A New Method Based on Time-Varying Filtering Intrinsic Time-Scale Decomposition and General Refined Composite Multiscale Sample Entropy for Rolling-Bearing Feature Extraction.

The early fault diagnosis of rolling bearings has always been a difficult problem due to the interference of strong noise. This paper proposes a new method of early fault diagnosis for rolling bearings with entropy participation. First, a new signal decomposition method is proposed in this paper: intrinsic time-scale decomposition based on time-varying filtering. It is introduced into the framework of complete ensemble intrinsic time-scale decomposition with adaptive noise (CEITDAN). Compared with traditional intrinsic time-scale decomposition, intrinsic time-scale decomposition based on time-varying filtering can improve frequency-separation performance. It has strong robustness in the presence of noise interference. However, decomposition parameters (the bandwidth threshold and B-spline order) have significant impacts on the decomposition results of this method, and they need to be artificially set. Aiming to address this problem, this paper proposes rolling-bearing fault diagnosis optimization based on an improved coyote optimization algorithm (COA). First, the minimal generalized refined composite multiscale sample entropy parameter was used as the objective function. Through the improved COA algorithm, optimal intrinsic time-scale decomposition parameters based on time-varying filtering that match the input signal are obtained. By analyzing generalized refined composite multiscale sample entropy (GRCMSE), whether the mode component is dominated by the fault signal is determined. The signal is reconstructed and decomposed again. Finally, the mode component with the highest energy in the central frequency band is selected for envelope spectrum variation for fault diagnosis. Lastly, simulated and experimental signals were used to verify the effectiveness of the proposed method.

Rolling Bearing Fault Diagnosis Based on Refined Composite Multi-Scale Approximate Entropy and Optimized Probabilistic Neural Network.

A rolling bearing early fault diagnosis method is proposed in this paper, which is derived from a refined composite multi-scale approximate entropy (RCMAE) and improved coyote optimization algorithm based probabilistic neural network (ICOA-PNN) algorithm. Rolling bearing early fault diagnosis is a time-sensitive task, which is significant to ensure the reliability and safety of mechanical fault system. At the same time, the early fault features are masked by strong background noise, which also brings difficulties to fault diagnosis. So, we firstly utilize the composite ensemble intrinsic time-scale decomposition with adaptive noise method (CEITDAN) to decompose the signal at different scales, and then the refined composite multi-scale approximate entropy of the first signal component is calculated to analyze the complexity of describing the vibration signal. Afterwards, in order to obtain higher recognition accuracy, the improved coyote optimization algorithm based probabilistic neural network classifiers is employed for pattern recognition. Finally, the feasibility and effectiveness of this method are verified by rolling bearing early fault diagnosis experiment.

An enhanced extrapolation method based on particle swarm optimization-support vector regression to determine the friction coefficient between aircraft tire and runway surface.

This paper proposes a hybrid pretreating method in the support vector regression (SVR) machines to enhance the extrapolated performance of the traditional method based on particle swarm optimization (PSO)-SVR. This method is introduced into a novel domain wherein the friction coefficient (COF) is extrapolated between the aircraft tire and the runway surface. It includes two parts: the normalized data and the extrapolated method for COF. The first part develops a novel data normalized method, which allows the experiment data to follow sine distribution (SN) instead of traditional linear distribution. For the second part, the SVR with the training set initially extrapolates a COF, and the COF is subsequently added into the original training set to build a new training sample. This process is repeated until the extrapolation is finished. This method is named step-wise (SW) extrapolation. PSO is used to optimize the regularization constant C, the parameter gamma γ, and epsilon parameter ε in SVR. Finally, this study indicates that the proposed method (SN-SW-PSO-SVR) is more suitable for the extrapolation of the COF between the airplane tire and the runway surface than other extrapolated methods.

Fault Feature Extraction and Diagnosis of Rolling Bearings Based on Enhanced Complementary Empirical Mode Decomposition with Adaptive Noise and Statistical Time-Domain Features.

In this paper, a novel method is proposed to enhance the accuracy of fault diagnosis for rolling bearings. First, an enhanced complementary empirical mode decomposition with adaptive noise (ECEEMDAN) method is proposed by determining two critical parameters, namely the amplitude of added white noise (AAWN) and the ensemble trails (ET). By introducing the concept of decomposition level, the optimal AAWN can be determined by judging the mutation of mutual information (MI) between adjacent intrinsic mode functions (IMFs). Furthermore, the ET is fixed at two to reduce the computational cost. This method can avoid disturbance of the spurious mode in the signal decomposition and increase computational speed. Enhanced CEEMDAN demonstrates a more significant improvement than that of the traditional CEEMDAN. Vibration signals can be decomposed into a set of IMFs using enhanced CEEMDAN. Some IMFs, which are named intrinsic information modes (IIMs), effectively reflect the vibration characteristic. The evaluated comprehensive factor (CF), which combines the shape, crest and impulse factors, as well as the kurtosis, skewness, and latitude factor, is developed to identify the IIM. CF can retain the advantage of a single factor and make up corresponding drawbacks. Experiment results, especially for the extraction of bearing fault under variable speed, illustrate the superiority of the proposed method for the fault diagnosis of rolling bearings over other methods.

Dissipation of two field-incurred pesticides and three degradation products in rice (Oryza sativa L.) from harvest to dining table.

BACKGROUND: High levels of harmful pesticide residues in rice can cause undesirable side effects and are a source of great concern to consumers. Reduction of pesticide residues to provide rice security has thus became an urgent problem. RESULTS: In this study, the effects of commercial and home processing on removal of chlorpyrifos and carbosulfan residues from rice, and the formation of metabolites during processing, were studied. The results showed that 3,5,6-trichloro-2-pyridinol (0.87 mg kg-1 ) and carbofuran (0.43 mg kg-1 ) were the predominant components detected in paddy rice. All detected residues were primarily deposited on the rice hull and bran. Washing twice followed by high-pressure cooking was able to further decrease residues in polished rice with the processing factor value <0.25. Following application of pesticides at the recommended rate and twice the recommended rate, with a preharvest interval of 28 days, changes in residues from harvest to dining table based on efficient processing techniques were investigated. The final residues dropped to below maximum residue levels after washing twice followed by high-pressure cooking. CONCLUSION: This simple cooking process thus reduces the risk of dietary exposure, and it is recommended that it is adopted by all consumers. © 2019 Society of Chemical Industry.

Experimental rig to estimate the coefficient of friction between tire and surface in airplane touchdown simulations.

To estimate the coefficient of friction between tire and runway surface during airplane touchdowns, we designed an experimental rig to simulate such events and to record the impact and friction forces being executed. Because of noise in the measured signals, we developed a filtering method that is based on the ensemble empirical mode decomposition and the bandwidth of probability density function of each intrinsic mode function to extract friction and impact force signals. We can quantify the coefficient of friction by calculating the maximum values of the filtered force signals. Signal measurements are recorded for different drop heights and tire rotational speeds, and the corresponding coefficient of friction is calculated. The result shows that the values of the coefficient of friction change only slightly. The random noise and experimental artifact are the major reason of the change.

[Experiment study on the metamorphic amylum production wastewater treatment by anaerobic baffled reactor].

The present study reports the start-up of treating metamorphic amylum production wastewater by anaerobic baffled reactor (ABR) and the bio-chemical features of granular sludge. The optimal conditions in treatment of the metamorphic amylum production wastewater were proposed, and the running performance in treating salt-containing wastewater was studied. Our results show that the common anaerobic activated sludge can be acclimated by increasing the organic loading and salt-concentration at the same time, and the granular sludge tolerant to low salt can be achieved by the acclimation. When chloric ion was 8,500 mg/L and salinity was 1.6%, the anaerobic activated sludge could degrade organic materials normally in the wastewater after the acclimation, and the COD removal is over 85%. When the concentration of metamorphic amylum production wastewater in the experiment was 12,640 mg/L and the optimal hydraulic retention time was 48 h, the removal efficiency of COD was 85.9%. Effect of sharp decrease of chloric ion concentration on sludge microorganism is larger than that of the sharp increase in the system. The system can endure the change of chloric ion concentration by increasing from 8,500 mg/L to 12,500 mg/L or decreasing from 8,500 mg/L to 4,500 mg/L, and it is more tolerant to the sudden increase than that of the sudden decrease of chloric ion concentration. The ABR system can treat the wastewater with chloric ion below 15,000 mg/L and salinity of about 2.5%.