Periodic Impulses Research Articles

Velocity pulse effects of near-fault earthquakes on a high-speed railway vehicle-ballastless track-benchmark bridge system

The near fault (NF) line waves send out signal envelopes that oscillate over lengthy periods of time with periodic impulses. Like train bridges, train tracks demonstrate comparable track-bridge (TB) motion dynamics. Using these coupling dynamics, are the high-speed train-track-bridge (HSTTB) system designs sensitive to those parameters? This research incorporates a Finite Element Analysis (FEA) technique developed for simulating the dynamic reactions of the coupled TB system when faced with simultaneous NF lateral and vertical ground motions (GMs). For the first time, data from the pre-commissioning field testing of the Beijing-Shanghai high-speed train are utilized to validate the Train-Track-Bridge Dynamic Analysis (TTBDA) test. As a matter of fact, the current research has concentrated on the running safety of the high-speed train's operations, as well as the possible derailment mechanism of the high-speed train, in light of the far-field (FF) earthquakes. This analysis reveals that the NF GMs in the bridge structure's seismic reactivity are considerable. Many high-speed train derailments are due to frequent wheel displacement, elevated wheels, and significant lateral motion. The data discovered in the field may give engineers vital information for calculating relevant situations and railroad engineering projects.

Feature Extraction Using Sparse Kernel Non-Negative Matrix Factorization for Rolling Element Bearing Diagnosis.

For early fault detection of a bearing, the localized defect generally brings a complex vibration signal, so it is difficult to detect the periodic transient characteristics from the signal spectrum using conventional bearing fault diagnosis methods. Therefore, many matrix analysis technologies, such as singular value decomposition (SVD) and reweighted SVD (RSVD), were proposed recently to solve this problem. However, such technologies also face failure in bearing fault detection due to the poor interpretability of the obtained eigenvector. Non-negative Matrix Factorization (NMF), as a part-based representation algorithm, can extract low-rank basis spaces with natural sparsity from the time–frequency representation. It performs excellent interpretability of the factor matrices due to its non-negative constraints. By this virtue, NMF can extract the fault feature by separating the frequency bands of resonance regions from the amplitude spectrogram automatically. In this paper, a new feature extraction method based on sparse kernel NMF (KNMF) was proposed to extract the fault features from the amplitude spectrogram in greater depth. By decomposing the amplitude spectrogram using the kernel-based NMF model with L1 regularization, sparser spectral bases can be obtained. Using KNMF with the linear kernel function, the time–frequency distribution of the vibration signal can be decomposed into a subspace with different frequency bands. Thus, we can extract the fault features, a series of periodic impulses, from the decomposed subspace according to the sparse frequency bands in the spectral bases. As a result, the proposed method shows a very high performance in extracting fault features, which is verified by experimental investigations and benchmarked by the Fast Kurtogram, SVD and NMF-based methods.

A 1.5D Spectral Kurtosis-Guided TQWT Method and Its Application in Bearing Fault Detection

Bearings are the key parts of rotating machinery, and their operation status is related to the operation safety of the whole equipment. Vibration signals often contain periodic impulse components which can reflect the fault state of bearings. However, due to the interference of signal transmission path and the influence of operating environment noise, the periodic impulse components in the signal are often submerged by the nonperiodic transient impulse components, modulation harmonic components, and noise components. Therefore, the core problem of bearing fault diagnosis theory is used to accurately extract the frequency band of bearing fault state information and suppress the frequency band of interference information. In this paper, the signal is processed by the tunable Q-factor wavelet transform (TQWT), the midfrequency band of the signal is tightly divided by selecting different Q-values, and the 1.5D spectral kurtosis defined in frequency domain is used to select the optimal subband. Simulated analysis shows that this method can avoid low-frequency harmonic interference, nonperiodic transient impulse components, and strong noise components in the time domain. Therefore, it can effectively realize the selection of the subbands of periodic impulse components and effectively extract fault feature information. Through experimental signal analysis, TQWT has good sparsity decomposition characteristics and can reasonably divide the signal frequency band, so as to separate the useful fault characteristic frequency band and interference frequency band. At the same time, compared with the kurtosis index, 1.5D spectral kurtosis has better robustness and resolution for low signal-to-noise ratio signals, which can achieve the purpose of fault characteristic band extraction.

Bearing fault diagnosis method based on adaptive maximum cyclostationarity blind deconvolution

Blind deconvolution has been proved to be an effective method for fault detection since it can recover periodic impulses from mixed fault signals convoluted by noise and periodic impulses. As a new blind deconvolution technique, maximum cyclostationarity blind deconvolution (CYCBD) has great advantages over minimum entropy deconvolution (MED), maximum correlation kurtosis deconvolution (MCKD), and optimal minimum entropy deconvolution (MOMEDA) in processing bearing fault signals. However, CYCBD has the following two defects: Cyclic frequency needs to be determined in advance; The filter length of CYCBD affects its ability to recover impulses. The ability of CYCBD to recover the impulse will increase with the increase of filter length, but the signal will be distorted if the length is too large. Besides, the time cost will increase significantly after the length is increased. In this paper, an optimization strategy of CYCBD parameters is proposed, and then an adaptive maximum cyclostationarity blind deconvolution (ACYCBD) is proposed. Firstly, aiming at the determination of cyclic frequency, this paper proposes a cyclic frequency set estimation method based on autocorrelation function of morphological envelope, and the validity of the method is verified by simulation and experiment. Secondly, for the second problem, after considering the performance and time cost of CYCBD, the performance efficiency ratio index is proposed. Then, the equal-step search strategy is used to adaptively select the filter length. Finally, the effectiveness of the method is verified by simulation and experiment.

Weak Signals Detection by Acoustic Metamaterials-Based Sensor

Weak acoustic signals detection from heavy background noise plays an important role in different research fields. However, the detection limit, namely the detection capability of minimal detectable pressure, of sensitivity still hinders the performance of ordinary acoustic sensors. We propose an acoustic metamaterials-based sensor for weak signals detection. We show that the trapezoidal structure with gradient refractive index (GRIN) can effectively realize the pressure field enhancement through acoustic wave compression effect. The acoustic pressure amplitudes can be amplified more than 20 times around the maximum pressure gain frequencies even with different thicknesses of acrylic plates and measured positions, which makes it possible to achieve a broadband acoustic enhancement. Moreover, we numerically demonstrate that both harmonic and periodic impulse signals can be detected much easier based on the GRIN metamaterial due to the acoustic enhancement. In addition, we also experimentally demonstrate that harmonic and periodic impulse signals can be effectively recovered from the background noise due to the improved signal to noise ratios (SNRs). All the results indicate that acoustic metamaterials-based sensor can be well used for weak acoustic signals detection.

Research on unknown fault diagnosis of rolling bearings based on parameter-adaptive maximum correlation kurtosis deconvolution.

Maximum correlation kurtosis deconvolution (MCKD) is an effective means of identifying the periodic impulses of fault signals. However, the multiple input parameters required by MCKD complicate the process of fault diagnosis. To overcome this drawback, a new method for identifying fault types based on a parameter-adaptive MCKD method is proposed. First, an improved adaptive variational mode decomposition is developed to denoise the raw signal. The improved method adopts the weighted envelope entropy, which is constructed by combining the envelope entropy with the kurtosis, allowing the salience of the denoising performance to be evaluated. Furthermore, the mean maximum correlation kurtosis is constructed to allow the specification of fault types and the corresponding parameters. Finally, two rolling bearing test datasets are used to demonstrate the strong adaptability of this method compared with other adaptive techniques.

Adaptive periodic mode decomposition and its application in rolling bearing fault diagnosis

As is known to all, rolling bearing fault will induce periodic impulses. Although the existing fault diagnosis methods, such as wavelet transform (WT) and ensemble empirical mode decomposition (EEMD), can effectively diagnose rolling bearing fault, their weak period recognition abilities limit their application in rolling bearing fault diagnosis. The Ramanujan subspace is introduced from the perspective of period recognition and extraction, and an adaptive periodic mode decomposition (APMD) method that can extract periodic components (PCs) without setting any parameters is proposed in this paper. APMD determines the major periods (MPs) in the signal by periodicity measurement, and projects the signal into respective Ramanujan subspaces of MPs to form the corresponding PCs. The analysis results of rolling bearing signals show that APMD has excellent ability to identify and extract PCs and is a valid method for rolling bearing fault diagnosis.

Time optimal control to a partial stochastic differential system with pseudo almost periodic coefficients

In this paper, we study the time optimal control of a class of partial stochastic impulsive functional differential systems with pseudo almost periodic coefficients in the α-norm functional spaces. Firstly, the existence of p-mean piecewise pseudo almost periodic mild solutions for the impulsive stochastic control systems is investigated by utilising stochastic analysis, analytic semigroup, the fixed-point techniques with fractional power arguments. Secondly, the existence of time optimal pairs of a system governed by partial stochastic impulsive functional differential equations is also obtained. Finally, two examples are provided to illustrate the time optimal control problems of parabolic control system with pseudo almost periodic coefficients and impulses.

Adaptive maximum second-order cyclostationarity blind deconvolution and its application for locomotive bearing fault diagnosis

Maximum second-order cyclostationarity blind deconvolution (CYCBD) outperforms other deconvolution methods in retrieving the weak periodic impulses related to bearing incipient faults. However, the main challenge in the practical application of CYCBD is how to set several key parameters appropriately, the uppermost of which is the targeted cyclic frequency or fault period. It may attribute to the fact that the advantage of CYCBD is greatly compromised by the provided period. To overcome the above limitations, an adaptive CYCBD (ACYCBD) is presented in this article. In the proposed method, a powerful tool, envelope harmonic product spectrum (EHPS), is tailored to estimate the true cyclic frequency or period precisely. Then, the estimated result instead of a coarsely provided value is regarded as the targeted cyclic frequency. Furthermore, in the presence of heavy energy harmonics or strong external noise, EHPS still has strong robustness in the fault period identification. Compared with the original CYCBD, ACYCBD can extract the weak impulses submerged in the raw vibration signal without any prior information about the period. Finally, the effectiveness and advantages of ACYCBD are revealed by employing it on the synthesized signals and experimental data collected from a locomotive bearing test rig.

Stability and stabilization of linear impulsive systems with large impulse-delays: A stabilizing delay perspective

This paper deals with the stability and stabilization of linear impulsive systems with large impulse-delays. To explore the stabilizing effect of the time delay in the discrete-time dynamics, a switched impulsive system approach is developed to establish non-conservative stability criteria for linear impulsive systems with periodic impulses and constant delay. Subsequently, sufficient conditions are given for stability of the impulsive systems with aperiodic impulses and time-varying delay by quasi-periodic Lyapunov function methods. By exploiting the particular structure of the stability conditions, impulsive stabilization problem using artificial impulse-delay is further discussed. These results are applied to sampled-data systems with input delay. Compared with the well-known input-delay approach, the proposed switched impulsive system approach not only provides simple stability conditions but also captures the stabilizing effect of the delay. Two numerical examples illustrate the proposed theoretical results.

HAZ Structure Formation of Carbonaceous Steel at Impulse Welding

For increase of mining technique capacity at production and restoration wear resistant facings are widely applied. In this work, impacts of impulse welding of an intermediate layer on structural factors and facture toughness of wear resistant surfacing are studied. It, is established, that applying an intermediate layer with periodic impulse «teeth» forms a heterogeneous texture of intermediate layer, causing higher fracture toughness of wear resistant surfacing. It, is shown that defects made by current impulse have promoted a fine-grained structure of HAZ facing and grain-boundary allocation of grainy pearlite.

Maximum cross-correlated kurtosis-based unsaturated stochastic resonance and its application to bearing fault diagnosis

Considering the random impulses of mechanical noise and the limitations involved while identifying mechanical fault impulse signals via traditional measurement indices of signal-to-noise ratio, which require the characteristic frequency to be known in advance, this study proposes an adaptive unsaturated stochastic resonance method employing maximum cross-correlated kurtosis as the signal detection index. The proposed method combines the features of a cross-correlated coefficient to indicate periodic fault transients and those of spectrum kurtosis to locate these transients in the frequency domain. Actual vibration signals collected from motor and gear bearings subjected to heavy noise are used to demonstrate the effectiveness of the proposed method. Through a coarse tree-based machine learning method, the proposed method is verified to be more suitable for explaining the periodic impulse components of bearing signals, as compared to the ensemble empirical mode decomposition denoising method and unsaturated stochastic resonance using the kurtosis-intercorrelation index.

Early detection of bearing faults using minimum entropy deconvolution adjusted and zero frequency filter

A method based on minimum entropy deconvolution with convolution adjustment and zero frequency filter is presented for the identification of weak faults in rolling element bearings. Localized fault present in rolling element bearings causes periodic impulses in the bearing vibration signal. The zero frequency filtering of the bearing vibration signal keeps only the localized disturbances at the impulse locations while attenuating the non-impulsive components of the signal. The effectiveness of zero frequency filtering depends on the strength of impulses present in the measured faulty bearing signal in time domain. In the present work, Minimum entropy deconvolution adjusted is used as a preprocessor to improve the strength of impulses in the measured time-domain bearing signal. The effectiveness of the proposed algorithm is tested with simulated signals for the faulty bearing vibration at different levels of added Gaussian noise. The algorithm is also validated using experimental bearing vibration dataset. Results from the proposed algorithm are compared with the results of the zero frequency filter and local mean subtraction-based technique for rolling element bearings’ fault identification. The proposed algorithm performs better detection in case of a weak fault signal.

The Enkurgram: A characteristic frequency extraction method for fluid machinery based on multi-band demodulation strategy

Modulation frequency extraction of fluid machinery is significant, not only for the condition monitoring and fault diagnosis of cooperative targets in industrial fields but also for object detection and information prejudgment of non-cooperative targets in military applications. However, most existing demodulation methods tend to show poor performance due to the ubiquitous heavy Gaussian and non-Gaussian noise. Especially, narrowband demodulation methods such as Kurtogram are in a dilemma due to the signal characteristic of multi-wideband carrier wave in fluid machinery. In this paper, the Enkurgram is proposed for multiple demodulation frequency bands selection based on the combination of energy factor and shape factor. This method shows excellent demodulation capability in both simulation analysis and applications to fluid machinery. Firstly, an Amplitude-Modulated (AM) signal model with multi-wideband carrier wave is established. Unlike other narrowband demodulation methods, multiple non-overlapping frequency bands are selected by Enkurgram for demodulation based on the composite criterion of Spectral Energy (SE) and Spectral Kurtosis (SK). Moreover, the demodulation performance is quantified by the proposed Signal Characterization Ratio (SCR). Secondly, the effectiveness of Enkurgram is validated by simulation signals under different noise interference situations, including white Gaussian noise, stochastic impulse interference, periodic impulse interference, and contaminated modulation wave. Finally, the proposed method is verified by the actual signals from centrifugal pump and propeller, respectively. The analysis results prove that the Enkurgram has satisfactory demodulation capability in dealing with signals under low Signal-to-Noise Ratio (SNR) levels or with non-Gaussian noise, which is the Achilles’ heel of SK-based methods.

Simplified Harmonic Rejection Mixer Analysis and Design Based on a Filtered Periodic Impulse Model

Hard-switched mixers have attractive linearity and noise properties, but are plagued by spurious responses due to the harmonic content of square-wave local oscillator (LO) signals. Harmonic rejection mixers (HRMs) reject (some of) these responses and can be constructed combining polyphase mixing and amplitude weighting. This brief presents a generalized model for the analysis of the harmonic rejection (HR) of such HRMs, based on circular convolution. We show that the effective LO signal can be modeled as a periodic Dirac impulse filtered by a time-discrete filter. For a multi-stage HR system, this filter consists of multiple stages as well, and the coefficients of each stage can be found simply by inspection. The total HR is shown to be the sum (in dB) of the rejection of the individual filtering stages, highly simplifying analysis and design of HRMs.

Modelling and dynamic behaviour analysis of the software rejuvenation system with periodic impulse

ABSTRACT Software rejuvenation is a policy to counter the phenomenon of software ageing. However, how to implement software rejuvenation is still an important issue. In this paper, periodic impulse control is proposed as an effective tool to perform software rejuvenation and improve the availability of software system. First, we formulate the software rejuvenation system with periodic impulse by a group of coupled differential equations with impulsive action. Then the well-posedness of the system is demonstrated by using operator semigroup theory. At the end of the paper, numerical examples are shown to illustrate the dynamic behaviour of the system and the relationship between the system instantaneous availability and the impulsive indexes, including impulse interval and impulse strength, which yields that the system availability can be improved by adjusting the impulsive indexes.

Research on Fault Extraction Method of CYCBD Based on Seagull Optimization Algorithm

Maximum cyclostationarity blind deconvolution (CYCBD) can recover the periodic impulses from mixed fault signals comprised by noise and periodic impulses. In recent years, blind deconvolution has been widely used in fault diagnosis. However, it requires a preset of filter length, and inappropriate filter length may cause the inaccurate extraction of fault signal. Therefore, in order to determine filter length adaptively, a method to optimize CYCBD by using the seagull optimization algorithm (SOA) is proposed in this paper. In this method, the ratio of SNR to kurtosis is used as the objective function; firstly, SOA is used to search the optimal filter length in CYCBD by iteration, and then it uses the optimal filter length to perform CYCBD; finally, the frequency‐domain waveform is determined through Fourier transformation. The method proposed is applied to the fault extraction of a simulated signal and a test vibration signal of the closed power flow gearbox test bed, and the fault frequency is successfully extracted, in addition, using maximum correlation kurtosis deconvolution (MCKD) and multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) to compare with CYCBD‐SOA, which validated availability of the proposed method.

Adaptive Morphological Analysis Method and Its Application for Bearing Fault Diagnosis

The vibration response of faulty bearing is always characterized by periodic transient impulses in the signal. Generally, these fault-related features are inevitably submerged in noise and harmonic components. Mathematical morphology is an excellent method of noise reduction, which can retain the detail information of impulses in the time domain. However, the filtering effect of traditional morphological operator (MO) might be easily affected by random impulses, and the proper selection of the structure element (SE) depends heavily on the experience of researchers. In order to effectively remove these interferences and extract the fault features accurately, an improved method, named adaptive morphological filter (AMF), is proposed in this article. This method utilizes autocorrelation to lift MO in time domain to enhance periodic components, and the scale of SE can, therefore, be calculated with the local maximum of the autocorrelation spectrum. Since the selection of the optimal SE scale is adaptive, researchers’ experience is no longer needed, and there is also no need to calculate the fault characteristic frequency (FCF) for the determination of maximum scale of SE. The vibration and acoustical signals of faulty locomotive wheel set bearing are analyzed with this method, and the results verify its effectiveness and ability.

Subspace Identification of Linear Time-Periodic Systems With Periodic Inputs

This paper proposes a new methodology for subspace identification of linear time-periodic (LTP) systems with periodic inputs. This method overcomes the issues related to the computation of frequency response of LTP systems by utilizing the frequency response of the time-lifted system with linear time-invariant structure instead. The response is estimated with an ensemble of input-output data with periodic inputs. This allows the frequency-domain subspace identification technique to be extended to LTP systems. The time-aliased periodic impulse response can then be estimated and the order-revealing decomposition of the block-Hankel matrix is formulated. The consistency of the proposed method is proved under mild noise assumptions. Numerical simulation shows that the proposed method performs better than multiple widely-used time-domain subspace identification methods when an ensemble of periodic data is available.

Adaptive correlated Kurtogram and its applications in wheelset-bearing system fault diagnosis

As one of the crucial subsystems, the performance of the wheelset-bearing system will significantly affect the running safety, stability and comfort of a high-speed train. Compared with the traditional rotating machinery vibration signals, those of wheelset-bearing systems have a distinct difference, for instance, the wheelset tread damage induced vibrations and bearing fault-related vibrations coexist and have similar wave-forms. Therefore, it is necessary to advance a reasonable method to identify the two kinds of cyclo-stationary components. To achieve this purpose, the adaptive correlated Kurtogram (ACK) is proposed. The proposed ACK method has two advantages: the first one is it can adaptively generate the paving of the plane for Kurtogram by using scale-space representation (SSR) to guarantee the detected boundaries cover the resonant frequency band well; the second one is it can highlight the special periodic impulses by correlated kurtosis. The scale-scale plane (SSP), which is obtained by SSR, is used to represent the frequency distribution characteristic of the acquired signal, firstly. After that, the paving of the plane is arbitrarily divided based on the SSP. Then the empirical wavelet transform (EWT) is employed to construct the sub-band signals according to the paving of the plane. The correlated kurtosis values with interesting periodic are calculated to distinguish the special repetitive transients. The nodes with the largest correlated kurtosis values are used to conduct a further envelope spectrum analysis. ACK method improves the effectiveness of the identification of the frequency bands containing impact information, and is capable of detecting multiple frequency bands simultaneously. The proposed ACK method is demonstrated by the simulated signal and two experimental signals acquired from a wheelset-bearing system test bench. The results indicate that the ACK can simultaneously extract the wheelset tread damage and bearing fault-related vibration component, and it is superior to the other traditional methods.