To acquire high-fidelity images, CST is typically implemented by the line-of-sight wavelength modulation spectroscopy measurements from multiple laser beams. In this article, we present a novel quasi-parallel sensing technique and electronic circuits for industrial CST. Although the acquisition and processing of these multiple beams using a fully parallel data acquisition and signal processing system can achieve maximized temporal response in CST, it leads to a highly complex and power-consuming instrumentation with electronics-caused inconsistency between the sampled beams, in addition to a significant burden on data transfer infrastructure. To address these issues, the digitization and demodulation of the multibeam signals in the proposed quasi-parallel sensing technique are multiplexed over the high-frequency modulation within a wavelength scan. Our development not only maintains the temporal response of the fully parallel sensing scheme but also facilitates the cost-effective implementation of industrial CST with very low complexity and reduced load on data transfer compared with the fully parallel sensing technique. The proposed technique was analytically proofed and then numerically examined by noise-contaminated CST simulations. Finally, the designed electronics was experimentally validated using a lab-scale CST system with 32 laser beams.

Improved multi-channel interferometric fiber-optic sensor demodulation based on the Goertzel algorithm.

Abstract: In a multi-channel interferometric fiber-optic sensor system using space-division multiplexing (SDM) and phase-generated-carrier (PGC) demodulation, the phase delay and phase modulation depth fluctuation of each channel will affect the amplitude consistency and harmonic distortion of the demodulation results. In this paper, an improved demodulation scheme based on the Goertzel algorithm is proposed to calculate the multi-channel phase delay and phase modulation depth and to compensate for their fluctuations simultaneously. First, the carrier's 1st to 6th harmonic amplitudes in the interference fringe are extracted using the Goertzel algorithm. Then, the phase delay is calculated using the real and imaginary components of the 1st harmonic amplitude. The phase modulation depth is calculated with a combinatorial operation of the 1st to 6th harmonic amplitudes. In addition, a reference channel is introduced to implement phase delay and modulation depth feedback control. The experimental results demonstrate that the improved scheme can effectively suppress the harmonic distortion and improve the amplitude consistency of multi-channel interferometric fiber-optic sensors with low resource consumption.

Fabry-Perot interferometric sensor demodulation system utilizing multi-peak wavelength tracking and neural network algorithm.

Abstract: For FPI sensor demodulation systems to be used in actual engineering measurement, they must have high performance, low cost, stability, and scalability. Excellent performance, however, necessitates expensive equipment and advanced algorithms. This research provides a new absolute demodulation system for FPI sensors that is high-performance and cost-effective. The reflected light from the sensor was demultiplexed into distinct channels using an array waveguide grating (AWG), with the interference spectrum features change translated as the variation of the transmitted intensity in each AWG channel. This data was fed into an end-to-end neural network model, which was utilized to interrogate multiple interference peaks' absolute peak wavelengths simultaneously. This architecturally simple network model can achieve remarkable generalization capabilities without training large-scale datasets using an appropriate data augmentation strategy. Experiments show that in simultaneous multi-wavelength and cavity length interrogations, the proposed system has the precision of up to ± 14 pm and ± 0.07 µm, respectively. The interrogation resolution can theoretically reach the pm level benefit from the neural network method. Furthermore, the system's outstanding demodulation repeatability and suitability were demonstrated. The system is expected to provide a high-performance and cost-effective, reliable solution for practical engineering applications.

Optimal Power Splitting of Wireless Information and Power Transmission Using a Novel Dual-Channel Rectenna

Abstract: Traditionally, the signal power is divided equally between dual functions in wireless information and power transmission (WIPT), which, intuitively, is not an optimal solution since the power sensitivities for communication and charging node pose different constraints for signals. To address this challenge, a two-channel rectenna with an asymmetrical coupler feeding network (ACFN) has been proposed for WIPT under the power-splitting and time-sharing (PS and TS) schemes. The proposed two-port rectenna consists of a receiving antenna integrated with ACFN where one output port is connected to a rectifying circuit, whereas the other is used for information recovery. Different from the conventional PS scheme with equal division, the proposed rectenna can adjust ratios of power at two outputs. That is, routing high power to the radio frequency (RF)-dc port, and transferring low power but of a sufficient signal-to-noise-ratio (SNR) to the port being used for signal demodulation/decoding. As a result, the simulated/measured maximum RF-dc conversion efficiency of the rectenna can reach up to 73.9%/70.4% for quadrature phase shift keying (QPSK) modulated signals and 63.9%/60.7% for continuous wave (CW) signals. In addition, good isolation between the two output ports ensures low interference against information decoding.

Content-agnostic backscatter from thin air

Abstract: We present CAB, a content-agnostic backscatter system that can demodulate both tag and ambient data from ambient backscattered WiFi alone. In contrast to prior ambient backscatter systems that use ambient data (content) as tag-data carriers, we focus on zero-subcarriers, which are invariant and independent for any ambient OFDM WiFi. The idea of using zero-subcarriers to convey tag data is simple and elegant. Not only does it for the first time remove the dependency of tag-data demodulation on ambient data, but it also significantly improves the practicality of ambient backscatter. We prototype CAB using off-the-shelf FPGAs and SDRs. Extensive experiments show CAB is universal as it can work with multi-band, multi-stream, and multi-user ambient traffic, including WiFi 3/4/5/6. CAB is also high-performing since it can deliver 340.9 Mbps aggregate throughput, reaching 97% Shannon capacity. Since CAB is general, we extend it to leverage ambient LTE traffic as excitations, and the achieved tag-data BER is below 0.002%. As the first content-agnostic backscatter that delivers near Shannon-capacity throughput, we believe CAB takes a curial step forward on ubiquitous battery-free IoTs.

An Adaptive Demodulation Band Segmentation Method to Optimize Spectral Boundary and Its Application for Wheelset-Bearing Fault Detection

Abstract: Wheelset-bearing working states directly affect the stability of bogies, and existing defects may threaten the running safety of high-speed trains. Thus, the fault detection of wheelset bearing is of great importance. In this article, a novel wheelset-bearing fault detection method, named adaptive autocorrelated kurtogram (AAK), is proposed based on the developed autocorrelated kurtosis and the presented spectral segmentation method. Autocorrelated kurtosis is designed to reduce the unrelated component’s interference and increase the signal-to-noise ratio (SNR). The spectral segmentation method is proposed to obtain the fault zone as completely as possible. Based on the feature that rotation frequency is related to bearing fault frequency, the rotation frequency-based window size selection and extension strategy is proposed. With different frequency levels, the AAK is formed. The proposed method is validated by simulated and experimental data. The results show that this method can automatically and adaptively search for a reasonable demodulation bandwidth according to the fault information feature and resonance region position. The method can estimate the center frequency and bandwidth and avoid unrelated component interference. The AAK can provide accurate detection results and possesses excellent performance. Thus, it is suitable for wheelset-bearing fault detection.