Fault Diagnosis for Reducers Based on a Digital Twin.

A new method based on a digital twin is proposed for fault diagnosis, in order to compensate for the shortcomings of the existing methods for fault diagnosis modeling, including the single fault type, low similarity, and poor visual effect of state monitoring. First, a fault diagnosis test platform is established to analyze faults under constant and variable speed conditions. Then, the obtained data are integrated into the Unity3D platform to realize online diagnosis and updated with real-time working status data. Finally, an industrial test of the digital twin model is conducted, allowing for its comparison with other advanced methods in order to verify its accuracy and application feasibility. It was found that the accuracy of the proposed method for the entire reducer was 99.5%, higher than that of other methods based on individual components (e.g., 93.5% for bearings, 96.3% for gear shafts, and 92.6% for shells).

DV-Hop Algorithm Based on Multi-Objective Salp Swarm Algorithm Optimization.

The localization of sensor nodes is an important problem in wireless sensor networks. The DV-Hop algorithm is a typical range-free algorithm, but the localization accuracy is not high. To further improve the localization accuracy, this paper designs a DV-Hop algorithm based on multi-objective salp swarm optimization. Firstly, hop counts in the DV-Hop algorithm are subdivided, and the average hop distance is corrected based on the minimum mean-square error criterion and weighting. Secondly, the traditional single-objective optimization model is transformed into a multi-objective optimization model. Then, in the third stage of DV-Hop, the improved multi-objective salp swarm algorithm is used to estimate the node coordinates. Finally, the proposed algorithm is compared with three improved DV-Hop algorithms in two topologies. Compared with DV-Hop, The localization errors of the proposed algorithm are reduced by 50.79% and 56.79% in the two topology environments with different communication radii. The localization errors of different node numbers are decreased by 38.27% and 56.79%. The maximum reductions in localization errors are 38.44% and 56.79% for different anchor node numbers. Based on different regions, the maximum reductions in localization errors are 56.75% and 56.79%. The simulation results show that the accuracy of the proposed algorithm is better than that of DV-Hop, GWO-DV-Hop, SSA-DV-Hop, and ISSA-DV-Hop algorithms.

Dual Cluster Head Optimization of Wireless Sensor Networks Based on Multi-Objective Particle Swarm Optimization.

Energy conservation is one of the main problems in a wireless sensor network (WSN). Compared with a single cluster head (CH), a dual CH optimization was proposed for less energy consumption by the WSN and an acquisition delay by the mobile sink (MS). Firstly, a fuzzy c-means clustering algorithm and a multi-objective particle swarm optimization were utilized for the determinations of the first and second CHs. Following that, the ideal trajectory of MS was assessed using the improved ant colony algorithm. Finally, the lifetimes, the death rounds of the first node and the 50% node, and the number of packets received at the base station were compared among the proposed approach. Moreover, five algorithms were compared to validate the optimization, and the improved trajectory was compared with the original one as well. It was found that, for 100 nodes, the number of dead rounds from the proposal increased by 7.9%, 22.9%, 25.1%, 61%, and 74.4% for the first node, and that of the 50% nodes increased by 27.8%, 34.2%, 98.3%, 213.1%, and 211.2%, respectively. The base station packet reception increased by about 19.3%, 53.5%, 27%, 86.8%, and 181.2%, respectively. The trajectory of MS could also decrease by about 10%.

Simultaneous noninvasive ultrasensitive detection of prostate specific antigen and lncRNA PCA3 using multiplexed dual optical microfibers with strong plasmonic nanointerfaces.

Low accuracy of diagnosing prostate cancer (PCa) was easily caused by only assaying single prostate specific antigen (PSA) biomarker. Although conventional reported methods for simultaneous detection of two specific PCa biomarkers could improve the diagnostic efficiency and accuracy, low detection sensitivity restrained their use in extreme early-stage PCa clinical assay applications. In order to overcome above drawbacks, this paper herein proposed a multiplexed dual optical microfibers separately functionalized with gold nanorods (GNRs) and Au nanobipyramids (Au NBPs) nanointerfaces with strong localized surface plasmon resonance (LSPR) effects. The sensors could simultaneously detect PSA protein biomarker and long noncoding RNA prostate cancer antigen 3 (lncRNA PCA3) with ultrahigh sensitivity and remarkable specificity. Consequently, the proposed dual optical microfibers multiplexed biosensors could detect the PSA protein and lncRNA PCA3 with ultra-low limit-of-detections (LODs) of 3.97 × 10-15 mol/L and 1.56 × 10-14 mol/L in pure phosphorus buffer solution (PBS), respectively, in which the obtained LODs were three orders of magnitude lower than existed state-of-the-art PCa assay technologies. Additionally, the sensors could discriminate target components from complicated physiological environment, that showing noticeable biosensing specificity of the sensors. With good performances of the sensors, they could successfully assay PSA and lncRNA PCA3 in undiluted human serum and urine simultaneously, respectively. Consequently, our proposed multiplexed sensors could real-time high-sensitivity simultaneously detect complicated human samples, that providing a novel valuable approach for the high-accurate diagnosis of early-stage PCa individuals.