Low-Frequency Bandgaps of the Lightweight Single-Phase Acoustic Metamaterials with Locally Resonant Archimedean Spirals

In order to achieve the dual needs of single-phase vibration reduction and lightweight, a square honeycomb acoustic metamaterials with local resonant Archimedean spirals (SHAMLRAS) is proposed. The independent geometry parameters of SHAMLRAS structures are acquired by changing the spiral control equation. The mechanism of low-frequency bandgap generation and the directional attenuation mechanism of in-plane elastic waves are both explored through mode shapes, dispersion surfaces, and group velocities. Meanwhile, the effect of the spiral arrangement and the adjustment of the equation parameters on the width and position of the low-frequency bandgap are discussed separately. In addition, a rational period design of the SHAMLRAS plate structure is used to analyze the filtering performance with transmission loss experiments and numerical simulations. The results show that the design of acoustic metamaterials with multiple Archimedean spirals has good local resonance properties, and forms multiple low-frequency bandgaps below 500 Hz by reasonable parameter control. The spectrograms calculated from the excitation and response data of acceleration sensors are found to be in good agreement with the band structure. The work provides effective design ideas and a low-cost solution for low-frequency noise and vibration control in the aeronautic and astronautic industries.

Sensor-integrating gears: wear detection by in-situ MEMS acceleration sensors

Gear tooth wear is a common phenomenon leading to malfunctions in machines. To detect wear and faults, gear condition monitoring by vibration is established. The problem is that the measurement data quality for detection of wear by vibration is not good enough with currently established measurement methods, caused by long signal paths of the commonly used housing mounted sensors. In-situ sensors directly at the gear achieve better data quality, but are not yet proved in wear detection. Further it is unknown what analysis methods are suited for in-situ sensor data. Existing gear condition metrics are mainly focused on localized gear tooth faults, and do not estimate wear related values. This contribution aims to improve wear detection by investigating in-situ sensors and advance gear condition metrics. Using a gear test rig to conduct an end of life test, the wear detection ability of an in-situ sensor system and reference sensors on the bearing block are compared through standard gear condition metrics. Furthermore, a machine-learned regression model is developed that maps multiple features related to gear dynamics to the gear mass loss. The standard gear metrics used on the in-situ sensor data are able to detect wear, but not significantly better compared to the other sensors. The regression model is able to estimate the actual wear with a high accuracy. Providing a wear related output improves the wear detection by better interpretability.

Impact of Sensor Data Characterization with Directional Nature of Fault and Statistical Feature Combination for Defect Detection on Roll-to-Roll Printed Electronics

Gravure printing, which is a roll-to-roll printed electronics system suitable for high-speed patterning of functional layers have advantages of being applied to flexible webs in large areas. As each of the printing procedure from inking to doctoring followed by ink transferring and setting influences the quality of the pattern geometry, it is necessary to detect and diagnose factors causing the printing defects beforehand. Data acquisition with three triaxial acceleration sensors for fault diagnosis of four major defects such as doctor blade tilting fault was obtained. To improve the diagnosis performances, optimal sensor selection with Sensor Data Efficiency Evaluation, sensitivity evaluation for axis selection with Directional Nature of Fault and feature variable optimization with Feature Combination Matrix method was applied on the raw data to form a Smart Data. Each phase carried out on the raw data progressively enhanced the diagnosis results in contents of accuracy, positive predictive value, diagnosis processing time, and data capacity. In the case of doctor blade tilting fault, the diagnosis accuracy increased from 48% to 97% with decreasing processing time of 3640 s to 16 s and the data capacity of 100 Mb to 5 Mb depending on the input data between raw data and Smart Data.

Research on the New Drive of a Laboratory Screen with Rectilinear Vibrations in Transient States

The paper presents the results of research on the vibrating motion of a laboratory screen with a rectilinear (segmental) trajectory of vibrations during its start-up and braking. The investigations were carried out on a modernized stand equipped with a system of two vibrating motors applied in newer solutions of industrial screens, which are mounted directly on the riddle. The tests were carried out for three different frequencies using three-axis acceleration sensors. The analysed parameter was the increase in the amplitude of vibrations in transient states compared to the amplitude during the stable operation of the device. The maximum multiplication of the vibration amplitude of the classic drive system during start-up was 9.7 (mm/mm) in the vertical direction and 5.7 (mm/mm) for the new system. During braking, the maximum multiplication of the vibration amplitude of the classic drive system was 6.9 (mm/mm) vertically, while for the drive system with vibration motors, it was 11.4 (mm/mm). The absence of flexible couplings in the drive system reduces the damping of vibrations and increases the value of amplitude during the start-up and free braking of the machine. This does not have a major influence on the correct operation of the machine in a steady state. However, the use of the new drive system resulted in a significant reduction in power demand and shortened the start-up time, which has a positive effect on the operating costs of the machine.

Research Status and Development Trend of Magnetic Fluid Acceleration Sensors

As a new type of accelerometer, in recent years, the magnetic liquid acceleration sensor has attracted widespread attention worldwide, and related research results have also continued to emerge. This article mainly introduces the theoretical basis and general structure of the magnetic liquid acceleration sensor, and according to the difference of inertial mass, briefly describes the research progress of the magnetic liquid acceleration sensor by national and foreign scholars in recent years and some in existing problems. Finally, suggestions and prospects for the future development trend of the magnetic liquid acceleration sensor are given.

Estimation of Knee Joint Extension Force Using Mechanomyography Based on IGWO-SVR Algorithm

Muscle force is an important physiological parameter of the human body. Accurate estimation of the muscle force can improve the stability and flexibility of lower limb joint auxiliary equipment. Nevertheless, the existing force estimation methods can neither satisfy the accuracy requirement nor ensure the validity of estimation results. It is a very challenging task that needs to be solved. Among many optimization algorithms, gray wolf optimization (GWO) is widely used to find the optimal parameters of the regression model because of its superior optimization ability. Due to the traditional GWO being prone to fall into local optimum, a new nonlinear convergence factor and a new position update strategy are employed to balance local and global search capability. In this paper, an improved gray wolf optimization (IGWO) algorithm to optimize the support vector regression (SVR) is developed to estimate knee joint extension force accurately and timely. Firstly, mechanomyography (MMG) of the lower limb is measured by acceleration sensors during leg isometric muscle contractions extension training. Secondly, root mean square (RMS), mean absolute value (MAV), zero crossing (ZC), mean power frequency (MPF), and sample entropy (SE) of the MMG are extracted to construct feature sets as candidate data sets for regression analysis. Lastly, the features are fed into IGWO-SVR for further training. Experiments demonstrate that the IGWO-SVR provides the best performance indexes in the estimation of knee joint extension force in terms of RMSE, MAPE, and R compared with the other state-of-art models. These results are expected to become the most effective as guidance for rehabilitation training, muscle disease diagnosis, and health evaluation.

Rotating Machinery Diagnosing in Non-Stationary Conditions with Empirical Mode Decomposition-Based Wavelet Leaders Multifractal Spectra

Diagnosing the condition of rotating machines by non-invasive methods is based on the analysis of dynamic signals from sensors mounted on the machine—such as vibration, velocity, or acceleration sensors; torque meters; force sensors; pressure sensors; etc. The article presents a new method combining the empirical mode decomposition algorithm with wavelet leader multifractal formalism applied to diagnosing damages of rotating machines in non-stationary conditions. The development of damage causes an increase in the level of multifractality of the signal. The multifractal spectrum obtained as a result of the algorithm changes its shape. Diagnosis is based on the classification of the features of this spectrum. The method is effective in relation to faults causing impulse responses in the dynamic signal registered by the sensors. The method has been illustrated with examples of vibration signals of rotating machines recorded on a laboratory stand, as well as on real objects.

One-Dimensional Convolutional Neural Network for Drill Bit Failure Detection in Rotary Percussion Drilling

Drill bit failure is a prominent concern in the drilling process of any mine, as it can lead to increased mining costs. Over the years, the detection of drill bit failure has been based on the operator’s skills and experience, which are subjective and susceptible to errors. To enhance the efficiency of mining operations, it is necessary to implement applications of artificial intelligence to produce a superior method for drill bit monitoring. This research proposes a new and reliable method to detect drill bit failure in rotary percussion drills using deep learning: a one-dimensional convolutional neural network (1D CNN) with time-acceleration as input data. 18 m3 of granite rock were drilled horizontally using a rock drill and intact tungsten carbide drill bits. The time acceleration of drill vibrations was measured using acceleration sensors mounted on the guide cell of the rock drill. The drill bit failure detection model was evaluated on five drilling conditions: normal, defective, abrasion, high pressure, and misdirection. The model achieved a classification accuracy of 88.7%. The proposed model was compared to three state-of-the-art (SOTA) deep learning neural networks. The model outperformed SOTA methods in terms of classification accuracy. Our method provides an automatic and reliable way to detect drill bit failure in rotary percussion drills.

A comparative study of different acceleration sensors in measuring energy consumption of human martial arts

At present, there are many acceleration sensors for measuring human martial arts in the market. However, due to the inaccurate measurement of some acceleration sensors, people who love martial arts are deeply troubled and unable to find an excellent acceleration sensor specifically for energy consumption detection of human martial arts. The development of this sensor is imminent, which is of great significance for the comparative study of energy consumption measurement of human martial arts in our country. In this study, 160 students aged 11–14 years were selected, and the subjects were divided into normal body mass group and abnormal body mass group. Of the 96 male adolescents, 32 were obese body mass, which was determined as male abnormal body mass Group; 64 male adolescents were normal body weight and male normal body weight group; female 64 adolescents were normal body weight and set as female normal body mass group. Using a built-in accelerometer and a mobile phone three-dimensional accelerometer, the subjects were subjected to a 3–8 km/h human martial arts exercise load test (each speed is continuously performed for 5 min). The two acceleration sensors collectively assess the accuracy of the prediction of the use of force in human martial arts experiments. The average power consumption of human art exercises uses a frequency of 60 times/min, 90 times/min and 120 times/min compared to two acceleration sensors. Test results show that the data points for the mobile accelerator eraser are scattered, and the distance between the data varies. The data points of the three-dimensional acceleration sensor are more concentrated and present a certain trend. The use of three-dimensional acceleration sensors to measure martial arts can fully reflect the energy consumption of human activities, and achieve an energy consumption measurement accuracy of more than 94%.