Industrial pipework maintenance inspection can be automated through machine vision-based effusion monitoring. However, colorless effusions such as water can be difficult to detect in a complex industrial environment due to weak illumination and poor visibility of the background. This paper deploys the reflective characteristics of effusion and its lower temperature compared to the environment in order to develop an automatic inspection system for power plant pipeworks’ maintenance. Such a system is aimed at detecting the colorless fluid effusion based on dual source images and a contour features algorithm. In this respect, a visible light source unit highlights the reflective features of the effusion edge. Meanwhile, high-definition images of the potential effusion are acquired under both visible and infrared lights. A customized image processing procedure extracts the potential effusion features from the infrared image to retrieve the region of interest for segmentation purposes and transfer such information to the visible light image to determine the effusion contour. Finally, a decision-making support tool based on the image contour closure is enabled for classification purposes. The implementation of the proposed system is tested on a real industrial environment. Experimental results show a classification accuracy up to 99%, demonstrating excellent suitability in meeting industrial requirements.

The new energy electric vehicle, which takes clean electric energy as the main driving force, has no pollutants and exhaust emissions during its operation and has a higher energy utilization ratio than the fuel locomotive. Therefore, electric vehicles have been widely developed in recent years. The maximum temperature and temperature consistency of the battery pack in the electric vehicle have a great influence on the life and safety of the battery. In this paper, the thermal management system of the lithium battery pack was taken as the research object. The temperature distribution and uniformity of the battery pack under different heat dissipation conditions were analyzed based on computational fluid dynamics (CFD). The multi-objective optimization method of the battery pack thermal management system was carried out by combining the surrogate model with fast non-dominated sorting genetic algorithm (NSGA-II). The maximum temperature of the battery pack obtained from candidate point 1 is 310.72 K, which is 4.99 K lower than the initial model temperature, and the temperature standard deviation is 0.76 K, with a reduction rate of 51.9%. Experiment results showed that maximum difference between the optimized and experimental value of the maximum temperature is 0.8 K, and the error was within 1 K. Therefore, the multi-objective optimization method proposed in this paper has high accuracy.

Purpose: Lubricating oil leakage is a common issue in thermal power plant operation sites, requiring prompt equipment maintenance. The real-time detection of leakage occurrences could avoid disruptive consequences caused by the lack of timely maintenance. Currently, inspection operations are mostly carried out manually, resulting in time-consuming processes prone to health and safety hazards. To overcome such issues, this paper proposes a machine vision-based inspection system aimed at automating the oil leakage detection for improving the maintenance procedures.Design/methodology/approach: The approach aims at developing a novel modular-structured automatic inspection system. The image acquisition module collects digital images along a predefined inspection path using a dual-light (i.e. ultraviolet and blue light) illumination system, deploying the fluorescence of the lubricating oil while suppressing unwanted background noise. The image processing module is designed to detect the oil leakage within the digital images minimizing detection errors. A case study is reported to validate the industrial suitability of the proposed inspection system.Findings: On-site experimental results demonstrate the capabilities to complete the automatic inspection procedures of the tested industrial equipment by achieving an oil leakage detection accuracy up to 99.13%.Practical implications: The proposed inspection system can be adopted in industrial context to detect lubricant leakage ensuring the equipment and the operators safety.Originality/value: The proposed inspection system adopts a computer vision approach, which deploys the combination of two separate sources of light, to boost the detection capabilities, enabling the application for a variety of particularly hard-to-inspect industrial contexts.