In this paper, an indirect method of measuring an analyte concentration in a test solution using the resonant frequency change of a Helmholtz resonator is proposed, using a novel architecture of Helmholtz resonator filled with two kinds of fluids (fixed fluid and test solution). Since the analyte concentration yields changes of density and sound speed of the test solution, the resonant frequency of the proposed Helmholtz resonator is affected by the analyte concentration of the test solution. From this effect, the analyte concentration of the test solution can be measured by the spectrum of acoustic resonance of the Helmholtz resonator. The experiment was done using a 3D-printed Helmholtz resonator system with an acoustic power source and detectors, which is consistent with analytical results and showed that the analyte concentration can be measured with higher sensitivity compared to conventional cantilever-type sensors. As an example application, the possibility of measuring glucose concentration of human blood was demonstrated, showing higher sensitivity and relatively low frequency range compared to previous resonance based methods.

Helmholtz resonators (HR) have been proven to have feasibility for sensor applications with good performance. However, for biosensor application, when the amount of test sample is limited, the operating frequency tends to be very high, which may bring challenges of resonance excitation and frequency measurement. In this paper, a modified HR was proposed for biosensor application at a lower frequency, by designing the neck of the HR as a type of an Archimedes spiral, which can increase the neck length as much as possible without occupying much space. The resonant frequency of the proposed HR was derived by treating it as an HR with a straight neck with the equivalent length. An analytical example was calculated and verified by acoustic finite element analysis, and the results clearly showed that the frequency decreases in comparison to that of the conventional HR. Based on the underlying theory, the measurement principle was proposed and the experiment setup was established using a 3D-printed structure. The experiment was done using test solutions with different glucose concentrations, whose results showed consistency with the analytical results and noticeable frequency increases with the glucose concentration. Prospective results of the proposed HR after miniaturization show an operating frequency around 3 kHz when the amount of test sample is 0.1 mL, which will facilitate use of the common acoustic power source and acoustic pressure sensors in the sensing system.

The acoustic pressure signal generated by blades is one of the key indicators for condition monitoring and fault diagnosis in the field of turbines. Generally, the working conditions of the turbine are harsh, resulting in a large amount of interference and noise in the measured acoustic pressure signal. Therefore, denoising the acoustic pressure signal is the basis of the subsequent research. In this paper, a denoising method of micro-turbine acoustic pressure signal based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Variable step-size Normalized Least Mean Square (VSS-NLMS) algorithms is proposed. Firstly, the CEEMDAN algorithm is used to decompose the original signal into multiple intrinsic mode functions (IMFs), based on the cross-correlation coefficient and continuous mean square error (CMSE) criterion; the obtained IMFs are divided into clear IMFs, noise-dominated IMFs, and noise IMFs. Finally, the improved VSS-NLMS algorithm is adopted to denoise the noise-dominated IMFs and combined with the clear IMF for reconstruction to obtain the final denoised signal. Adopting the above principles, the acoustic pressure signals generated by a micro-turbine with different rotation speeds and different states (normal turbine and fractured turbine) are denoised, respectively, and the results are compared with the axial flow fan test (ideal interference-free signal). The results show that the denoising method proposed in this paper has a good denoising effect, and the denoised signal is smooth and the important features are well preserved, which is conducive to the extraction of acoustic pressure signal characteristics.

Long-term complex alternating loads will significantly impact the lifespan of blades. As a non-contact measurement method, blade tip timing (BTT) is regarded as an efficient technique for blade vibration monitoring as it reflects the condition of blades. Most of the existing parameter identification methods need several sensor and strict probe layout requirements; thus, an inappropriate sensor layout may fail BTT measurements. Using fewer sensors can reduce the impact on the compressor casing strength and the cost of test system. Considering arrangement, cost, and safety restrictions, in this work, a novel blade vibration parameters identification method via a single BTT sensor is proposed. A method of improved BTT technology is presented which obtain two-channel vibration displacement sequence using a single sensor. The elliptic model between two-channel vibration displacement sequence is established. Based on the model, the relationship between blade vibration parameters and elliptic geometric parameters is derived. Numerical simulation and experimental results show that the method is highly feasible and robust. The advantage of this method is that it utilizes only one sensor and achieves high accuracy identification of blade vibration parameter, which provides a new idea to blade vibration monitoring.

For vibration damping, coatings are prepared on surface of the structures (substrates), which constitute the coating‐substrate composite structures. Elastic parameters of the coating are indispensable for the vibration and damping analysis of the composite structure. Due to the small scale of coating thickness and elastic difference compared with the substrate, the identification results are inevitably influenced by the existence of substrate. Moreover, resulting from the preparation process, elastic properties of hard coating often exhibit anisotropic properties. All the above factors bring about the difficulties of accurate identification. In this study, a method for identifying anisotropic elastic parameters of hard coatings considering substrate effect is proposed, by combining nanoindentation and finite element analysis. Based on the identification results, finite element models are established to analyze the vibration characteristics of the coating‐substrate composite structure, which verify the rationality of the anisotropic elastic parameters for vibration analysis. The studies in this paper are significant to more accurately identify the mechanical parameters for establishing the dynamic model. Moreover, they lay the foundation for further optimization design of hard coating damping.