The intensity of the infrared radiation emitted by objects is mainly a function of their temperature. In infrared thermography, this feature is used for multiple purposes: as a health indicator in medical applications, as a sign of malfunction in mechanical and electrical maintenance or as an indicator of heat loss in buildings. This paper presents a review of infrared thermography especially focused on two applications: temperature measurement and non-destructive testing, two of the main fields where infrared thermography-based sensors are used. A general introduction to infrared thermography and the common procedures for temperature measurement and non-destructive testing are presented. Furthermore, developments in these fields and recent advances are reviewed.

The maintenance of industrial equipment extends its useful life, improves its efficiency, reduces the number of failures, and increases the safety of its use. This study proposes a methodology to develop a predictive maintenance tool based on infrared thermographic measures capable of anticipating failures in industrial equipment. The thermal response of selected equipment in normal operation and in controlled induced anomalous operation was analyzed. The characterization of these situations enabled the development of a machine learning system capable of predicting malfunctions. Different options within the available conventional machine learning techniques were analyzed, assessed, and finally selected for electronic equipment maintenance activities. This study provides advances towards the robust application of machine learning combined with infrared thermography and augmented reality for maintenance applications of industrial equipment. The predictive maintenance system finally selected enables automatic quick hand-held thermal inspections using 3D object detection and a pose estimation algorithm, making predictions with an accuracy of 94% at an inference time of 0.006 s.

The need for improved quality control in industry makes object detection crucial. This work addresses the challenging problem of subsurface defect detections using a combination of active thermography and deep learning. The novel contribution of this work is to pose the problem as one of object detection rather than semantic segmentation or classification. The images used as input for the deep learning algorithms are three-channel color images obtained using principal component thermography (PCT). The use of these images improves the signal-to-noise ratio (SNR). A framework to label ground truths automatically is also created. The most widely used deep learning detector algorithms were evaluated, and the fifth version of you only look once (YOLO), i.e., YOLOv5, was selected because of its excellent average precision (AP) and its low inference time. The resulting combination of this algorithm and active thermography is effective and accurate in detecting subsurface defects.

In recent years, the benefits of both Augmented Reality (AR) technology and infrared thermography (IRT) have been demonstrated in the industrial maintenance sector, allowing maintenance operations to be carried out in a safer, faster, and more efficient manner. However, there still exists no solution that optimally combines both technologies. In this work, we propose a new AR system—MANTRA—with specific application to industrial maintenance. The system can automatically align virtual information and temperature on any 3D object, in real time. This is achieved through the joint use of an RGB-D sensor and an IRT camera, leading to high accuracy and robustness. To achieve this objective, a pose estimation method that combines a deep-learning-based object detection method, YOLOV4, together with the template-based LINEMOD pose estimation method, as well as a model-based 6DOF pose tracking technique, was developed. The MANTRA system is validated both quantitatively and qualitatively through a real use-case, demonstrating the effectiveness of the system compared to traditional methods and those using only AR.

The authors introduce a new controller, aimed at industrial domains, that improves the performance and accuracy of positioning systems based on Stewart platforms. More specifically, this paper presents, and validates experimentally, a sliding mode control for precisely positioning a Stewart platform used as a mobile platform in non-destructive inspection (NDI) applications. The NDI application involves exploring the specimen surface of aeronautical coupons at different heights. In order to avoid defocusing and blurred images, the platform must be positioned accurately to keep a uniform distance between the camera and the surface of the specimen. This operation requires the coordinated control of the six electro mechanic actuators (EMAs). The platform trajectory and the EMA lengths can be calculated by means of the forward and inverse kinematics of the Stewart platform. Typically, a proportional integral (PI) control approach is used for this purpose but unfortunately this control scheme is unable to position the platform accurately enough. For this reason, a sliding mode control (SMC) strategy is proposed. The SMC requires: (1) a priori knowledge of the bounds on system uncertainties, and (2) the analysis of the system stability in order to ensure that the strategy executes adequately. The results of this work show a higher performance of the SMC when compared with the PI control strategy: the average absolute error is reduced from 3.45 mm in PI to 0.78 mm in the SMC. Additionally, the duty cycle analysis shows that although PI control demands a smoother actuator response, the power consumption is similar.