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Bio-mechanoreceptors capable of micro-motion sensing have inspired mechanics-guided designs of micro-motion sensors in various fields. However, it remains a major challenge for mechanics-guided designs to simultaneously achieve high sensitivity and broadband sensing due to the nature of resonance effect. By mimicking rat vibrissae, here we report a metamaterial mechanoreceptor (MMR) comprised of piezoelectric resonators with distributed zero effective masses featuring a broad range of local resonances, leading to near-infinite sensitivity for micro-motion sensing within a broad bandwidth. We developed a mechanical frequency-division multiplexing mechanism for MMR, in which the measured micro-motion signal is mechanically modulated in non-overlapping frequency bands and reconstructed by a computational multi-channel demodulation approach. The maximum sensitivity of MMR is improved by two orders of magnitude compared to conventional mechanics-guided mechanoreceptors, and its bandwidth with high sensitivity is extendable towards both low-frequency and high-frequency ranges in 0-12 kHz through tuning the local resonance of each individual sensing cell. The MMR is a promising candidate for highly sensitive and broadband micro-motion sensing that was previously inaccessible for mechanics-guided mechanoreceptors, opening pathways towards spatio-temporal sensing, remote-vibration monitoring and smart-driving assistance., (© 2023. Springer Nature Limited.)

The method of dynamic mass isolation is utilized in a self-moving precision positioning stage actuated by a piezostack to increase its moving speed. Two prototypes, namely, the referenced stage and the modified stage, have been fabricated. The only difference between the two stages is the flexure hinge manufactured in the modified stage to achieve an efficient dynamic mass isolation method. The step response has been investigated. The modified stage with dynamic mass isolation presents the average displacement of 6.6 µm with the applied step voltage being 55 V. By contrast, the referenced stage without dynamic mass isolation presents the average displacement of 1.6 µm. As a type of quasi-static piezoactuator/motors, the modified stage moves approximately four times faster than the referenced stage under the same driving frequency. By utilizing the dynamic mass isolation method, the modified stage still features the advantages of the referenced stage, such as cost-effective controllers, heavy-load capability, and motion of nanoscale. The concept and technique presented in this study can be applied to precision positioning stages for improved speed performance.

Vibrations carry a wealth of useful physical information in various fields. Identifying the multi-source vibration information generally requires a large number of sensors and complex hardware. Compressive sensing has been shown to be able to bypass the traditional sensing requirements by encoding spatial physical fields, but how to encode vibration information remains unexplored. Here we propose a randomized resonant metamaterial with randomly coupled local resonators for single-sensor compressed identification of elastic vibrations. The disordered effective masses of local resonators lead to highly uncorrelated vibration transmissions, and the spatial vibration information can thus be physically encoded. We demonstrate that the spatial vibration information can be reconstructed via a compressive sensing framework, and this metamaterial can be reconfigured while maintaining desirable performance. This randomized resonant metamaterial presents a new perspective for single-sensor vibration sensing via vibration transmission encoding, and potentially offers an approach to simpler sensing devices for many other physical information.

Stochastic resonance (SR) has been proved to be an effective approach for weak sensor signal detection. This study presents a new weak signal detection method based on a SR in an underdamped system, which consists of a pinning potential model. The model was firstly discovered from magnetic domain wall (DW) in ferromagnetic strips. We analyze the principle of the proposed underdamped pinning SR (UPSR) system, the detailed numerical simulation and system performance. We also propose the strategy of selecting the proper damping factor and other system parameters to match a weak signal, input noise and to generate the highest output signal-to-noise ratio (SNR). Finally, we have verified its effectiveness with both simulated and experimental input signals. Results indicate that the UPSR performs better in weak signal detection than the conventional SR (CSR) with merits of higher output SNR, better anti-noise and frequency response capability. Besides, the system can be designed accurately and efficiently owing to the sensibility of parameters and potential diversity. The features also weaken the limitation of small parameters on SR system.

The railway occupies a fairly important position in transportation due to its high speed and strong transportation capability. As a consequence, it is a key issue to guarantee continuous running and transportation safety of trains. Meanwhile, time consumption of the diagnosis procedure is of extreme importance for the detecting system. However, most of the current adopted techniques in the wayside acoustic defective bearing detector system (ADBD) are offline strategies, which means that the signal is analyzed after the sampling process. This would result in unavoidable time latency. Besides, the acquired acoustic signal would be corrupted by the Doppler effect because of high relative speed between the train and the data acquisition system (DAS). Thus, it is difficult to effectively diagnose the bearing defects immediately. In this paper, a new strategy called online Doppler effect elimination (ODEE) is proposed to remove the Doppler distortion online by the introduced unequal interval sampling scheme. The steps of proposed strategy are as follows: The essential parameters are acquired in advance. Then, the introduced unequal time interval sampling strategy is used to restore the Doppler distortion signal, and the amplitude of the signal is demodulated as well. Thus, the restored Doppler-free signal is obtained online. The proposed ODEE method has been employed in simulation analysis. Ultimately, the ODEE method is implemented in the embedded system for fault diagnosis of the train bearing. The results are in good accordance with the bearing defects, which verifies the good performance of the proposed strategy.

We design an instrument with a novel embedded adaptive stochastic resonance (SR) algorithm that consists of a SR module and a digital zero crossing detection module for on-line weak signal detection in digital signal processing applications. The two modules are responsible for noise filtering and adaptive parameter configuration, respectively. The on-line weak signal detection can be stably achieved in seconds. The prototype instrument exhibits an advance of 20 dB averaged signal-to-noise ratio and 5 times averaged adjust R-square as compared to the input noisy signal, in considering different driving frequencies and noise levels.

A fault diagnosis strategy based on the wayside acoustic monitoring technique is investigated for locomotive bearing fault diagnosis. Inspired by the transient modeling analysis method based on correlation filtering analysis, a so-called Parametric-Mother-Doppler-Wavelet (PMDW) is constructed with six parameters, including a center characteristic frequency and five kinematic model parameters. A Doppler effect eliminator containing a PMDW generator, a correlation filtering analysis module, and a signal resampler is invented to eliminate the Doppler effect embedded in the acoustic signal of the recorded bearing. Through the Doppler effect eliminator, the five kinematic model parameters can be identified based on the signal itself. Then, the signal resampler is applied to eliminate the Doppler effect using the identified parameters. With the ability to detect early bearing faults, the transient model analysis method is employed to detect localized bearing faults after the embedded Doppler effect is eliminated. The effectiveness of the proposed fault diagnosis strategy is verified via simulation studies and applications to diagnose locomotive roller bearing defects.

Vibration sensor data from a mechanical system are often associated with important measurement information useful for machinery fault diagnosis. However, in practice the existence of background noise makes it difficult to identify the fault signature from the sensing data. This paper introduces the time-frequency manifold (TFM) concept into sensor data denoising and proposes a novel denoising method for reliable machinery fault diagnosis. The TFM signature reflects the intrinsic time-frequency structure of a non-stationary signal. The proposed method intends to realize data denoising by synthesizing the TFM using time-frequency synthesis and phase space reconstruction (PSR) synthesis. Due to the merits of the TFM in noise suppression and resolution enhancement, the denoised signal would have satisfactory denoising effects, as well as inherent time-frequency structure keeping. Moreover, this paper presents a clustering-based statistical parameter to evaluate the proposed method, and also presents a new diagnostic approach, called frequency probability time series (FPTS) spectral analysis, to show its effectiveness in fault diagnosis. The proposed TFM-based data denoising method has been employed to deal with a set of vibration sensor data from defective bearings, and the results verify that for machinery fault diagnosis the method is superior to two traditional denoising methods.

This Note reports a tristable cantilever that exploits stochastic resonance (SR) phenomenon for a study of signal amplification and filtering. The tristable device system combines the benefits of bistable system (wide interwell spacing) and monostable system (smooth motion in potential). The prototype tristable cantilever exhibits 42 times root-mean-square amplitude, 35.86 dB power gain, advance of 15 dB signal-to-noise ratio, and twice fidelity at around 7.6 Hz as compared to the input signal. In a wide operating bandwidth [5.5 Hz, 8.2 Hz], the tristable SR cantilever outperforms the traditional monostable cantilever and bistable SR cantilever in these characteristics.

Non-invasive estimation of minute ventilation is important for quantifying the intensity of physical activity of individuals. In this paper, several improved regression models are presented, based on the measurement of chest and abdomen movements from sensor belts worn by subjects (n = 50) engaged in 14 types of physical activity. Five linear models involving a combination of 11 features were developed, and the effects of different model training approaches and window sizes for computing the features were investigated. The performance of the models was evaluated using experimental data collected during the physical activity protocol. The predicted minute ventilation was compared to the criterion ventilation measured using a bidirectional digital volume transducer housed in a respiratory gas exchange system. The results indicate that the inclusion of breathing frequency and the use of percentile points instead of interdecile ranges over a 60 s window size reduced error by about 43%, when applied to the classical two-degrees-of-freedom model. The mean percentage error of the minute ventilation estimated for all the activities was below 7.5%, verifying reasonably good performance of the models and the applicability of the wearable sensing system for minute ventilation estimation during physical activity.

Estimation of ventilation volume from dimensional changes of the rib cage and abdomen is of interest to researchers interested in quantifying internal exposure to environmental pollutants in the atmosphere. In this paper, we present different statistical regression models for estimating ventilation volume during free-living activities. The movements of the rib cage and abdomen were measured by piezoelectric sensor belts. Multiple linear regression as the calibration method was applied. Five regression models with different combinations out of thirteen features were developed and the performance of these models was compared through experimental study of 11 subjects. The effect of training approaches - model trained for each subject and for all subjects, and the effect of time intervals for computing features were also investigated. The results indicate that Model 2, combining respiratory features and breathing frequency, with a longer time intervals will lead to a higher accuracy.

This paper presents a new method for nonlinear trend estimation of non-stationary signals, by which the trend can be self-adaptively decomposed through calculating the midpoint-based local means. In this method, the so-called midpoints are proposed to construct the local mean of a signal instead of two envelopes in the classical empirical mode decomposition (EMD) algorithm, thus resulting in the midpoint-based empirical decomposition. Furthermore, a negentropy-based statistical method is presented to justify decomposition of the trend. Simulation results indicate that the new algorithm improves the performance of signal decomposition and trend estimation in comparison with the classical EMD algorithm. The proposed method also shows the value in self-adaptively estimating the nonlinear respiratory component from non-invasively measured ventilation signals.

Estimation of respiration commonly employs piezoelectric sensors secured to rib cage and abdominal belts. However, these respiratory signals are often contaminated by tissue artifact. This paper presents a signal decomposition technique for tissue artifact removal in respiratory signals, based on empirical mode decomposition (EMD). After introducing the theoretical foundation, this method is performed on three synthetic signals, and performance of tissue artifact removal using EMD is compared with low-pass filter and independent component analysis (ICA) techniques. A simulation study and experimental results show that EMD can effectively remove tissue artifact in respiratory signals.

Objective: To study the contribution of genetic factors to the variance of serum leptin concentration in healthy, normotensive twins., Methods: A total of 57 pairs of twins were investigated: 28 female and 19 male pairs of monozygotic(MZ) twins, and 6 female and 4 male pairs of dizygotic(DZ) twins. The zygosity of twins was determined by comparing the concordance of the genotype of nine fluorescence-labeled microsatellite markers. The genetic analysis was performed using the variance-based method. Serum leptin levels were determined in duplicate by a radioimmunoassay Kit (Linco Research, Inc., St. Charles, Missouri) as previously described., Results: The test of genetic variance revealed a significantly larger within-pair variance of serum leptin in the DZ twins, in comparison with the MZ twins. The corresponding heritability for serum leptin was 8%. Adjusted for BMI, gender, and uric acid (UA), the heritability for serum leptin was 0.18%. Log leptin correlated significantly with blood pressure (SBp r=0.355 P<0.001; DBp r=0.339 P<0.001). Stepwise multiple linear regression analysis revealed that only BMI, gender and UA were linked independently to serum leptin levels(R(2)=0.788, P<0.001)., Conclusion: The above data indicate that environmental factors other than genetic factors are important determinants of leptinemia in normal subjects.