Spectral Amplitude Modulation Research Articles

Bearing Compound Fault Diagnosis Using Energy-Constrained Swarm Decomposition and Adaptive Spectral Amplitude Modulation

Bearing Compound Fault Diagnosis Using Energy-Constrained Swarm Decomposition and Adaptive Spectral Amplitude Modulation

Contribution of positive affect in infant directed speech: what do amplitude modulations patterns suggest?

Speech perception relies heavily on cortical entrainment of amplitude modulations in speech, which occur at different rates. Following a study by Leong et al. (2017) showing slower modulations have higher power than faster modulations in infant directed speech (IDS), we hypothesized that positive emotions in IDS might drive this pattern. Using the same analyses (spectral amplitude modulation phase hierarchy method and phase synchronization index (PSI) (Leong and Goswami, 2015)), we compared the power of isolated modulation rates (synchronous with neural oscillations) and the synchrony between them in IDS and adult directed speech (ADS), using English stimuli from Many Babies Consortium (Frank et al., 2020). We repeated the same analyses comparing happy and neutral ADS using stimuli of four native English speakers (Pell et al., 2009). Our analysis did not uncover significant power differences between IDS and ADS. However, it revealed happy ADS has higher power at slower rates, with the reversed pattern for neutral ADS (p < 0.001). PSI was higher for two faster rates in neutral ADS (p < 0.0001), as reported by Leong et al. comparing IDS versus ADS. These findings reveal novel acoustic features of vocal emotions that might be important in attracting infant attention to IDS.

Nonlinear characterization of enhanced and generalized Hjorth’s feature space for bearing condition monitoring

The degradation assessment of rolling bearings provides a reasonable maintenance plan for the safe operation of mechanical equipment. The general strategy for bearing condition monitoring is to construct a health indicator (HI) to characterize different degradation stages. A preferable HI that can sensitively detect initial faults and track machine degradation is crucial to developing timely maintenance strategies for mechanical equipment to avoid catastrophic accidents. However, many developed and reported HIs are still insensitive to early faults, resulting in delayed maintenance schedules. To identify the incipient defects as early as possible, a novel HI constructed by nonlinear characterization of enhanced and generalized Hjorth’s feature space based on extended probability entropy is proposed in this paper. Firstly, the time-frequency spectral amplitude modulation helps to enhance the characteristics of the original signal with the amplitude editing in the time-frequency domain. Then, three new features of generalized Hjorth’s parameter combinations are designed and combined with other similar feature combinations to construct a high-dimensional enhanced and generalized Hjorth’s feature space. On this basis, a set of low-dimensional sensitive features is obtained by nonlinearly characterizing high-dimensional features through extended probability entropy after these features are standardized. Finally, a novel HI is developed by calculating the distance between the minimum volume ellipse (MVE) center of the low-dimensional feature subspace based on nonlinear characterization and the low-dimensional feature vector of the real-time monitoring signal. The performance of the proposed approach is verified in three cases, whose experimental results indicate that the proposed HI is more sensitive and significant in detecting early faults compared to some current HIs.

MIMO DFRC Signal Design in Signal-Dependent Clutter

This paper deals with the Dual-Function Radar and Communication (DFRC) signal design for a Multiple-Input–Multiple-Output (MIMO) system, considering the presence of signal-dependent clutter. A modulation methodology called Spectral Position Index and Amplitude (SPIA) modulation is proposed, which involves selecting passband and stopband positions and applying amplitude modulation. Signal to Interference plus Noise Ratio (SINR) is maximized to enhance radar detectability. Meanwhile, variable modulus and communication modulation constraints are enforced to ensure compatibility with the current hardware techniques and communication demand, respectively. In addition, the mainlobe width and sidelobe level constraints used to concentrate energy in a specific area of space are enforced. To tackle the resulting nonconvex and NP-hard optimization problem, an Iterative Block Enhancement (IBE) framework that alternately updates each signal in each emitting antenna is exploited to monotonically increase SINR. Each block involves the Dinkelbach’s Iterative Procedure (DIP), Sequential Convex Approximation (SCA) and Alternating Direction Method of Multipliers (ADMM) to obtain a single signal. The computational complexity and convergence of the algorithm are analyzed. Finally, the numerical results highlight the effectiveness of the proposed dual-function scheme in sidelobe signal-dependent clutter.

An improved spectral amplitude modulation method for rolling element bearing fault diagnosis

An improved spectral amplitude modulation method for rolling element bearing fault diagnosis

A Tacholess Order Tracking Method Based on Spectral Amplitude Modulation for Variable Speed Bearing Fault Diagnosis

Rolling bearings often work under nonstationary conditions, which will lead to spectrum smearing phenomenon and the failure of traditional diagnostic methods. Order tracking is a common technique to alleviate this problem by resampling the time-domain signal using the instantaneous rotation frequency. For tacholess applications, it is of great importance to obtain accurate rotation speeds. In this paper, a tacholess order tracking method based on spectral amplitude modulation (SAM) is proposed. Based on the fact that SAM can separate frequency components with different levels of energy, an appropriate weight is selected to highlight the instantaneous frequency, which is then extracted from the time-frequency representation by peak search algorithm. Moreover, entropy is employed to evaluate the valuable information in the time-frequency representation in order to select an optimal weight adaptively. A simulated signal and experimental signals of bearing outer and inner ring faults demonstrate that the proposed method can extract instantaneous rotation frequency and detect nonstationary bearing fault characteristics effectively.

Hierarchical amplitude modulation structures and rhythm patterns: Comparing Western musical genres, song, and nature sounds to Babytalk.

Statistical learning of physical stimulus characteristics is important for the development of cognitive systems like language and music. Rhythm patterns are a core component of both systems, and rhythm is key to language acquisition by infants. Accordingly, the physical stimulus characteristics that yield speech rhythm in "Babytalk" may also describe the hierarchical rhythmic relationships that characterize human music and song. Computational modelling of the amplitude envelope of "Babytalk" (infant-directed speech, IDS) using a demodulation approach (Spectral-Amplitude Modulation Phase Hierarchy model, S-AMPH) can describe these characteristics. S-AMPH modelling of Babytalk has shown previously that bands of amplitude modulations (AMs) at different temporal rates and their phase relations help to create its structured inherent rhythms. Additionally, S-AMPH modelling of children's nursery rhymes shows that different rhythm patterns (trochaic, iambic, dactylic) depend on the phase relations between AM bands centred on ~2 Hz and ~5 Hz. The importance of these AM phase relations was confirmed via a second demodulation approach (PAD, Probabilistic Amplitude Demodulation). Here we apply both S-AMPH and PAD to demodulate the amplitude envelopes of Western musical genres and songs. Quasi-rhythmic and non-human sounds found in nature (birdsong, rain, wind) were utilized for control analyses. We expected that the physical stimulus characteristics in human music and song from an AM perspective would match those of IDS. Given prior speech-based analyses, we also expected that AM cycles derived from the modelling may identify musical units like crotchets, quavers and demi-quavers. Both models revealed an hierarchically-nested AM modulation structure for music and song, but not nature sounds. This AM modulation structure for music and song matched IDS. Both models also generated systematic AM cycles yielding musical units like crotchets and quavers. Both music and language are created by humans and shaped by culture. Acoustic rhythm in IDS and music appears to depend on many of the same physical characteristics, facilitating learning.

A time-frequency spectral amplitude modulation method and its applications in rolling bearing fault diagnosis

As one of the key components in rotating machinery, rolling bearings can affect the running state of equipment and even cause huge damage. Therefore, various methods have been proposed to improve the processing of bearing fault signals. Among them, spectral amplitude modulation is an empirical and automated method that can extract the fault characteristics of rolling bearings effectively. However, the characteristic frequencies can be no longer evident or even overwhelmed due to strong background noise. In this paper, a time-frequency spectral amplitude modulation (TFSAM) method is proposed. The proposed method obtains the amplitude in the time-frequency domain using short-time Fourier transform and modifies them with different weights. Thus, more accurate and detailed information about the amplitude can be calculated. Meanwhile, an indicator is proposed to select the optimal weight automatically, after which more obvious characteristic frequencies can be spotted. The effectiveness of the method is verified by a simulated signal and applied to rolling bearing fault diagnosis of outer ring, inner ring and compound faults respectively. The results indicate its accuracy and effectivity in identifying fault information. Comparisons with other commonly used techniques verify the advantages of the proposed method.

Differential spectral amplitude modulation and its applications in rolling bearing fault diagnosis

Rolling bearings are key components of rotating machinery and easy to be damaged due to complex working conditions. Thus, it is of great significance to detect bearing faults effectively. This paper proposes a new nonlinear filtering technique named differential spectral amplitude modulation (DSAM), which can automatically extract different signal components based on their energy levels without any pre-defined parameters. The core of this algorithm is to adjust the energy distribution in a spectrum by using the time-domain differential of a signal, which can not only preserve different constituents of an original signal, but also affect signal characteristics by adaptively enhancing the elements in high-frequency region. Moreover, an indicator is proposed to optimize the outcome, which can weaken the interference of invalid components and obtain more evident fault information. The rationality of this method is verified by two simulated signals and experimental signals of bearing outer ring, inner ring and multiple faults. Comparisons with other commonly used approaches demonstrate the advantages of the proposed method.

Fault feature extraction method of gear based on spectral amplitude modulation and autocorrelation analysis

Due to the strong noise interference and amplitude modulation effect, it is difficult to extract the impact characteristics of the fault gear from the spectrum of the fault signal. To tackle these issues, a gear fault feature extraction method based on spectral amplitude modulation (SAM) and autocorrelation analysis is proposed. First, the SAM method is used to decompose the signal into different components according to energy, and the optimal component with the most fault information is determined by combining kurtosis index and magnitude order selection range. Then, the optimal component is denoised using the autocorrelation function. Finally, the fault feature frequency is extracted by calculating the squared envelope spectrum of the denoised signal. The superiority of the method is verified by simulating the signal. Furthermore, the effectiveness and superiority of the method in gear fault diagnosis are verified by comparison with the fast kurtogram, cepstrum pre-whitening, and SAM.

Fault diagnosis of rolling bearings based on improved direct fast iterative filtering and spectral amplitude modulation

As the transient impulse components in early fault signals are weak and easily buried by strong background noise, the fault features of rolling bearings are difficult to be extracted effectively. Focusing on this issue, a novel method based on improved direct fast iterative filtering and spectral amplitude modulation (IDFIF-SAM) is presented for detecting the early fault of rolling bearings. First, the ratio of the average crest factor of autocorrelation envelope spectrum to the average envelope entropy is taken as the fitness function to search the optimal parameters of direct fast iterative filtering (DFIF) adaptively via particle swarm optimization (PSO). Then, the efficient kurtosis entropy (EKE) index is being employed to choose the suitable components to reconstruct the signal. Finally, the reconstructed signal is subjected to spectral amplitude modulation (SAM) to strengthen the impulse features. The superiority of improved direct fast iterative filtering (IDFIF) over fixed-parameter DFIF, fast iterative filtering (FIF), and hard thresholding fast iterative filtering (HTFIF) is clarified through the simulated signal. Moreover, the comparative experimental analysis shows that the proposed IDFIF-SAM method can identify the early fault feature of rolling bearings more effectively.

Differential Spectral Amplitude Modulation and its Applications in Rolling Bearing Fault Diagnosis

Differential Spectral Amplitude Modulation and its Applications in Rolling Bearing Fault Diagnosis

Spectral amplitude modulation and dynamic near-field displaying of all-silicon terahertz metasurfaces supporting bound states in the continuum

The bound states in a continuum (BICs) are objective physical phenomena that defy conventional wisdom, and they exist in the radiating continuous spectrum but remain perfectly localized with non-radiation, which is different from the traditional bound states. In this paper, we report in theory and experiment the high thickness all-silicon terahertz (THz) metasurfaces supporting BIC and quasi-BIC, which is realized by simple pairs of elliptical pillars. Meanwhile, we used an extra optical pump to modulate the transmission amplitude difference between BIC and quasi-BIC metasurfaces, to complete the active control from “On” state to “Off” state. We utilize the abundant amplitude gradation of metasurfaces with different asymmetric degrees to develop the polarization-dependent THz near-field displaying application that can make the grayscale characteristics of a photograph reappear and also be dynamically controlled at On state to Off state.

Multichromatic Polarization-Controlled Pulse Sequences for Coherent Control of Multiphoton Ionization

In this review, we report on recent progress in the generation and application of multichromatic polarization-tailored pulse sequences for the coherent control of multiphoton ionization (MPI) dynamics and present unpublished experimental results that complement our previous findings. Specifically, we utilize single-color, bichromatic, and trichromatic polarization-controlled pulse sequences generated by spectral amplitude, phase and polarization modulation of a carrier-envelope phase (CEP)-stable white light supercontinuum for MPI. The analysis of the number of ionization pathways and the number of distinct final free electron states shows that both increase significantly, but scale differently with the number of absorbed photons and the number of pulses in the sequence. In our experiments, ultrafast polarization shaping is combined with high-resolution photoelectron tomography to generate, control, and reconstruct three-dimensional photoelectron momentum distributions from atomic and molecular MPI. We discuss the use of polarization-controlled single-color and bichromatic pulse sequences in perturbative and non-perturbative coherent control of coupled electron-nuclear dynamics in molecules, atomic spin-orbit wave packet dynamics and the directional photoemission from atoms and chiral molecules. We compare the coherent control of CEP-insensitive intraband multipath interference in the MPI with a fixed number of photons with CEP-sensitive interband multipath interference in the ionization with a different number of photons. The generation and control of free electron vortices with even-numbered rotational symmetry by MPI with single-color pulse sequences is contrasted with the bichromatic control of CEP-sensitive electron vortices with odd-numbered rotational symmetry. To illustrate the potential of multichromatic pulse sequences for coherent control, we present a trichromatic scheme for shaper-based quantum state holography.

Axial Spectral Encoding of Space-Time Wave Packets

Space-time (ST) wave packets are propagation-invariant pulsed optical beams whose group velocity can be tuned in free space by tailoring their spatiotemporal spectral structure. To date, efforts on synthesizing ST wave packets have striven to maintain their propagation invariance. Here, we demonstrate that one degree of freedom of a ST wave packet---its on-axis spectrum---can be isolated and purposefully controlled independently of the others. Judicious spatiotemporal spectral amplitude and phase modulation yields ST wave packets with programmable spectral changes along the propagation axis, including red-shifting or blue-shifting spectra, or more sophisticated axial spectral encoding including bidirectional spectral shifts and accelerating spectra. In all cases, the spectral shift can be larger than the initial on-axis bandwidth, while preserving the propagation-invariance of the other degrees of freedom, including the wave packet spatiotemporal profile. These results may be useful in range finding in microscopy or remote sensing via spectral stamping.

Modeling and analysis of all-optical 2-bit and 4-bit synchronous up counter using silicon waveguide based all-optical JK flip-flop in Z domain

Placing reliance on Silicon waveguide based OMRR, this paper has brought a simple design of optically pumped 2-bit and higher bit counters using optical J–K flip flop.The present paper has used the non-linerarity characteristic of OMRR. A Pump pulse is applied from the top which causes switching operation. The Counter Circuit has been achieved by using the delay line signal process approach. Circuit’s performance has been assessed vide exploration of Z-domain model. The validity and viability of the present model is determined by evaluating the performance parameters such as Free Spectral Range, Q-factor, Amplitude modulation, ON-OFF ratio, Extinction Ratio (ER), Fineness and Contrast Ratio during simulation.

Rapid and dynamic detection of antimicrobial treatment response using spectral amplitude modulation in MZO nanostructure-modified quartz crystal microbalance

We report a dynamic and rapid detection of the response of S. epidermidis to various antimicrobial treatments utilizing the real-time spectral amplitude modulations of the magnesium zinc oxide nanostructure-modified quartz crystal microbalance (MZOnano-QCM) biosensor. The sensor consists of a quartz crystal microbalance (QCM) with magnesium zinc oxide (MZO) nanostructures grown directly on the sensing electrode using metalorganic chemical vapor deposition (MOCVD). Combining the high sensitivity detection of bacteria provided by the MZO nanostructures with the QCM's dynamic acoustic spectrum makes a highly-sensitive dynamic biosensor well-suited for monitoring viscoelastic transitions during drug treatment compared to the QCM's conventional frequency shift signals. We demonstrated dynamically monitoring the response of S. epidermidis to various concentrations of the drug ciprofloxacin, and response to three different antimicrobials vancomycin, oxacillin, and ciprofloxacin, using spectral amplitude modulations of the MZOnano-QCM. Our results indicate that the amplitude modulations exhibit high sensitivity to S. epidermidis response to different drug treatments compared to the conventional frequency shift signals of the device, allowing for rapid determination (within 1.5 h) of the efficacy of the antimicrobial drug. The high sensitivity demonstrated by the spectral amplitude modulations is attributed to the direct relationship of these signals to the viscoelastic transitions of the bacterial cells on the device's sensing area while responding to drug treatment. This relationship is established by the Butterworth-Van-Dyke (BVD) model of the MZOnano-QCM. Standard microbiological protocols and assays were performed to determine the optimal drug dosages and the minimum inhibitory concentrations to serve as the benchmark for the sensor data.

Condition monitoring and intelligent diagnosis of rolling element bearings under constant/variable load and speed conditions

Extensive research has been conducted for intelligent fault diagnosis and prognosis of rolling element bearings, a vital component in every rotating machinery, and many robust and reliable techniques have been developed thus far. The majority of the proposed approaches, however, are established for constant operational conditions and therefore encounter difficulties when working conditions vary, which is common in industrial applications. The reason is that many characteristics of a normal state of a system in one working condition might be similar to the characteristics of a defected one in another working condition. The aim of this paper is to develop a method that can differentiate between different health states of machinery, regardless of load and speed conditions. For this purpose, a newly proposed approach, namely spectral amplitude modulation (SAM), is employed to highlight various components of a signal with different energy levels. Subsequently, the impulsivity of these extracted signals’ envelope spectrum is computed to quantify their cyclostationarity level. These quantities could be further utilized as the input variables of machine learning algorithms for automated and intelligent diagnosis of bearings. In this paper, two methods for data classification, namely support vector machine (SVM) and subspace k-nearest neighbors, are employed. Moreover, the computed impulsiveness of signals contains information about the health state of machinery and therefore could be employed as a health indicator for online condition monitoring of machines. To thoroughly assess the potential of the proposed method for condition monitoring and intelligent diagnosis of machinery in constant and highly variable working conditions, it is implemented on data collected from three distinct test rigs, namely the IMS, PoliTo and FEMTO data sets. The damages on bearings in those experiments have different severity levels, types, and they are located on different components of the bearings. In addition to localized defects, distributed faults, which are advanced and critical stages of defects, are also studied in this research. This type of defect is more difficult to detect and has been largely overlooked due to the fact that the characteristics of its signals are different from localized ones and similar to other modulation sources.

Picosecond contrast degradation by surface imperfections in chirped-pulse-amplification stretchers.

In this paper, we propose a study of the picosecond temporal contrast degradation of ultrashort laser pulses by surface defects in pulse stretchers. In a chirped-pulse-amplification stretcher or compressor, dust and damages on the surface of an optical element lead to a spectral amplitude modulation. Furthermore, surface figure errors of optical elements happening where the pulse is spatially dispersed yield a modulation of the spectral phase. The influence of both amplitude and phase noise effects is numerically investigated using a hybrid ray-tracing method that enables treating separately the influence of noise sources, whether noise occurs in the near field or the far field. We show that the main issue in terms of picosecond contrast degradation is a combined effect of surface pattern distortion in the far field and phase-amplitude coupling caused by spatial frequency filters. Temporal domains can be defined, where the temporal contrast is dominated by different noise effects. The algorithm used in this paper is compared to the cross-correlation trace of a pulse. The conclusions emerging from the presented analysis are universally applicable to known grating stretcher geometries.

The spectral amplitude modulation: A nonlinear filtering process for diagnosis of rolling element bearings

Rolling element bearings are the critical parts of every rotating machinery and their failure is one of the main reason of the machine downtime and even breakdown. For this reason, various methods have been proposed in the past for their early diagnosis. Among them, envelope analysis and cyclic spectral analysis (CSA) are the most effective and widely used approaches, working according to the principle of linear filtering process of signals to remove undesirable components. However, in many cases machine structures are excited in different frequency ranges by impacts generated when defects are engaged, hence, the signal should be filtered for multiple frequency bands to completely extract the defect signals. Also, finding the proper frequency bands for demodulation is not always a simple task. To overcome these challenges, in this paper an empirical and automated nonlinear filtering process will be proposed in which different components of a signal are decomposed based on their powers. Then, their squared envelope spectra are computed to seek the presence of bearings characteristic frequencies. Therefore, this method can be seen as complementary to the narrowband amplitude demodulation techniques. The phase of each filtered component is similar to the phase of the original signal but the magnitude is transformed. The idea is that reconstruction of a signal only by its phase preserves many useful features. A similar approach is employed by cepstrum pre-whitening for bearings diagnosis to reduce the effect of powerful exogenous sources which mask the bearings signals and, despite its simplicity, has achieved noteworthy outcomes. Finally, the performance of the proposed method is validated on real case data recorded from two test rigs. Also, its effectiveness is investigated under constant and variable speed regimes and in presence of various level of Gaussian and non-Gaussian (highly impulsive) background noise.