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The analysis of vibration or acoustic signals is most widely used in the health diagnosis of rolling element bearings. One of the main challenges for vibration or acoustic bearing diagnosis is that the weak signature of incipient defects is generally swamped by severe surrounding noise in the acquired signals. This problem can be solved by the stochastic resonance (SR) approach, which is to enhance the desired signal by the aid of noise. This paper presents an adaptive multiscale noise tuning SR (AMSTSR) for effective and efficient fault identification of rolling element bearings. A new criterion, called weighted power spectrum kurtosis (WPSK), is proposed as the optimization index without prior knowledge of the bearing fault condition. The WPSK concerns both the kurtosis in signal power spectrum and the similarity to a sinusoidal signal in signal waveform, thus it can balance the enhancement of possible characteristic frequency in the frequency domain and the regularity of the signal in the time domain for the SR performance. Two parameters in the AMSTSR, including the cutoff wavelet decomposition level and the tuning parameter, are simultaneously optimized based on the WPSK index through the artificial fish swarm algorithm. The AMSTSR is further applied to the health diagnosis of rolling element bearings and four experimental case studies verify the effectiveness of the proposed method in adaptive identification of the bearing characteristic frequencies. [ABSTRACT FROM PUBLISHER]

The sensitive feature extraction from vibration signals is still a great challenge for effective fault classification of rolling element bearing. Current fault classification generally depends on feature pattern difference of different fault classes. This paper explores the active role of healthy pattern in fault classification and proposes a new fusion feature extraction method based on locality preserving projections (LPP). This study intends to discover the local feature pattern difference between each bearing status and the healthy condition to characterize and discriminate different bearing statuses. Specifically, the proposed fusion feature is achieved by two main steps. In the first step, a two-class model is firstly constructed for each class by using this class of signals and healthy condition signals. Then a fusion mapping is generated by mathematically combing the mappings of the LPP or its improvement for all two-class models. In the second step, the LPP is further applied to reduce the fusion mapping dimension, which is to find more sensitive low-dimensional information hidden in the high-dimensional fusion feature structure. The final achieved fusion feature can enhance the discrimination between all classes by improving the between-class scatter and within-class scatter for fault classification. Experimental results using different bearing fault types and severities under different loads show that the proposed method is well-suited and effective for bearing fault classification. [ABSTRACT FROM AUTHOR]

Stochastic resonance (SR), a noise-assisted tool, has been proved to be very powerful in weak signal detection. The multiscale noise tuning SR (MSTSR), which breaks the restriction of the requirement of small parameters and white noise in classical SR, has been applied to identify the characteristic frequency of a bearing. However, the multiscale noise tuning (MST), which is originally based on discrete wavelet transform (DWT), limits the signal-to-noise ratio (SNR) improvement of SR and the performance in identifying multiple bearing faults. In this paper, the wavelet packet transform (WPT) is developed and incorporated into the MSTSR method to overcome its shortcomings and to further enhance its capability in multiple faults detection of bearings. The WPT-based MST can achieve a finer tuning of multiscale noise and aims at detecting multiple target frequencies separately. By introducing WPT into the MST of SR, this paper proposes an improved SR method particularly suited for the identification of multiple transient faults in rolling element bearings. Simulated and practical bearing signals carrying multiple characteristic frequencies are employed to validate the performance improvement of the proposed method as compared to the original DWT-based MSTSR method. The results confirm the good capability of the proposed method in multi-fault diagnosis of rolling element bearings. [ABSTRACT FROM AUTHOR]

Highlights: [•] Propose a synthetic technique for trackside detection of railroad bearing defects. [•] Solve three problems in trackside acoustic signal of defective roller bearing. [•] Propose the VRRD method to extract the envelope using the Doppler shift property. [•] Envelope resampling is followed by logarithmic transform and non-linear detrending. [•] Simulation and experiments verify good performance of the proposed technique. [Copyright &y& Elsevier]

Abstract: The vibration or acoustic signal from rotating machinery with localized fault usually behaves as the form of amplitude modulation (AM) and/or frequency modulation (FM). The demodulation techniques are conventional ways to reveal the fault characteristics from the analyzed signals. One of these techniques is the time-scale manifold (TSM) ridge demodulation method with the merits of good time–frequency localization and in-band noise suppression properties. However, due to the essential attribute of wavelet ridge, the survived in-band noise on the achieved TSM will still disturb the envelope extraction of fault-induced impulses. This paper presents an improved TSM ridge demodulation method, called exchanged ridge demodulation of TSM, by combining the benefits of the first two TSMs: the noise suppression of the first TSM and the noise separation of the second TSM. Specifically, the ridge on the second TSM can capture the fault-induced impulses precisely while avoiding the in-band noise smartly. By putting this ridge on the first TSM, the corresponding instantaneous amplitude (IA) waveform can represent the real envelope of pure faulty impulses. Moreover, an adaptive selection method for Morlet wavelet parameters is also proposed based on the smoothness index (SI) in the time-scale domain for an optimal time-scale representation of analyzed signal. The effectiveness of the proposed method is verified by means of a simulation study and applications to diagnosis of bearing defects and gear fault. [Copyright &y& Elsevier]

Abstract: The ultrasonic guided wave is widely used for structure health monitoring with the sparse piezoelectric actuator/transducer array in recent decades. It is based on the principle that the damage in the structure would reflect or scatter the wave pulse and thus, the damage-scattered signal could be applied as the feature signal to distinguish the damage. Precise measurement of time of the flight (TOF) of the propagating signal plays a pivotal role in structure damage localization. In this paper, a time–frequency analysis method, Wigner–Ville Distribution (WVD), is applied to calculate the TOF of signal based on its excellent time–frequency energy distribution property. The true energy distribution in the time–frequency domain is beneficial to reliably locate the position of damage. Experimental studies are demonstrated for damage localization of one-dimensional and two-dimensional structures. In comparison with traditional Hilbert envelope and Gabor wavelet transform methods, the proposed WVD-based method has better performance on the accuracy and the stability of damage localization in one-dimensional structure. In addition, the proposed scheme is validated to work effectively for damage imaging of a two-dimensional structure. [Copyright &y& Elsevier]

Abstract: The diagnosis of train bearing defects plays a significant role to maintain the safety of railway transport. Among various defect detection techniques, acoustic diagnosis is capable of detecting incipient defects of a train bearing as well as being suitable for wayside monitoring. However, the wayside acoustic signal will be corrupted by the Doppler effect and surrounding heavy noise. This paper proposes a solution to overcome these two difficulties in wayside acoustic diagnosis. In the solution, a dynamically resampling method is firstly presented to reduce the Doppler effect, and then an adaptive stochastic resonance (ASR) method is proposed to enhance the defective characteristic frequency automatically by the aid of noise. The resampling method is based on a frequency variation curve extracted from the time–frequency distribution (TFD) of an acoustic signal by dynamically minimizing the local cost functions. For the ASR method, the genetic algorithm is introduced to adaptively select the optimal parameter of the multiscale noise tuning (MST)-based stochastic resonance (SR) method. The proposed wayside acoustic diagnostic scheme combines signal resampling and information enhancement, and thus is expected to be effective in wayside defective bearing detection. The experimental study verifies the effectiveness of the proposed solution. [Copyright &y& Elsevier]

The research in fault diagnosis for rolling element bearings has been attracting great interest in recent years. This is because bearings are frequently failed and the consequence could cause unexpected breakdown of machines. When a fault is occurring in a bearing, periodic impulses can be revealed in its generated vibration frequency spectrum. Different types of bearing faults will lead to impulses appearing at different periodic intervals. In order to extract the periodic impulses effectively, numerous techniques have been developed to reveal bearing fault characteristic frequencies. In this study, an adaptive varying-scale morphological analysis in time domain is proposed. This analysis can be applied to one-dimensional signal by defining different lengths of the structure elements based on the local peaks of the impulses. The analysis has been first validated by simulated impulses, and then by real bearing vibration signals embedded with faulty impulses caused by an inner race defect and an outer race defect. The results indicate that by using the proposed adaptive varying-scale morphological analysis, the cause of bearing defect could be accurately identified even the faulty impulses were partially covered by noise. Moreover, compared to other existing methods, the analysis can be functioned as an efficient faulty features extractor and performed in a very fast manner. [ABSTRACT FROM AUTHOR]

Abstract: For rotating machines, the localized faults of key components generally represent as periodic transient impulses in vibration signals. The existence of background noise will corrupt transient impulses in practice, and will thus increase the difficulty to identify specific faults. This paper combines the concepts of time–frequency manifold (TFM) and image template matching, and proposes a novel TFM correlation matching method to enhance identification of the periodic faults. This method is to conduct correlation matching of a vibration signal in the time–frequency domain by using the TFM with a short duration as a template. By this method, the time–frequency distribution (TFD) of a vibration signal is firstly achieved by the Smoothed Pseudo-Wigner–Ville distribution (SPWVD) method. Then the TFM template is learned to do correlation matching with the TFD of the analyzed signal. Finally, the ridge is extracted from the correlation matching image and the ridge coefficients are analyzed for periodic fault identification. The proposed method takes advantages of the TFM in noise suppression and template matching in object enhancement, and can enhance the fault impulses of interest in a unified scale. The novel method is verified to be superior to traditional enveloping method with providing smoother and clearer fault impulse component via applications to gearbox fault detection and bearing defect identification. [Copyright &y& Elsevier]

Abstract: This paper proposes a new study to explore the wavelet packet energy (WPE) flow characteristics of vibration signals by using the manifold learning technique. This study intends to discover the nonlinear manifold information from the WPE flow map of vibration signals to characterize and discriminate different classes. A new feature, called WPE manifold feature, is achieved by three main steps: first, the wavelet packet transform (WPT) is conducted to decompose multi-class signals into a library of time–frequency subspaces; second, the WPE is calculated in each subspace to produce a feature vector for each signal; and finally, low-dimensional manifold features carrying class information are extracted from the WPE library for either training or testing samples by using the manifold learning algorithm. The new feature reveals the nonlinear WPE flow structure among various redundant time–frequency subspaces. It combines the benefits of time–frequency characteristics and nonlinear information, and hence exhibits valuable properties for vibration signal classification. The effectiveness and the merits of the proposed method are confirmed by case studies on vibration analysis-based machine fault classification. [Copyright &y& Elsevier]

Multiple moving sound sources' aliasing problem occurs in many fields. The number and relative position of these sources provide critical information for further signal processing. However, estimating the number and relative position under aliasing of multiple moving sound sources is challenging. In this study, the Fibonacci sequence was applied to design a novel two-dimensional Fibonacci array, which has lower maximum sidelobe level than other typical array structures in a certain frequency scope. Conventional beamforming was utilized as an acoustic camera for stationary sound source localization by two-dimensional spatial scanning. Unlike the acoustic camera, a spatiotemporal map (STM) method was developed to estimate the number and relative position of multiple moving sound sources through spatial scanning and temporal focusing. STM is called focused acoustic camera because of the spatiotemporal information extraction of dynamic sources. A deconvolution approach called clean STM (CSTM) was created to estimate the spatiotemporal parameters for further signal processing and to overcome the influence of array pattern on STM. CSTM was combined with two-dimensional time-varying spatial filtering rearrangement and harmonic product spectrum-based sparse filtering enhancement. Consequently, a systematic technique solution was developed to be potentially applied in wayside acoustic bearing fault diagnosis systems. Simulation and experimental cases verified the effectiveness of the proposed method and the systematic technique solution. • Multiple moving sound sources aliasing problem is proposed in this study. • A Fibonacci array with good performance is designed with Fibonacci sequence. • A deconvolution CSTM method is proposed to estimate spatio-temporal parameters. • A new concept of focused acoustic camera is proposed. • A systematic technique solution is proposed for wayside acoustic bearing fault diagnosis. [ABSTRACT FROM AUTHOR]

For rotary machines, the measured vibration signals contain multiple frequency modulation components with much machine health information. Decomposing vibration signals of rotary machines into a series of sub-signal components with physical meaning is of great significance for a deep understanding of the machine properties in solving fault diagnosis problems. However, accurately detecting and decomposing frequency modulation components is a challenging problem. This article proposed a multiple frequency modulation components detection and decomposition approach for rotary machine fault diagnosis. Specifically, the amplitude and concentration at the orders (defined as multiple of the rotating frequency) of each signal to be analyzed are calculated, and thresholds are set to distinguish noise and non-noise signal components. Then the cross correlation between the non-noise signal components and an order ruler (constructed with normalized amplitude only on the orders of characteristic components of the faulty signal) is calculated to obtain the instantaneous frequencies (IFs) of these signal components. Finally, a variational algorithm framework defined only by the characteristic components of the faulty signal is used to achieve signal decomposition. The proposed method fully uses the inherent geometric relationship of the frequency modulation signal in the angular domain and is robust to strong environmental noise. Both the simulation example and the actual application cases demonstrated the effectiveness of the proposed method, which shows the potential in solving real industrial problems. [ABSTRACT FROM AUTHOR]

In recent years, large amounts of bitumen have been discovered in the Permian Lower Wuerhe Formation (P2w) reservoir exploration of the Mahu Sag on the northwestern margin of the Junggar Basin. However, bitumen genesis and its influence on oil accumulation remain unclear, affecting the subsequent exploration and development in this area. Therefore, this study analyzed the genesis and source of bitumen and its influence on hydrocarbon accumulation through chromatography, microscopic observation, inclusion temperature tests, and reservoir physical property experiments. Studies have shown that the crude oil produced in the early period of the Fengcheng Formation (P1f) was destroyed due to tectonic movement. Crude oil undergoes biodegradation, where the lighter components are dispersed and lost. The remaining heavy components undergo high temperature conditions during long-term burial to form bitumen. Microscopic observation showed that the overlying reservoir has a higher bitumen content and the underlying reservoir has a lower bitumen content. After the bitumen was dissolved, the porosity and permeability of the overlying reservoir increased by 6.77% and 34.67 mD from the original 3.53% and 3.41 mD, respectively. The porosity and permeability of the underlying reservoir increased by 3.21% and 27.68 mD from the original 5.86% and 1.51 mD, respectively. This shows that the initial physical properties of the overlying reservoir were better than those of the underlying reservoir. Therefore, early crude oil is more likely to enter the overlying reservoir, resulting in a higher bitumen content. The difference in the homogenization temperature of inclusions and the maturity of bitumen indicates that the oil and gas accumulation in this area has undergone two stages. Early filling with oil occurred in the late Permian, but the reservoir was destroyed because of tectonic movements. The residual components formed bitumen during the long burial, which occupied most of the reservoir space and made it difficult for late crude oil to enter the pores. [ABSTRACT FROM AUTHOR]

As a high efficiency and environmental friendly energy conversion technique, proton exchange membrane fuel cell (PEMFC) system faces challenges of limited durability and performance decay during long-term operation. Prognosis estimates the remaining useful life (RUL) of the system, from which maintenance policy can be scheduled to extend its useful life. However, parameters related to either PEMFC historical state or operating mode are used in most existing PEMFC prognostic studies, while their effects on PEMFC predictions are not clarified, this brings great challenge in selecting appropriate parameters for reliable PEMFC prognosis in practical applications subjected to complex operating conditions. In this paper, the effectiveness of PEMFC historical behavior and operating mode on PEMFC future performance at both static and non-static conditions are investigated, using back propagation neural network (BPNN) and adapted neural fuzzy inference system (ANFIS), respectively. From the findings, PEMFC historical state and operating mode make varying contributions to PEMFC prognostic results at different operating scenarios. At static operating condition, PEMFC predictions are dominated by its historical state, since constant operating mode is applied in this scenario, thus reliable prediction can be made by using only parameters representing PEMFC historical state. However, at non-static operating condition, the varying operating mode makes more contribution to the PEMFC predictions, and accurate prognosis should be provided by including variables representing varying operating mode in the prognostic analysis. The results can be beneficial in selecting appropriate parameters in prognostic analysis at practical PEMFC applications, where complex operating conditions may be experienced. • Effectiveness of historical state and operating condition in prognosis are studied. • Necessity of selecting proper variables in PEMFC prognosis is investigated. • Guideline of selecting variables in PEMFC prognosis at various scenarios is proposed. [ABSTRACT FROM AUTHOR]

• Different kinds of fault-induced vibration signal modulation effects are formulated, illustrated and summarized. • An up-to-date methodological review for signal modulation feature extraction methods is provided. • Representative works of vibration signal modulation feature extraction-based fault diagnosis are reviewed and illustrated. Rotating machinery faults can induce characteristic modulation effects in a vibration signal, and their diagnosis can thus be conducted by extracting fault-induced modulation features. To be specific, such a diagnostic strategy typically involves three main aspects, i.e., fault-induced signal modulation mechanisms, modulation feature extraction methods and applications for diagnosis. To date, the research works on these three aspects all achieved great progresses. A systematic review is thus urgently needed to summarize these achievements, and guide their future directions and developments. This paper aims to fill these gaps and address the needs. Three kinds of typical modulation effects induced by transmission elements are firstly reviewed and summarized. For each kind of modulation effects, a representative phenomenological model is given to formulate and illustrate the relationship between fault-induced modulation patterns and characteristic spectral distributions. As the primary tools to extract signal modulation features, different kinds of time-frequency analysis and signal decomposition methods are then systematically reviewed, including their up-to-date developments, classifications and application scenarios. Some representative works of vibration signal modulation feature extraction-based fault diagnosis are finally introduced along with several typical examples. Based on current research achievements, two potential topics for the future developments of vibration-based diagnostic techniques are the quantitative study of fault feature evolution patterns and the development of more robust and efficient feature extraction algorithms. [ABSTRACT FROM AUTHOR]

With the global expansion of shale oil and gas exploration, analyzing the geochemical characteristics of different types of shale has seized extensive attention. Taking the Chang 7 Member and Shanxi Formation in the Ordos Basin as examples, this study investigated the geochemical and hydrocarbon expulsion characteristics of different types of shale. The results show that the Chang 7 shale is mainly type I. The total organic carbon (TOC) distribution of the Chang 7 shale in the study area was between 0.72% and 27.5%, with an average of 7.46%, and the vitrinite reflectance (Ro) ranged from 0.45% to 1.28%. It is a good source rock in immature to mature stages. The Shanxi shale is mainly type III. The TOC distribution of the Shanxi shale was between 0.6% and 58.5%, with an average of 8.92%, and the Ro distribution was between 0.42% and 2.39%, indicating good immaturity over mature source rock. The average hydrocarbon expulsion efficiencies of the Chang 7 shale calculated using the hydrocarbon generation potential method and the original hydrocarbon generation potential method were 82.49% and 94.81%, respectively. Simultaneously, a correlation analysis of hydrocarbon expulsion efficiency with organic matter abundance, maturity, and type found that when TOC was less than 5%, hydrocarbon expulsion efficiency increased with increased organic matter abundance and maturity. When the TOC exceeds 5%, the hydrocarbon expulsion efficiency is mainly related to maturity. The hydrocarbon expulsion efficiency of the sapropelic shale was much higher than that of the humic shale. This study is significant for evaluating shale gas and shale oil resources in similar areas. [ABSTRACT FROM AUTHOR]

Transient signal analysis (TSA) has been proven an effective tool for motor bearing fault diagnosis, but has yet to be applied in processing bearing fault signals with variable rotating speed. In this study, a new TSA-based angular resampling (TSAAR) method is proposed for fault diagnosis under speed fluctuation condition via sound signal analysis. By applying the TSAAR method, the frequency smearing phenomenon is eliminated and the fault characteristic frequency is exposed in the envelope spectrum for bearing fault recognition. The TSAAR method can accurately estimate the phase information of the fault-induced impulses using neither complicated time-frequency analysis techniques nor external speed sensors, and hence it provides a simple, flexible, and data-driven approach that realizes variable-speed motor bearing fault diagnosis. The effectiveness and efficiency of the proposed TSAAR method are verified through a series of simulated and experimental case studies. [ABSTRACT FROM AUTHOR]

• Gearbox fault diagnosis using BFNCC is proposed for nonstationary condition. • The method can remove the effect of the structure transfer paths. • The proposed method is robust to the location of measuring points. • Method shows potential for a machinery with complicated structure transfer paths. The main challenges in gearbox condition monitoring and fault diagnosis are the heavy noises caused by harsh operating environments and the complicated nonstationary condition during the runtime. It is extremely difficult and even impossible to obtain sensitive fault characteristics through direct measurements. Thus, it is of great significance of fault characteristic inversion based on the structure vibration transfer paths for complicated mechanical systems fault diagnosis. This paper proposes a gearbox fault diagnosis method under nonstationary condition through using nonlinear chirp components extracted from bearing force (BFNCC). In the proposed method, the bearing dynamic forces are first identified based on the vibration transfer function matrix, and the intrinsic signal components are then decomposed through a nonlinear chirp mode decomposition method. The proposed method improved the quality of vibration signals by removing the effect of the structure vibration transfer paths. In comparison with order tracking, the BFNCC is capable of clearly reflecting the magnitude increase phenomenon under gear fault condition. Numerical simulation and experimental studies show that the proposed method is effective for gear fault diagnosis under nonstationary condition. The proposed method is demonstrated to be superior to the existing approaches and robust to the location of measuring points, and thus shows potential in machinery fault diagnosis under the interference of complicated structure transfer paths. [ABSTRACT FROM AUTHOR]

For rotating machines, the defective faults of bearings generally are represented as periodic transient impulses in acquired signals. The extraction of transient features from signals has been a key issue for fault diagnosis. However, the background noise reduces identification performance of periodic faults in practice. This paper proposes a time-varying singular value decomposition (TSVD) method to enhance the identification of periodic faults. The proposed method is inspired by the sliding window method. By applying singular value decomposition (SVD) to the signal under a sliding window, we can obtain a time-varying singular value matrix (TSVM). Each column in the TSVM is occupied by the singular values of the corresponding sliding window, and each row represents the intrinsic structure of the raw signal, namely time-singular-value-sequence (TSVS). Theoretical and experimental analyses show that the frequency of TSVS is exactly twice that of the corresponding intrinsic structure. Moreover, the signal-to-noise ratio (SNR) of TSVS is improved significantly in comparison with the raw signal. The proposed method takes advantages of the TSVS in noise suppression and feature extraction to enhance fault frequency for diagnosis. The effectiveness of the TSVD is verified by means of simulation studies and applications to diagnosis of bearing faults. Results indicate that the proposed method is superior to traditional methods for bearing fault diagnosis. [ABSTRACT FROM AUTHOR]

Wayside acoustic condition monitoring and fault diagnosis for train bearings depend on acquired acoustic signals, which consist of mixed signals from different train bearings with obvious Doppler distortion as well as background noises. This study proposes a novel scheme to overcome the difficulties, especially the multi-source problem in wayside acoustic diagnosis system. In the method, a time–frequency data fusion (TFDF) strategy is applied to weaken the Heisenberg׳s uncertainty limit for a signal׳s time–frequency distribution (TFD) of high resolution. Due to the Doppler Effect, the signals from different bearings have different time centers even with the same frequency. A Doppler feature matching search (DFMS) algorithm is then put forward to locate the time centers of different bearings in the TFD spectrogram. With the determined time centers, time–frequency filters (TFF) are designed with thresholds to separate the acoustic signals in the time–frequency domain. Then the inverse STFT (ISTFT) is taken and the signals are recovered and filtered aiming at each sound source. Subsequently, a dynamical resampling method is utilized to remove the Doppler Effect. Finally, accurate diagnosis for train bearing faults can be achieved by applying conventional spectrum analysis techniques to the resampled data. The performance of the proposed method is verified by both simulated and experimental cases. It shows that it is effective to detect and diagnose multiple defective bearings even though they produce multi-source acoustic signals. [ABSTRACT FROM AUTHOR]

The diagnosis of train bearing defects plays a significant role in maintaining the safety of railway transport. However, the phenomenon of Doppler Effect in the acoustic signal recorded by the wayside Acoustic Defective Bearing Detector (ADBD) system leads to the difficulty for fault diagnosis of train bearings with a high moving speed. This paper proposes a double-searching solution based on improved Dopplerlet transform and Doppler transient matching to overcome the difficulty in wayside acoustic bearing diagnosis. In the solution, the first searching procedure is to extract necessary parameters of Doppler Effect under the situation with very low signal-to-noise ratio (SNR) based on an improved Dopplerlet transform. Using the obtained parameters, the Doppler Effect can be embedded into the constructed periodic Laplace wavelet transient models. Subsequently, the second searching procedure is conducted to search fault impact period of the defective bearing through an operation, called Doppler transient matching, which is to calculate the correlation coefficient between the Doppler transient model and the filtered raw signal with the Doppler Effect. The proposed double-searching algorithm can adapt to the real Doppler Effect situation and extract the exact fault impact period from the Doppler distorted signal, and thus shows powerful capability to analyze wayside acoustic signals from train bearings. The proposed wayside acoustic diagnostic scheme is verified by means of a simulated Doppler distorted signal with a very low SNR (−20 dB) and the experiments conducted on train bearings. The results indicate that the proposed algorithm is effective and has obvious advantages for ADBD system. [ABSTRACT FROM AUTHOR]

• A phononic crystal resonator (PnCR) is proposed for directional enhancement of gear weak fault detection. • The PnCR offers a tunable acoustic gain resonance band to enhance the target weak acoustic signal. • The PnCR demonstrates high acoustic angle resolution with directional enhanced perception enabled. • Experimental results and comparisons highlight its excellent performance for weak fault detection under high energy spatial noise interference. Condition monitoring and diagnosis of the gears is essential to ensure the reliability of the whole mechanical equipment. Sensing technology based on acoustic signals plays a vital role in gear fault diagnosis with its advantages of non-contact and flexible installation. However, the signals detected by acoustic sensing are interfered by spatial noise from the operating environment and other equipment. This interference hampers the detection of weak fault features in gears, thereby limiting the applicability of acoustic sensing technology for fault diagnosis. In this study, combining the great advantages of acoustic wave manipulation and modulation by phononic crystals (PnC) and acoustic metamaterials, a phononic crystal resonator (PnCR) with the ability of directional enhancement of acoustic signals is proposed for fault detection in gears. In contrast to traditional acoustic diagnostic techniques, this method can enhance gear fault features in the desired direction at the front end of the sensing layer while simultaneously suppressing noise from other directions, eliminating the need for large-sized acoustic array systems and complex post-processing algorithms. The effectiveness of the designed PnCR directional acoustic enhancement is explored through numerical simulation and experimental validation. In addition, compared to the conventional resonance demodulation method based on spectral kurtosis, the device exhibits more excellent gear fault orientation enhancement performance at low signal-to-noise ratios (SNR). This work provides new ideas for the development of acoustic sensing with both high sensitivity and high directivity, which has a broad potential for application in the field of mechanical equipment fault diagnosis. [Display omitted] [ABSTRACT FROM AUTHOR]

Considering the fact that various features can be used in proton exchange membrane fuel cell (PEMFC) fault diagnosis, while the lack of feature evaluation method brings great difficulty in selecting appropriate features at practical PEMFC applications, a generalized feature evaluation and selection method is urgently required in PEMFC fault diagnosis. This article proposes a novel feature evaluation method, where feature discrimination capacity and robustness are evaluated. With the proposed method, features providing accurate and consistent diagnostic performance can be determined. In this study, features widely used in existing PEMFC fault diagnosis are utilized, which are extracted from either PEMFC voltage or multisensor signals, and their effectiveness in identifying faults at different PEMFC systems is investigated. Results demonstrate that with the proposed evaluation method, available features from various PEMFC test data can be ranked based on their diagnostic results. From the findings, appropriate features for PEMFC fault diagnosis can be determined. Moreover, early stage PEMFC faults can also be distinguished with high ranking features. This will be beneficial in practical PEMFC systems, where mitigation strategies can be taken to remove the effect due to early stage faults. [ABSTRACT FROM AUTHOR]

Highlights: [•] An effective de-noising method for ADBD system is proposed. [•] The Morse acoustic theory and variable digital filters are applied to this method. [•] This method could extract useful information in a heavy noise background. [•] Method could deal with multi-source acoustic signals with Doppler Effect. [•] Proposed method has been verified by simulation and experimental cases. [ABSTRACT FROM AUTHOR]

Highlights: [•] A new method is developed to remove the Doppler Effect for ADBD system. [•] IFE and time domain re-sampling are employed in the method. [•] There is no pre-measurement carried out in the methods. [•] Technique has superior performance than the existing method. [•] Proposed new method has been verified by simulation and experimental cases. [ABSTRACT FROM AUTHOR]

[Display omitted] • A line-defect PnC-based enhanced diagnosis method for rotating machinery weak fault feature is proposed. • A line-defect PnC structure with acoustic gain resonance peak is designed to achieve signal enhancement in the target frequency band. • The desired gain resonance band can be obtained by tunning the structural parameters through equal scaling. • The experimental results demonstrate that the proposed method exhibits superior performance compared to conventional fault diagnosis methods in extracting fault features of rotating machinery under low SNR conditions. The fault features of rotating machinery at the early degradation stage are always weak as a result of interference from strong noise and irrelevant harmonics. Although traditional acoustic diagnosis techniques have attracted much attention with the merits of rich information and non-contact, extracting meaningful features related to faults in extremely low signal-to-noise ratios (SNRs) has always been a challenging problem. Considering the extraordinary physical properties of phononic crystals (PnCs) and acoustic metamaterials, this study proposes a structural enhancement method for rotating machinery fault diagnosis via line-defect PnC sensing. In contrast to conventional acoustic filtering and enhancement methods, this method can directly filter out noise and enhance fault features in the pre-processing stage of acoustic perception without the need for complex post-processing algorithms. Consequently, the original information of the fault features is preserved intact, thus further increasing the detection limit of current acoustic sensing. Specially, the designed line-defect PnC is parametrically tunable. Combined with the prior knowledge of rotating machinery fault features, such as gears and bearings, it is possible to design structures suitable for enhancing their fault features, which has great potential for practical engineering applications. The enhancement mechanism of line-defect PnC is theoretically described, and numerical simulations are also conducted to verify its ability to detect weak faults in rotating machinery. The experimental results show that by comparison with the variational modal decomposition (VMD)-based method, the proposed method exhibits superior fault feature enhancement performance under low SNR conditions. By systematically combining fault detection methods and acoustic metamaterial sensing, it can be foreseen that the proposed method shows great potential in mechanical equipment fault diagnosis. [ABSTRACT FROM AUTHOR]

Abstract: BGSO polycrystalline powder was synthesized as starting material by sintering of 4N Ge2O3, SiO2 and Bi2O3 using solid state reaction method. Using 〈010〉-oriented Bi4Si3O12 crystals as seeds, the mixed crystals of (Bi4(Ge x Si1−x )3O12, x=0.05, 0.1, 0.15, BGSO) have been grown by the vertical Bridgman method. Transparent BGSO crystals with a size up to 10×15×30mm3 have been obtained. The lattice parameters increase monotonically with increasing Ge3+ content. The transmittance spectra of BGSO single crystals were achieved in the UV and visible light regions and the absorption edge of BGSO crystal is about 285nm. [Copyright &y& Elsevier]

Abstract: This paper proposes a multiscale slope feature extraction method using wavelet-based multiresolution analysis for rotating machinery fault diagnosis. The new method mainly includes three following steps: the discrete wavelet transform (DWT) is first performed on vibration signals gathered by accelerometer from rotating machinery to achieve a series of detailed signals at different scales; the variances of multiscale detailed signals are then calculated; finally, the wavelet-based multiscale slope features are estimated from the slope of logarithmic variances. The presented features reveal an inherent structure within the power spectra of vibration signals. The effectiveness of the proposed feature was verified by two experiments on bearing defect identification and gear wear diagnosis. Experimental results show that the wavelet-based multiscale slope features have the merits of high accuracy and stability in classifying different conditions of both bearings and gearbox, and thus are valuable for machinery fault diagnosis. [Copyright &y& Elsevier]

• A gearbox fault diagnosis method based on bearing force identification is proposed. • Identified bearing dynamic forces outperform vibration signals for fault diagnosis. • The proposed method avoids the dependence on the layout of measuring points. • The method can theoretically be combined with the feature extraction algorithms. In the area of gearbox fault diagnosis, to obtain sufficient and effective vibration information, traditional studies are usually conducted from the perspective of optimizing the layout of measuring points. This paper focuses on a gearbox fault diagnosis method based on bearing dynamic force identification. Gearbox bearing dynamic forces under the operating condition are modeled as excitation sources, and vibration signals measured at any position on the housing are modeled as receivers. Then mutual coupling effect of different structure transfer paths of housing on the excitation signals can be quantitatively modeled. The bearing dynamic forces are finally constructed with the transfer function matrix and vibration signals by solving the inverse problem. The identified bearing dynamic forces is capable of clearly reflecting the gear fault characteristics, which outperforms the original vibration signals measured on the gearbox housing due to poor signal quality and the effect of structure transfer paths. Numerical simulation and experimental studies show the effectiveness of the estimated bearing dynamic force signals for gear fault diagnosis. The proposed method is demonstrated to be insensitive to the location of measuring points, and shows a good potential in complicated mechanical system fault diagnosis. [ABSTRACT FROM AUTHOR]

• The proposed unsupervised method merges anomaly detection and fault tracing. • Signal decomposition-based energy features are extracted to make the diagnosis more robust. • The model training process only requires normal data. • The diagnostic process can be performed without labeled fault samples. The absence of detailed fault data presents a significant challenge in diagnosing faults of rotating machinery. This paper focuses on addressing abnormal detection and fault localization challenges in drivetrain components under the assumption that only normal data is available. Specifically, this paper proposes an unsupervised hypersphere description (UHD) framework by seamlessly integrating abnormal detection and localization. By constructing a one-class description hypersphere, this UHD method achieves fault detection without special fault samples. Abnormal samples can be detected based on their distance from the center of hypersphere, enabling accurate localization of the underlying abnormality. Validation of experimental data demonstrates that the UHD approach effectively detects anomalies and accurately localizes faults within specific locations. Remarkably, the UHD approach surpasses several mainstream unsupervised methods in terms of fault tracing accuracy. This shows the potential of the UHD approach in abnormal detection and localization in a drivetrain, particularly when only normal data is available. [ABSTRACT FROM AUTHOR]

• Single-sensor on-shaft vibration source identification problem is studied. • A dynamic model with resonance frequency random distribution is proposed. • A smart metasurface shaft model is designed with random local resonators. • The frequency response transfer functions are designed to be highly uncorrelated. • Combining compressive sensing algorithm realizes the single-sensor identification. In vibration monitoring of rotating machinery, traditional methods necessarily rely on multiple sensors to locate the vibration sources on the shaft. Reducing the number of sensors is an engineering challenge for on-shaft vibration source identification. This paper proposed a smart metasurface shaft (SMST) to achieve single-sensor identification of vibration sources. For the proposed SMST, a thin metasurface consisting of rubber metal rings is designed to cover the shaft and is capable of smartly on-shaft sensing of vibration sources. For the metasurface, the rubber metal rings with random metal masses are used to construct random local resonator units, which leads to the random modulation of location-different vibration responses. Theoretical derivation and simulations demonstrate the mechanism of random modulation of on-shaft vibrations. By combining the SMST with the compressive sensing algorithm, the vibration source locations can be reconstructed from the measurement of a single sensor. For the proposed SMST, experiments demonstrate that the single-sensor vibration source identification can be achieved with a high correct recognition ratio. The proposed SMST provides a new idea on single-sensor on-shaft vibration sensing, which shows potential applications in rotor dynamics, such as rotating machinery condition monitoring, vibration identification, and fault diagnosis. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach for fault diagnosis of polymer electrolyte membrane fuel cell (PEMFC) with two-dimension (2D) image data, and investigates its effectiveness of identifying faults at different PEMFCs in terms of discrimination capacity and robustness. In the analysis, one-dimension (1D) voltage data from single cell is converted to corresponding 2D image using signal-to-image conversion technique. Various features are then extracted from the 2D image data, and optimal features are determined using Fisher discriminant analysis (FDA). Test data from PEMFC at different faulty states, including flooding and dehydration states, is collected for the analysis, and the effectiveness of optimal features in discriminating various states is investigated using K-means clustering method. Moreover, diagnostic performance with 2D image data is compared to those using 1D voltage signals, such that its effectiveness can be better highlighted. Furthermore, with test data collected from two different single cells, robustness of proposed method can be illustrated. • A novel method is proposed for PEMFC fault diagnosis with 2D image data. • Its effectiveness in identifying flooding and dehydration is studied. • Its performance is compared to those using features from 1D signal data. • Its robustness is investigated using test data from different PEMFC systems. [ABSTRACT FROM AUTHOR]

• A stiffness-mass-coding model realizes the tunable multi-resonance coupling. • The stiffness-mass-coding metamaterial is proposed with tunable performance. • The coupling of tunable out-of-plane and in-plane vibration bandgaps is realized. • The SMCM realizes the broadband tunability for low-frequency vibration isolation. Low-frequency vibration isolation is essential for safety, accuracy and stability in various engineering areas. Traditional methods are generally limited to the adjustment of vibration bandwidth and frequency, so broadband tunability is challenging in practical low-frequency isolation applications. This paper proposed a stiffness-mass-coding metamaterial (SMCM) to achieve broadband tunability for low-frequency vibration isolation. For the proposed SMCM, two basic stiffness-mass-coding supercells are connected vertically, leading to the coupling of out-of-plane and in-plane vibration bandgaps. The stiffness-mass-coding supercell is composed of four tunable local resonators with tunable effective stiffness and effective mass, which is designed with tunable magnetic force and center mass to realize the tunable multiple bandgaps. Theoretical derivation explains the tunable multi-resonance coupling mechanism of the proposed SMCM model. Experiments demonstrate that the lower bound and the bandwidth of the SMCM bandgap are both flexibly adjustable with the stiffness-mass-coding model. Furthermore, a tunable ultra-broad bandgap can be generated with the SMCM based on the tunable multi-resonance coupling mechanism, leading to the broadband tunability for low-frequency vibration isolation. The proposed SMCM design realizes the tunable multi-resonance coupling, provides a new idea on the low-frequency broadband tunability and shows potential in many related engineering applications. [ABSTRACT FROM AUTHOR]

Recently, the Devonian Guanwushan Formation in the northwestern Sichuan Basin gained industrial attention when solid bitumen was discovered in reservoirs and outcrops. This discovery initiated the exploration and development of the Devonian natural gas. Devonian in the northwestern Sichuan Basin is a new stratum for the exploration and development of Paleozoic marine natural gas in Sichuan Basin. However, the unclear recognition on the genesis, the sources and the hydrocarbon evolution history of solid bitumen and natural gas seriously restricted further exploration and development. Based on the solid bitumen reflectance, carbon isotopes, saturated hydrocarbon chromatography, and solvent extract bitumen biomarkers combined with the component and carbon isotopes of natural gas, comprehensive analyses have been performed on the origin of the solid bitumen and the accumulation and evolution of the natural gases. The results showed that the outcrop solid bitumen is derived from biodegraded oil from the Lower Cambrian source rocks, and is characterised by low bitumen reflectance, low saturated/aromatic hydrocarbon ratios, seriously damaged normal-alkanes, and severe migration of the gas chromatographic baseline. The reservoir bitumen formed through thermal cracking of oil from Lower Cambrian and Lower Permian source rocks, and is characterised by high bitumen reflectance, high saturated/aromatic hydrocarbons, and complete normal-alkanes. The differences of tectonic evolution take control of the two different-genesis bitumen. The Longmen Mountain strata in the northwestern Sichuan Basin were uplifted sharply after the Jurassic and the early generated crude oil was biodegraded during uplifting and formed the outcrop bitumen. However, strata in Well Shuangtan-3 (ST3) continued to subside and the paleo reservoir thermal-cracked during deep burial into dry gas and heavy components gathered to form reservoir bitumen. Revealing the Devonian hydrocarbon evolution in Sichuan Basin is of highly significance and is meaningful to guide further researches on the gas-source correlation of natural from paleo-strata. It is of great significance to reveal the Devonian hydrocarbon evolution in Sichuan Basin and to guide further researches on the gas-source correlation of natural gas from paleo-strata. • Outcrop solid bitumen is derived from biodegraded oil from the Lower Cambrian source rocks. • Reservoir solid bitumen formed through thermal cracking of oil from Lower Cambrian and Lower Permian source rocks. • The differences of tectonic evolutions control of the genesis of the two different solid bitumen. • The significance of the results for exploration of Devonian gas is highlighted. [ABSTRACT FROM AUTHOR]

• An adaptive parametric Doppler correction scheme is mechanistically built for wayside acoustic correction. • The principle of the proposed method aims to mathematically build wayside array acoustic physical model with multiple perception operators. • Short-time reconstruction with formulaic compensation mathematically recover the real-time signal. • STR-based PDC simultaneously recover amplitude, frequency distribution and phase in high fidelity. • Results show validity and potential in propagation and distortion correction for the real-time diagnosis of TADS. The trackside acoustic detection system (TADS) plays a vital role in vehicle bearings condition monitoring. However, the inevitable Doppler distortion embedded in collected data will bring considerable difficulties to defect inspection and diagnosis in the system. The distortion elimination approaches mainly achieve spectrum recovery based on time-domain interpolation resampling (TIR) in a data-driven way. These will be further limited by the requirement to signal to noise ratio (SNR) or calculation speed. Considering the principle of wayside acoustic physical model and the benefits of array acoustic in practice, this study proposes an adaptive parametric Doppler correction (PDC) scheme based on short-time reconstruction (STR), including perception modeling, inverse perception optimization and Doppler correction. Firstly, mathematical modeling with multiple perception operators, including acoustic amplitude modulation operator, frequency modulation operator and array time delay operator, a wayside acoustic physical model based on array sensing is established. Secondly, through the corresponding inverse multiple perception operators, a transition signal with frequency-domain energy concentrated is obtained for the optimal moving parameters searching. Thirdly, within short-time sequence segments of the sound source, the real-time position recovery is realized via the proposed STR with formulaic propagation delay compensation. Theoretically, it can be seen that the proposed PDC scheme can simultaneously recover the amplitude, frequency distribution and phase of the Doppler signal, which can be applicable to all wayside acoustic diagnosis scenarios. With the correction analysis of simulation and experimental array defect signals, it can be confirmed that the proposed STR-based PDC scheme is effective for Doppler signal recovery in high fidelity, and has great potential to the real-time diagnosis of TADS. [ABSTRACT FROM AUTHOR]

Abstract Rolling bearings under time-varying speed conditions will induce non-stationary vibration signals. Fault diagnosis of varying-speed bearings is a challenging task. Towards this goal, the most widely used method is to extract the fault characteristic frequency (FCF) of the bearing by detecting ridge curves from a time-frequency representation (TFR) of the vibration signal. However, the resolution of the TFR is restricted by the Heisenberg uncertainly principal, resulting in the limited accuracy of the FCF estimation. In this paper, a high-accuracy FCF extraction method for varying-speed bearing fault diagnosis is proposed based on an iterative envelope-tracking filter (IETF). Besides estimating the signal envelope, the IETF incorporates an iterative frequency-refinement procedure to accurately estimate the FCF of the faulty bearing. In addition, a high-resolution TFR based on the estimated signal envelopes and frequencies is constructed to clearly reveal bearing fault features. Both our simulation and experimental studies have shown that the IETF can generate much more accurate estimation results than using the conventional TFR ridge detection method alone. Highlights • The IETF is introduced for fault diagnosis of varying-speed bearings. • The IETF can extract FCFs much more accurately than TF ridge detection. • The IETF can generate a high-resolution TFR for bearing fault signals. • The method is validated by both simulation and experimental studies. [ABSTRACT FROM AUTHOR]

Highlights • A novel method named M-VNCMD is introduced to deal with the intra-wave FM signal. • The intra-wave FM phenomenon in rubbing response is revealed and investigated. • The relation between modulation pattern and spectrum of response is established. • The superiority of diagnosing rubbing fault based on modulation law is verified. Abstract The rub-impact fault occurring in the rotating machinery can cause the periodically frequency modulation (FM) in its vibration response. Such kind of modulation mode has been named as intra-wave FM effect, which carries the intrinsic fault information of rubbing-related dynamic system. Assisted by a fast-modulated instantaneous frequency (IF) extraction method named M-VNCMD, the intra-wave FM phenomena arising in several simulated and real rubbing responses are revealed and investigated in detail in this paper. To be specific, the intra-wave FM response is firstly modeled as the sinusoidal modulation form according to the extracted fault features, and then expanded as a series of harmonics to establish the relationship between its modulation pattern and Fourier spectrum. The connection among the fault diagnosis criteria based on modulation patterns and Fourier spectrum is finally uncovered to demonstrate the superiority of diagnosing the rubbing fault depending on modulation features. [ABSTRACT FROM AUTHOR]

• A MWI approach is proposed for ill-conditioned inverse problem in dynamics. • The unbiasedness and convergence of the proposed MWI method are proved. • MWI is superior to TSVD and Tikhonov regularization in application cases. • MWI has high accuracy and anti-noise property. Many problems in dynamics can be formulated as inverse problems that require the determination of the unknown input from the known output. Limited by the measurement noise, the computational cost, the error in numerical computation, etc., solving ill-conditioned inverse problems in dynamics is a big challenge. In this paper, we propose Maximumly Weighted Iteration (MWI) approach to solve ill-conditioned inverse problems in dynamics generally. The ill-condition of the system coefficient matrix is controlled by iterative weighted decomposition and a weighted term, also avoiding matrix inversion of solving and reconstruction process. As an iterative method, MWI has been theoretically proven to have consistency, absolute convergence, and conditional unbiasedness. The numerical results demonstrate that MWI is superior to Truncated Singular Value Decomposition and Tikhonov regularization in terms of accuracy and anti-noise property. Two application cases are used to demonstrate the potential of MWI in engineering practice. The first is elastic vibrations identification combined with randomized resonant metamaterials, and the second is fault diagnosis of mechanical transmission systems. [Display omitted] [ABSTRACT FROM AUTHOR]

The gas foil bearing (GFB) is an important component of high-speed, oil-free micro turbomachinery. However, the instability of GFBs under certain conditions hinders their widespread application. The traditional solution is to decrease the continuity of the gas film and increases the structural damping of GFBs through 'passive' structural improvements. Thus, the performances of improved bearings cannot be actively adjusted. This paper proposes an active bump-metal mesh foil bearing (AB-MFB) in which three metal mesh blocks (MMBs) are inserted into the active bump-type foil bearing (ABFB) to control radial preloads through piezoelectric actuators (PZTs) and increase the structural damping. A model coupling of the active-metal mesh substructures, bump foil, and top foil is presented to analyse the mechanical properties of the compliant structure of AB-MFB. Dynamic excitation tests are conducted to validate the model. This study then predicts the static and dynamic performance of the AB-MFB by combining the model and compressible gas Reynolds equation. Finally, the effects of different parameters on the dynamic performance of the AB-MFB are discussed. [ABSTRACT FROM AUTHOR]

[Display omitted] • Lacustrine and deltaic source rocks of the Chang 7 member can be distinguished by logging curves, biomarkers and trace elements. • ∑C 21 -/∑C 22 +, (C 19 + C 20)/C 21 and C 30 */C 30 H can be used to study the fine oil-source correlation. • The tight oils of the Chang 6 member in the Dingbian-Wuqi are dominated by vertical migration, without apparent lateral migration. During the deposition of the Chang 7 Member of the Yanchang Formation, pronounced lake invasion occurred, resulting in the formation of the largest lake in the Ordos Basin. This change has caused ambiguity in the source rock characteristics among the different sedimentary environments represented, thereby restricting the exploration of oil–source correlations and hydrocarbon migration pathways corresponding to oil and gas in this formation. In this study, we comprehensively assessed the characteristics of source rocks from different sedimentary environments and selected effective biomarker parameters to allow for a detailed exploration of oil formation and hydrocarbon migration. The source rock biomarkers differed notably among the different sedimentary environment, with source rocks from semi-deep to deep lakes having high ratios of pristane to phytane and ∑C 21 -/∑C 22 + n-alkanes, relatively high C 27 regular sterane contents, and low (C 19 + C 20)/C 21 and C 30 -rearranged hopane contents. Source rocks from these facies developed in reductive environments and had high V/Cr, Ni/Co, V/(V + Ni), and U/Th values, while those from the delta facies developed in a semi-oxidative to oxidative environment and thus showed the opposite characteristics. The Chang 6 tight oil in the Dingbian-Wuqi area primarily migrated vertically near its source; the western block originated from semi-deep to deep lake source rocks, while the eastern block formed from delta front source rocks. These findings should guide future research in this reservoir, especially with respect to high-resolution oil–source correlations across different sedimentary environments. [ABSTRACT FROM AUTHOR]

• An acoustic physical model-driven method called parametric Doppler correction analysis is proposed. • The acoustic forward propagation model and reverse reconstruction model are simultaneously built. • Pseudo Doppler correction is presented to adaptively remove the signal distortion. • PDCA can adaptively capture the features with a combination of physical model and data-driven optimization. • Results show validity and potential for practical application in wayside acoustic diagnosis. Wayside acoustic system plays a crucial role in monitoring and diagnosing the status of train wheel bearings. However, due to the signal distortion caused by Doppler effect, the diagnosis accuracy will be seriously disturbed. In this manner, this paper proposes a model-driven Doppler distortion self-tuning method in theory, named as parametric Doppler correction analysis (PDCA). Different from traditional methods, such as instantaneous frequency tracking and Doppler distortion sparse representation, the proposed method aims to abstractly construct a physical model of acoustic signal distortion propagation based on Morse acoustic theory, where the acoustic forward propagation model and reverse reconstruction model are simultaneously built. The scheme consists of four basic steps. Firstly, the physical model with amplitude modulation operator and frequency shift operator is described for the distortion process. Secondly, the pseudo transition signal with the characteristics of energy accumulation and no distortion is obtained via a construction of frequency rearrangement operator and amplitude demodulation operator. Then, pseudo Doppler correction (PDC) is presented to solve the frequency distortion of the received signal, where a high energy accumulation for frequency distribution is designed as optimization function. Especially, quasi Newton algorithm L-BFGS is used to realize the adaptive learning for distortion parameters. Finally, through time-domain interpolation resampling (TIR) technique, the corrected signal can be analytically reconstructed with the optimal parameters. Applying the PDCA scheme to simulated signals and experimental signals, its validity is verified. The comparison with the instantaneous frequency ridge extraction approach further indicates the proposed model-driven method can reach a more accurate correction result in a faster and adaptive process. [ABSTRACT FROM AUTHOR]

Abstract: Novel relaxor ferroelectric single crystal (1−x)Pb(Zn1/3Nb2/3)O3–xPbTiO3 (PZNT) was grown by vertical Bridgman method, the mechanical properties of the (111) face were systematically researched using microindentation technique. It was found that the Vickers’ microhardness Hv was as a function of the applied load. In the range of 0.1–3kg, Hv was decreased from 4.03 to 3.26GPa. The dependence of Hv on applied load could be explained on the basis of Kick''s law, and the Meyer index n was determined to be 1.88. In-plane crack anisotropy behavior was observed on the wafer, the crack length in the direction parallel to domain wall was longer than that perpendicular to it, therefore, the (111) PZNT crystal displayed fracture toughness Kc anisotropy accordingly. The crack types (radial-median and Palmqvist systems) parallel and perpendicular to the domain wall in the 0.1–3kg load range were classified. [Copyright &y& Elsevier]

• Finding the weaknesses of permutation entropy in bearing health monitoring. • A new dynamic nonlinear bearing health feature is proposed. • The proposed feature can overcome the limitations of permutation entropy. Aiming at overcoming the limitations of permutation entropy (PE) in dynamic bearing health monitoring (Healthy-initial fault inception-severe fault stage) , in this paper, PE calculation based on tunable Q factor wavelet transform (TQWT) is implemented to quantify bearing health. Firstly, weaknesses of PE in depicting bearing health are studied through numerically simulated signals. Secondly, based on the biasness of PE towards bearing fundamental signal component (FSC), oscillation based method TQWT is used to separate the FSC from the bearing signal to calculate its PE. Since, the proposed PE calculation method includes an oscillation based procedure, the resultant feature value is termed as oscillation based PE (OBPE). Selection of the parameters needed for OBPE calculation are discussed in details. Two public bearing run to failure experiments have confirmed that OBPE provides a much better result than direct application of PE in dynamic bearing health monitoring. [ABSTRACT FROM AUTHOR]