An improved compressed sensing method for dynamic impact signals based on cubic spline interpolation.

During the dynamic acquisition of impact signals, a high sampling frequency brings significant challenges to the analog-to-digital converter and other test systems. To address this issue, in this study, an improved compressed sensing (CS) method is proposed for the measurement of impact signals based on cubic spline interpolation (CSI). According to the characteristics of the dynamic impact signal, a random non-uniform sampling strategy combining CS and CSI is presented. The CSI obviously reduces the number of observation points required by the traditional CS. To resolve the problem that the traditional orthogonal matching pursuit (OMP) algorithm can only guarantee the local optimal solution but cannot obtain the global optimal solution, an improved orthogonal matching pursuit (IOMP) algorithm is proposed. First, n atoms related to residuals are selected to build a local atomic dictionary. Subsequently, the atom most relevant to the signal observation result is selected from the local atomic dictionary. The iteration process is repeated until enough atoms are selected. The IOMP algorithm effectively improves the success rate of reconstruction. Finally, an impact signals test platform based on the Machete hammer is established. The results of theoretical simulations and several experiments indicate that the data reconstruction error of the proposed improved CS method for impact signals is approximately 5.0%.

A study on pick cutting properties with full-scale rotary cutting experiments and numerical simulations.

To investigate the cutting forces on road-header picks, a series of full-scale single-pick rotary cutting tests on sandstone samples were conducted at the National Engineering Laboratory of Coal Mining Machinery and Equipment, China. The primary objective of this study is to optimize the cut spacing and to verify the numerical simulation results. Cutting forces are investigated under different cutting depths and cut spacings. Cut spacing is optimized by analyzing the specific energy, coarseness index, and cutting force. The rock cutting process is simulated on a pick model using the PFC3D software. Rock samples are used as models, and particle assemblies and micro-properties are calibrated by uniaxial compressive strength tests and Brazilian disc tensile strength tests. The optimum ratio of cut spacing to cutting depth for the analyzed sandstone is determined to be in the range of 3 to 4. The experimental results show that a higher coarseness index corresponds to an increased block ratio, and specific energy decreases under optimum cutting conditions. Forces acting on the pick model are determined by simulation. A reasonable agreement exists between the experimental and numerical simulation results regarding the pick forces. The influence of the cut spacing on the rock-breaking effect observed in the experiments is confirmed by numerical simulations. Therefore, numerical simulations using the PFC3D software represent a reliable method for predicting the pick forces.

High precision measurement method for dynamic transient signal based on compressed sensing and spline polynomial interpolation.

During the measurement of dynamic transient signals, a high sampling frequency brings great challenges to the analog-to-digital converter (ADC) and testing system. To address these issues, a high precision measurement method for dynamic transient signals is first proposed in this paper. The characteristics of dynamic transient signals are analyzed first. On the basis of this, a random sampling method combining compressed sensing (CS) with spline polynomial interpolation (SPI) is put forward. The fusion of the two algorithms can effectively reduce the quantity of sampling and observation points to reduce the requirement of the ADC and testing system for transient signal measurement and to improve the observation efficiency of the existing uniform sampling. Finally, a Machete hammer test platform for dynamic transient signals is established. A series of simulation and experimental results validate that the error of data reconstruction using the random sampling method combining CS with SPI is not greater than 5.1%.

Impact feature recognition method for non-stationary signals based on variational modal decomposition noise reduction and support vector machine optimized by whale optimization algorithm.

It is difficult to effectively distinguish the key information of non-stationary dynamic signals in many engineering applications, such as fault detection, geological exploration, and logistics transportation. To deal with this problem, a classification and recognition algorithm based on variational mode decomposition (VMD) and the Support Vector Machine (SVM) optimized by the Whale Optimization Algorithm (WOA) optimization model is first proposed in this study. The algorithm first applies VMD to decompose the non-stationary time-domain signals into multiple variational intrinsic mode functions (VIMFs). Then, it calculates the correlation coefficient between each mode and the original signals and conducts signal reconstruction by sorting the VIMFs. On the base of this, it performs modal filtering on the non-stationary signals according to the correlation coefficients between the reconstructed signal and the original signal. Subsequently, the WOA is used to optimize two key parameters of the SVM. Finally, the optimization model is exploited to classify and recognize the impact and vibration of non-stationary signals. A series of simulations and experiments for the algorithm is carried out and analyzed deeply. The comparative test results indicate that the classification and recognition method for non-stationary signals based on VMD and WOA-SVM (VMD-WOA-SVM) proposed in this paper converges faster and recognizes the key information of non-stationary dynamic signals more accurately with a recognition precision of 96.66%.

Modeling and Studying Acceleration-Induced Effects of Piezoelectric Pressure Sensors Using System Identification Theory.

Transient pressure testing is often accompanied by shock acceleration. Aiming at the acceleration-induced effects of pressure sensors, a dynamic compensation method combining empirical mode decomposition (EMD) with system identification theory (SIT) is proposed in this paper. This method is more effective at reducing the error of the acceleration-induced effects without affecting the sensor's sensitivity and inherent frequency. The principle and theoretical basis of acceleration-induced effects is analyzed, and the static and dynamic acceleration-induced effects on the quartz crystal of a piezoelectric pressure sensor are performed. An acceleration-induced effects dynamic calibration system is built using a Machete hammer, which generates acceleration signals with larger amplitude and narrower pulse width, and an autoregressive exogenous (ARX)mathematical model of acceleration-induced effects is obtained using empirical mode decomposition-system identification theory (EMD-SIT). A digital compensation filter for acceleration-induced effects is designed on the basis of this model. Experimental results explain that the acceleration-induced effects of the pressure sensor were less than 11% after using the digital compensation filter. A series of test data verify the accuracy, reliability, and generality of the model.