Background Noise Components Research Articles

A novel optimal resonance band selection method for wheelset-bearing fault diagnosis based on tunable-Q wavelet transform

The detection of faults in the wheelset-bearing system is crucial for guaranteeing the safety of train operations. The core is to extract the optimal resonance band (ORB) and repetitive transient impact signals from the collected axle box acceleration signals. Accordingly, a novel automatic fault detection method called the bidirectional iterative merging multi-Q tunable-Q wavelet transform (BIMMQTQWT) is proposed to address the issue that existing methods are vulnerable to background noise and irrelevant components. First, a series of band-pass filters with almost constant bandwidth are constructed by the improved multi-Q tunable-Q wavelet transform (IMQTQWT) derived from the fault characteristics. Second, the fault information contained in each sub-band coefficient is preliminarily estimated using the correlative envelope comprehensive indicator (CECI). Third, the ORBs are automatically selected using the maximum CECI based on a strategy called bidirectionally merging adjacent frequency bands (BIMFBs). Finally, Envelope demodulation based on the ORB is executed followed by identifying bearing faults. The effectiveness in detecting multiple wheelset-bearing faults of the proposed method is validated through simulation and bench experiment signals. And the superior performance of the proposed method is exhibited compared with the existing average infogram and resonance sparse decomposition.

Iterative Laplace of Gaussian filter and improved Teager energy operator for bearing fault detection in gearboxes

Abstract The presence of cyclic impulses is considered to be a significant indicator of bearing defects. The extraction of fault-related information from gearboxes, however, poses a significant challenge due to the complex working conditions in gear transmission systems. These conditions often lead to fault-related impulse features being masked by inevitable background noise and vibration components. To address this issue, a novel iterative Laplace of Gaussian (LoG) filtering technique is proposed for the fault diagnosis field of bearings in gearboxes. An objective function incorporating Kurtosis statistics is used to iteratively update LoG coefficients instead of relying on fixed coefficients as in the original version, making the iterative LoG filtering technique better suited for handling fault-induced impact signals. Furthermore, an improved Teager energy operator (TEO) is developed to reduce the susceptibility of the original TEO to noise. The improvement involves incorporating a multi-resolution symmetric difference sequence into the discrete expression of TEO. This symmetric difference acts as a translation-invariant, sliding-window filter that, when combined with an energy operator, enables both energy detection and frequency filtering. The robustness against noise and sensitivity to frequency can be further improved by adjusting the multi-resolution parameter. The results of the experiments confirm the effectiveness of the proposed approach in identifying weak bearing fault features for gearboxes. Besides, the proposed method is also compared with the original LoG filter-based method and some competing methods, demonstrating its superior performance.

Possibilities of the X-ray diffraction data processing method for detecting reflections with intensity below the background noise component

The values of the signal-to-noise ratio are determined, at which the method of processing X-ray diffraction data reveals reflections with intensity less than the noise component of the background. The possibilities of the method are demonstrated on weak reflections of α-quartz. The method of processing X-ray diffraction data makes it possible to increase the possibilities of X-ray phase analysis in determining the qualitative phase composition of multiphase materials with a small (down to 0.1 wt.%) content of several (up to eight) phases.

Separation of Ambient Radio Noise and Radio Signals Received via Ionospheric Propagation

Systems for atmospheric research and wireless communication use the High Frequency (HF) radio spectrum. At these frequencies, typically up to 20 MHz, the ambient electromagnetic noise is stronger than the noise generated by the receiver itself, thereby limiting the sensitivity of the instruments. Especially in urban areas, the noise level is high. In remote rural environments, where artificial noise sources are absent, a much lower noise level is observed. It has been shown that this noise arrives via ionospheric propagation and consists of impulsive noise from lightning and a background component that resembles additive white Gaussian noise. To establish the absolute field strength of this background noise component, a direction- and polarization-agnostic antenna is realized by adding the power of two orthogonal antenna elements in the digital domain. To suppress radio signals arriving via ionospheric propagation—of which the spectral and temporal aspects are not known a priori—a novel adaptive filter is demonstrated that separates the background noise from the radio signals in the joint frequency-time domain. This method is demonstrated using measurements from a polarimetric experiment on 7 MHz in a remote rural area in Catalonia. The results are submitted to the International Telecommunication Union for the validation of ambient noise models.

Adaptive demodulation synchro-extracting transform for bearing time-varying fault feature extraction

Since bearing fault-induced impulses are time-varying under variable rotational speeds and often polluted by background noise and other interference components, it is still a challenging task for bearing feature extraction and diagnosis. As such, a novel bearing nonstationary fault feature extraction technique, termed adaptive demodulation synchro-extracting transform (ADSET) is developed in this paper. In the developed technique, the reset criterion is defined and introduced into the generalized demodulation transform (GDT) to transform the time-varying fault characteristic frequency (FCF) with strong anti-noise property; In addition, the local maximum criterion is developed and introduced into the synchro-extracting transform (SET) to extract fault-related harmonics and eliminate other interference components; Finally, the diagnosis index model is constructed to detect bearing fault location. The performance of the developed technique is verified using both simulated and different experiment signals, and the results show that rolling bearing fault is quantitatively characterized and accurately detected without rotational speed measurement.

Joint suppression of normal gear mesh component and background noise for early local fault detection based on dynamic evolutionary digital filter

Signal denoising is one of the most essential steps in mechanical fault diagnosis, which can help improve the signal-to-noise ratio (SNR) and find faults as earlier as possible. Many denoising techniques have been developed to suppress both Gaussian white and non-Gaussian background noise. However, most of them achieved poor performance in terms of assessing local gear fault in the early stage. The key problem is that only one or several gear tooth’s stiffness or displacement excitation are aroused in the beginning of local faults and most of the gear teeth or gear pairs are healthy, especially for the multi-stage gearbox. And both the normal gear meshing signals and background noise lead to low SNR of fault characteristics. To overcome this problem, this paper proposes a dynamic evolutionary digital filter (Dyna-EDF) method to realize the optimal suppression of background noise and normal gear meshing component in the acquired signal aiming to enhance the SNR of early fault characteristics. The proposed method belongs to an adaptive noise cancellation (ANC) method, which can find and suppress the relevant component between the acquired signal and reference signal based on an iterative optimization of a digital filter. In the proposed method, a filter parameter population and ANC structure is initialized and established firstly. Then the filter parameters of the population are dynamically adjusted to find the optimal filter based on cloning and mating evolutionary strategies. Furthermore, “survival of the fittest” idea is employed for the optimal filter searching and applied during each iteration in Dyna-EDF method to prevent the premature problem that found in traditional EDF. Finally, the derived optimal filter is applied to suppress the relevant component between the acquired signal and reference signal using an ANC process. Results show that the proposed Dyna-EDF method successfully increase the SNR of fault characteristics, both normal gear mesh component and background noise are suppressed effectively. The proposed Dyna-EDF method seems achieve better denoising performance than traditional EDF method, its improved versions and the traditional denoising methods.

Performance of NOMA-Based Dual-Hop Hybrid Powerline-Wireless Communication Systems

The mixture of wireless and power line communications (PLC) is vital for implementing new applications in smart grid and vehicular communications. In this work, we investigate the performance of non-orthogonal multiple access (NOMA) based dual-hop hybrid communication systems with decode-and-forward relay. The wireless channel is characterized by Nakagami- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</i> fading under an additive white Gaussian noise (AWGN), while the PLC channels are characterized by Log-normal distribution with Bernoulli Gaussian noise including both background and impulsive noise components. New closed-form expressions for the outage probability, the asymptotic outage probability and ergodic capacity are derived and verified via extensive representative simulations. For more insights on the outage performance, we analyze the diversity order. Additionally, we proposed a power allocation optimization technique to achieve an outage-optimal performance. The results show that the system outage probability improves as the impulsive noise index and the arrival probability of the impulsive component of the PLC additive noise decrease, while their effect is negligible on the ergodic capacity. Finally, the performance of the proposed system is compared against a benchmark OMA-based system.

A novel optimal demodulation frequency band extraction method of fault bearing based on power spectrum screening combination-gram

As the prerequisite for obtaining the optimal demodulation frequency band (ODFB), the frequency band division model must be delicately constructed. The division models in existing methods of obtaining ODFB are vulnerable to the background noise and irrelevant components, which will cause an unsuitable demodulation frequency band. To improve the performance of frequency band division model, this paper proposes a fault diagnosis method called power spectrum screening combination-gram (Psscgram). Firstly, a frequency band screening combination model based on the auto-regressive (AR) power spectrum is established to obtain the frequency band group of vibration signal. Secondly, the negative entropy (NE) is used to quantify the fault characteristics of the frequency band group. The frequency band with the largest NE value is extracted from the frequency band group, whose characteristic frequency is calculated via envelope spectrum. Finally, the robustness and the anti-interference of the proposed method is verified by simulated signals with different signal-to-noise ratio. Moreover, the ODFB extraction ability of the proposed method is proved by the analyses of three sets of bearing experimental signals.

Combining background noise and artificial masking to achieve privacy in sound zones

A private sound zone can be created by focusing a spoken message towards a target listener using a loudspeaker array. In practice, however, the reproduced speech cannot be completely contained within the target zone due to practical limits on the directivity of the array. Despite these limitations, the privacy of the message can be maintained if the leaked speech is sufficiently masked by noise. Two possible sources of this masking noise are considered in this article: the ambient noise in the reproduction environment, and an additional masking signal radiated by the loudspeaker array. The present article demonstrates that the process of designing a private audio system is significantly affected by the presence of ambient noise. A key complication is that temporal fluctuations and spatial non-uniformity in the ambient noise can reduce its effectiveness as a masker. These features also make it more difficult to estimate the corresponding reduction in the intelligibility of speech in each listening zone. To mitigate this spatial and temporal variance, it is proposed that systems should be designed to rely only on the masking provided by the diffuse, quasi-stationary background noise component of the environmental noise. It is shown that when systems utilise a combination of the background noise and an additional, artificial masker, a lower level of acoustic contrast is required from the system, compared to the case where the masking is supplied by the background noise exclusively.

Modulation signal bispectrum with optimized wavelet packet denoising for rolling bearing fault diagnosis

Transient impulses caused by local faults are critical informative indicators for rolling element bearing fault diagnosis. The methods for accurately extracting transient impulses while suppressing strong background noise and interference components have received extensive studies. In this article, a novel fault diagnosis scheme based on optimized wavelet packet denoising and modulation signal bispectrum is proposed, which takes advantage of the transient impulse enhancement of wavelet packet denoising and the demodulation ability of modulation signal bispectrum to diagnose bearing faults more accurately. First, the measured signals are decomposed into a series of time–frequency subspaces using wavelet packet transform. An optimal threshold value is selected based on the proposed threshold criterion by considering unbiased autocorrelation of envelope and Gini index of the transient impulses. Subsequently, the subspaces are denoised by the wavelet packet denoising with the optimized threshold value, and the master subspaces that containing the fault-related transient impulses are selected based on the Gini index indicator. Finally, the modulation signal bispectrum is utilized to further purify the signal and extract the modulation components contained in the transient impulses, and the suboptimal modulation signal bispectrum slices are selected based on the characteristic frequency intensity coefficient. The modulation signal bispectrum detector is then obtained by averaging the suboptimal modulation signal bispectrum slices to determine the type of the bearing faults. The proposed wavelet packet denoising-modulation signal bispectrum is validated based on the simulation and experimental studies. Compared with the variational mode decomposition and Teager energy operator, fast kurtogram as well as conventional modulation signal bispectrum, the proposed wavelet packet denoising-modulation signal bispectrum method has superior performance in extracting the fault feature of the incipient defects on different bearing components.

Sparse Elitist Group Lasso Denoising in Frequency Domain for Bearing Fault Diagnosis

The fault-induced impulse responses of localized bearing fault are usually interfered by the background noise and other harmonic components. They are strongly coupled together and are hard to be separated. It is crucial to develop a fast and reliable method to extract the impulse-based feature for online bearing fault diagnosis in the industry application. In this article, we propose a new sparse elitist group lasso denoising (SEGLD) algorithm in frequency domain to detect the incipient impulse-based fault feature, which is free of utilizing the prior knowledge. We first reveal the sparse characteristics of the bearing fault signals in frequency domain. Then, a tailored denoising model is proposed. To obtain a satisfactory analytical stationary solution, the Douglas-Rachford splitting solver is employed for the denoising model. Moreover, we explore the relationship between the best regularization parameters, the periodic information and the normalization estimated noise of the rolling bearing fault signal. A rule of adaptively selecting the best regularization parameters is demonstrated. Finally, the robustness and effectiveness of the proposed SEGLD algorithm are profoundly verified by the numerical simulation and two evaluation experiments under the conditions of early fault stage and low speed scenario. Also, it is demonstrated that the proposed approach outperforms the state-of-the-art method for extracting the weak fault feature.

Fault detection for planetary gearbox based on an enhanced average filter and modulation signal bispectrum analysis

Transient impulses are important information for machinery fault diagnosis. However, the transient features contained in the vibration signals generated by planetary gearboxes are usually immersed by a large amount of background noise and harmonic components. Even mathematical morphology (MM) is an excellent anti-noise signal processing method that can directly extract the geometry of impulse features in the time domain, but the four basic operators of MM can only extract one-way impulses while cannot extract the bidirectional impulses effectively at the same time. To accurately extract the impulse feature information, a novel method for fault detection of planetary gearbox based on an enhanced average (EAVG) filter and modulated signal bispectrum (MSB) is proposed. Firstly, the properties of the extracted impulses based on the four basic operators of MM will be divided into two categories of enhanced average operators. The four EAVG filters consist of the average weighted combination of enhanced average operators, and then the best EAVG filter is selected based on correlation coefficient to implement on the original vibration signal. It allows EAVG filter to extract positive and negative impulses of vibration signal, thereby improving the accuracy of planetary gearbox fault detection. Subsequently, the performance of the EAVG filter is influenced by the length of its structural element (SE), which is adaptively determined using an indicator based kurtosis. Then, the EAVG filter selects the optimal SE length to eliminate the interference of background noise and harmonic components to enhance the impulse components of the vibration signal. However, the nonlinear modulation components that are related to the fault types and severities are not extracted exactly and still remained in the filtered signal by EAVG. Finally, the MSB is utilized to the EAVG filtered signal to decompose the modulated components and extract the fault features. The advantages of EAVG over average (AVG) filter are clarified in the simulation study. In addition, the EAVG-MSB is validated by analyzing the vibration signals of planetary gearboxes with sun gear chipped tooth, sun gear misalignment and bearing inner race fault. The results indicate that the EAVG-MSB is effective and accurate in feature extraction compared with the combination morphological filter-hat transform (CMFH) and average combination difference morphological filter (ACDIF), and the feasibility of the EAVG-MSB are proved for planetary gearbox condition monitoring and fault diagnosis.

Improved local cepstrum and its applications for gearbox and rolling bearing fault detection

Cepstrum is a kind of powerful and widely used method in the fields of speech signal processing, echo signal detection, building acoustics, seismic analysis, condition monitoring and fault diagnosis. But in some circumstances, such as signals with localized distribution of spectrum or low signal-to-noise ratio (SNR) which are heavily drowned by noise, cepstrum fails to provide effective results. An improved local cepstrum is proposed on the basis of local cepstrum. The principle and algorithm of the proposed method are given. By introducing the autocorrelation denoising processing in the time domain and in the frequency domain individually, the background noise and nonharmonic components in vibration signal and power spectrum are reduced. In the mean time, the highly localized fault features in power spectrum are enhanced and converged to zero frequency in the power spectral autocorrelation function (PSAF). The difficulty of frequency band selection in local cepstrum analysis is solved with the help of spectral negentropy. At the step of transformation from the frequency domain to the quefrency domain, autoregressive (AR) spectrum estimation is adopted instead of the fast Fourier transform (FFT) for improving fault feature extraction effectiveness. The proposed method is employed to analyze simulated signal and experimental vibration signals of gearbox and rolling element bearing. In contrast to conventional cepstrum and local cepstrum, the proposed method has a strong ability to resist noise and is effective to detect the early fault.

A new adaptive solution based on joint acoustic noise and echo cancellation for hands-free systems

In this paper, we address the problem of joint acoustic noise and echo cancellations for hands-free systems. The problem of acoustic echo cancelation (AEC) in the presence of background noise is a real challenge for any actual system. In this paper, we propose a new system that combines both processes, i.e. ANC and AEC. In our approach, we propose a two-stages procedure as follows: in the first stage, we propose to use the forward blind source separation (FBSS) structure to cancel the background noise components that is superimposed to the acoustic echo signal in the same environment, this FBSS structure uses the two-channel normalized least mean square (TC-NLMS) adaptive algorithm to cancel the background noise from the primary signal. In the second step, we propose to use an AEC system based on a single channel NLMS (SC-NLMS) algorithm to efficiently suppress the acoustic echo signal. This new combination between the FBSS and the AEC system allows reducing the acoustic echo signal to lower mean square values (MSE) in the permanent regime, this behavior will not be possible without FBSS. The performance properties of the proposed algorithm in such environment (i.e. presence of background plus acoustic echo signals in the same time) is evaluated with competitive algorithm with various objective criteria (ERLE, SegSNR, and GainMSE).

Sparse representation based infrared small target detection via an online-learned double sparse background dictionary

For infrared small target detection, traditional sparse representation based methods decompose infrared images either over a background dictionary constructed using original image data or over a target dictionary generated by Gaussian intensity model. However, these methods can hardly be employed in practice, due to the time-consuming decomposition and the interference from heavy background clutter. To achieve a more efficient detection of infrared small targets, in this work, we propose a novel method based on sparse representation via an online-learned double sparse background dictionary. First, we employ the double sparsity model to construct a background dictionary and further propose a modified online learning algorithm to train the dictionary. Second, we use the sparse representation model to decompose the target image into background component, target component and noise component. Third, we propose an edge clutter suppression strategy to improve the robustness of detection. Experimental results on five real sequences show that the proposed method can not only detect targets precisely with low false alarm rate, but also suppress background clutters effectively.

Fault feature extraction for rolling element bearing diagnosis based on a multi-stage noise reduction method

To extract impulsive feature from vibration signals with strong background noise and interference components for accurate bearing diagnostics. A multi-stage noise reduction method is proposed based on ensemble empirical mode decomposition (EEMD), wavelet thresholding (WT) and modulation signal bispectrum (MSB). Firstly, noisy vibration signals are applied with EEMD to obtain a list of intrinsic mode functions (IMFs) that leverage the desired modulation components to different degrees. Then, a number of initial IMFs in the high frequency range, which are separated by using the mean of the standardized accumulated modes (MSAM) to have more modulation contents, are further denoised by a wavelet thresholding approach. These cleaned IMFs in the high frequency are combined with the low frequency IMFs to construct a pre-denoised signal that maintains the modulation properties of the raw signal. Finally, modulation signal bispectrum (MSB) is used to extract the modulation signature by suppressing further the residual random noise and deterministic interference components. This multi-stage noise reduction method was validated through a simulation study and two experimental fault cases studies of rolling element bearing. The results were more accurate and reliable in diagnosing the bearing inner and outer race defects in comparison with the individual use of the state-of-the-art EEMD and MSB.

Weak Degradation Characteristics Analysis of UAV Motors Based on Laplacian Eigenmaps and Variational Mode Decomposition.

Brushless direct current (BLDC) motors are the source of flight power during the operation of rotary-wing unmanned aerial vehicles (UAVs), and their working state directly affects the safety of the whole system. To predict and avoid motor faults, it is necessary to accurately understand the health degradation process of the motor before any fault occurs. However, in actual working conditions, due to the aerodynamic environmental conditions of the aircraft flight, the background noise components of the vibration signals characterizing the running state of the motor are complex and severely coupled, making it difficult for the weak degradation characteristics to be clearly reflected. To address these problems, a weak degradation characteristic extraction method based on variational mode decomposition (VMD) and Laplacian Eigenmaps (LE) was proposed in this study to precisely identify the degradation information in system health data, avoid the loss of critical information and the interference of redundant information, and to optimize the description of a motor’s degradation process despite the presence of complex background noise. A validation experiment was conducted on a specific type of motor under operation with load, to obtain the degradation characteristics of multiple types of vibration signals, and to test the proposed method. The results proved that this method can improve the stability and accuracy of predicting motor health, thereby helping to predict the degradation state and to optimize the maintenance strategies.

Fetal ECG extraction from time-varying and low-rank noninvasive maternal abdominal recordings

Objective: Noninvasive fetal electrocardiography is emerging as a low-cost and high-accuracy technology for fetal cardiac monitoring. Signal processing techniques have been used over the past fifty years in this domain. The current major challenges of this domain, addressed in this study are (1) fetal electrocardiogram (fECG) extraction from few numbers of maternal abdominal channels in low signal-to-noise ratios; (2) online fECG extraction; (3) automatic and online signal quality assessment and channel selection; and (4) accurate and robust fetal R-peak detection and ECG parameter extraction. Approach: Based on the theory of cyclostationarity, auxiliary maternal ECG channel(s) are synthetically constructed and augmented with the input channels. The augmented data are used to develop a robust multichannel source separation algorithm for online/offline fECG extraction, from as few as a single channel, and an accurate fetal R-peak detector using a two-pass matched filter. Several robust signal quality indexes (SQI) and a voting strategy are also proposed for automatic fetal signal quality assessment. Main results: It is shown that the fECG and the fetal R-peaks can be accurately extracted from standard online available datasets, for which classical source separation methods (requiring many channels) had previously failed. The signal quality indexes fully automate the extraction and channel selection procedure. Finally, the proposed R-peak detector is highly robust to background noise and residual maternal R-peak components. Significance: The proposed methods for fECG extraction, R-peak detection and automatic channel selection are evaluated (visually and numerically), on two online available datasets and compared with recently developed algorithms. The proposed algorithm is statistically shown to outperform the benchmarks in terms of average and standard deviation.

Chemicalome and metabolome profiling of polymethoxylated flavonoids in Citri Reticulatae Pericarpium based on an integrated strategy combining background subtraction and modified mass defect filter in a Microsoft Excel Platform

Detection of metabolites in complex biological matrixes is a great challenge because of the background noise and endogenous components. Herein, we proposed an integrated strategy that combined background subtraction program and modified mass defect filter (MMDF) data mining in a Microsoft Excel platform for chemicalome and metabolome profiling of the polymethoxylated flavonoids (PMFs) in Citri Reticulatae Pericarpium (CRP). The exogenously-sourced ions were firstly filtered out by the developed Visual Basic for Applications (VBA) program incorporated in the Microsoft Office. The novel MMDF strategy was proposed for detecting both target and untarget constituents and metabolites based on narrow, well-defined mass defect ranges. The approach was validated to be powerful, and potentially useful for the metabolite identification of both single compound and homologous compound mixture. We successfully identified 30 and 31 metabolites from rat biosamples after oral administration of nobiletin and tangeretin, respectively. A total of 56 PMFs compounds were chemically characterized and 125 metabolites were captured. This work demonstrated the feasibility of the integrated approach for reliable characterization of the constituents and metabolites in herbal medicines.

Phase-shift extraction algorithm for blind phase-shifting holography based on the quotient of inner products

This paper presents a phase-shift extraction method for blind phase-shifting holography applications based on the quotient of inner products. The method only requires acquiring three-frame holograms with arbitrary unequal phase-shift values and the intensity of the reference wave, without requiring the object wave intensity. Two phase shift values can be extracted precisely using the algorithm, which can remove the background noise and DC component effects. Both simulated and experimental results have demonstrated the phase step retrieval accuracy and the feasibility of reconstructing the complex amplitude of the object wave.