Search Results (3)

The use of artificial intelligence-based techniques to solve engineering problems is increasing. One of the most challenging tasks facing industry is the timely diagnosis of failures in electromechanical systems, as they are an essential part of production systems. In this sense, the earlier the detection, the higher the economic loss reduction. For this reason, this work proposes the development of a new methodology based on infrared thermography and an artificial intelligence-based classifier for the detection of multiple faults in an electromechanical system. The proposal combines the intensity profile of the grey-scale image, the use of Fast Fourier Transform and an artificial neural network to perform the detection of twelve states for the state of an electromechanical system: healthy, bearing defect, broken rotor bar, misalignment and gear wear on the gearbox. From the experimental setup, 50 thermographic images were obtained for each state. The method was implemented and tested under different conditions to verify its reliability. The results show that the precision, accuracy, recall and F1-score are higher than 99%. Thus, it can be concluded that it is possible to detect multiple conditions in an electromechanical system using the intensity profile and an artificial neural network, achieving good accuracy and reliability. Full article

Sandstones are enriched in deep energy reservoirs and also exist as outcrop rocks, where the pore characteristics of sandstone are influenced by hydration absorption and thermal effects. To study the influence of the initial temperature on the hydration absorption characteristics of outcrop porous sandstone in the Mogao Grottoes, China, an intelligent experimental device for rock hydration was used. The hydration absorption characteristics and temperature effects of sandstone were analyzed by using infrared thermography techniques to monitor the infrared radiation characteristics of the sandstone’s surface during hydration absorption. The experimental results show that the higher the initial temperature of the rock samples, the shorter the time it takes for the sandstone to absorb enough water to reach saturation. The temperature variation of sandstone with different initial temperatures was also determined; the variation in the hydration absorption of sandstone conforms to certain rules, which can be expressed by formulae containing certain parameters. The changing trend of hydration absorption in outcrop porous sandstone shows that the hydration absorption increases rapidly at first, and then the rate of increase slows down until the hydration absorption remains unchanged after saturation. The experimental technique and method provide feasible means and techniques to evaluate the hydration absorption and thermal effects of outcrop porous sandstone, for further detecting the weathering degree of rock grottoes and revealing the damage mechanisms. Full article

Condition-based monitoring (CBM) has emerged as a critical instrument for lowering the cost of unplanned operations while also improving the efficacy, execution, and dependability of tools. Thermal abnormalities can be thoroughly examined using thermography for condition monitoring. Thanks to the advent of high-resolution infrared cameras, researchers are paying more attention to thermography as a non-contact approach for monitoring the temperature rise of objects and as a technique in great experiments to analyze processes thermally. It also allows for the early identification of weaknesses and failures in equipment while it is in use, decreasing system downtime, catastrophic failure, and maintenance expenses. In many applications, the usage of IRT as a condition monitoring approach has steadily increased during the previous three decades. Infrared cameras are steadily finding use in research and development, in addition to their routine use in condition monitoring and preventative maintenance. This study focuses on infrared crucial thermographic theoretical stages, experimental methodologies, relative and absolute temperature requirements, and infrared essential thermographic theoretical processes for electrical and electronics energy applications. Furthermore, this article addresses the major concerns and obstacles and makes some specific recommendations for future development. With developments in artificial intelligence, particularly computer fiction, depending on the present deep learning algorithm, IRT can boost CBM analysis. Full article