In pipeline management, the advent of Internet of Things (IoT) technology enables effective pipeline maintenance with the deployment of embedded sensing units, the basic requirement of which is to make accurate and real-time measurement. Practically, it is impossible to directly measure the internal temperature of the pipe by laying conventional sensors in the oil. This work proposed a novel indirect testing framework to perform real-time and accurate temperature monitoring in oil pipes by solving the inverse problem based on pipe wall's temperature field. The contribution includes three aspects: 1) A rigorous theory was developed to validate the principle of equivalence for heat transfer in pipes. A single-layer boundary-equivalent diffusion model with effective thermal parameters exhibited excellent performance in predicting temperature change in a multilayer structure by simulation. 2) We established a microprocessor-enabled predictive temperature measurement modality to precisely determine the time-dependent temperature evolution in a multilayer composite pipe based on explicit analytical expressions. 3) The correctness and applicability of the modality were verified experimentally on a real oil pipe and a commercial device. The results show that precision of the predictive measurement modality reaches 0.3 K and 0.002 K/s for stable and linear-rising internal fluid temperature, respectively.

The main purpose of industrial nondestructive testing (NDT) is to diagnose the stability, reliability and failure probability of materials, components and structures. Industrial component mechanical strength is one of the most important properties NDT is used to characterize. Subtle but perceptible changes in stress-strain behavior can be reliable indicators of defect formation. A detailed review on the state-of-the-art NDT methods using optical-radiation, photoacoustic, and photothermal techniques for mechanical strength evaluation and defect pre-diagnosis is presented in this article. Mechanical strength is analyzed in terms of the deformation/strain field, the stress-strain relation, and the residual stress in an elastic material subjected to tensile or compressive loading, or impact. By introducing typical NDT experiments, the history and features of each methodology are revisited and typical applications are discussed. This review also aims to be used as a reference toward further research and development of NDT technologies characterizing mechanical strength of materials and components.

In pipeline maintenance, the advent of cloud computing and Internet of Things technologies enable effective pipeline management. Practically, it is impossible to provide fast-response, full-scale, multiterrain, and spatial-level monitoring of pipelines by sparse sensors. In this article, a fitting method for online pipeline structural health monitoring (SHM) is presented. The contribution includes three aspects. First, a new pipeline SHM method based on spatial deformation fitting is proposed, which is useful for three-dimensional full-scale assessment and multiterrain applications with the sparse sensor array. There are no strict restrictions on the number and arrangement of sensors, and no finite-element-model prior information is needed. The monitoring results can be delivered to multiterminals. Second, a rational cubic spline-based fitting algorithm mainly implemented by cloud computing is provided for rapid response. It features three-time interpolation fitting and one-time coordinate transformation. The value correspondence between the measured attitude angles and the fitted deformation is dexterously established. The fitted curve is locally adjustable. Third, the fitting error on deformation is analyzed, and the effectiveness of the method is tested and verified. Reasonable parameter values for the sensor quantity and the fitting spline are suggested.

This study studies the technology of environmental monitoring and control in an intelligent Internet of Things (IoT) perception. To improve the issue of small coverage and low stability in the traditional environmental parameter testing, a new environment monitoring technique based on intelligent perception is proposed. The technical framework consists of three layers, intelligent perception layer, network communication layer, and application layer. The intelligent perception collects and transmits data to the application‐level server via the network communication layer for data analysis and diversified display. Based on this technique, we constructed a smart home environment monitoring and control system. The experimental test result shows that the system can accurately collect and process the data through the server and diversified displayed, which verifies the accuracy and reliability of this technique which is potential for further application.

A hybrid double-loop optimization algorithm combing particle swarm optimization (PSO) and nonintrusive polynomial chaos (NIPC) is proposed for solving the robust trajectory optimization of hypersonic glide vehicle (HGV) under uncertainties. In the outer loop, the PSO method searches globally for the robust optimal control law according to a penalized fitness function that contains the system robustness considerations. In the inner loop, uncertainty propagation of the stochastic system is performed using the NIPC method, to provide statistical moments for the iterative scheme of the PSO method in the outer loop. Only control variables are discretized, and the state constraints are satisfied implicitly through the numerical integration process, which reduces the number of decision variables as well as the huge amount of computation increased by NIPC. In the end, the robust optimal control law is achieved conveniently. Numerical simulations are carried out considering a classical time-optimal trajectory optimization problem of HGV with uncertainties in both initial states and aerodynamic coefficients. The results demonstrate the feasibility and effectiveness of the proposed method.