In gearboxes, the occurrence of unexpected failures such as wear in the gears may occur, causing unwanted downtime with significant financial losses and human efforts. Nowadays, noninvasive sensing represents a suitable tool for carrying out the condition monitoring and fault assessment of industrial equipment in continuous operating conditions. Infrared thermography has the characteristic of being installed outside the machinery or the industrial process under assessment. Also, the amount of information that sensors can provide has become a challenge for data processing. Additionally, with the development of condition monitoring strategies based on supervised learning and artificial intelligence, the processing of signals with significant improvements during the classification of information has been facilitated. Thus, this paper proposes a novel noninvasive methodology for the diagnosis and classification of different levels of uniform wear in gears through thermal analysis with infrared imaging. The novelty of the proposed method includes the calculation of statistical time-domain features from infrared imaging, the consideration of a dimensionality reduction stage by means of Linear Discriminant Analysis, and automatic fault diagnosis performed by an artificial neural network. The proposed method is evaluated under an experimental laboratory data set, which is composed of the following conditions: healthy, and three severity degrees of uniform wear in gears, namely, 25%, 50%, and 75% of uniform wear. Finally, the obtained results are compared with classical condition monitoring approaches based on vibration analysis.
Tool selection is a very important step in manufacturing processes so as to improve productivity with high quality. The contribution of this work is the development of a new method for automatic tool selection in computer numerical control lathe machines, based on image processing techniques and information of the boundary of the piece, provided by either a .DXF file (drawing exchange format) or from an image taken with other devices. The proposed method detects the preferential direction in the boundary of the piece and creates a directional field through a directional gradient aiming at selecting the correct tool. Results from experiments show that the method makes it possible to work with a resolution of 1.1 micrometers, and to obtain good performance in automatic tool selection when several types of twodimensional parts in the image are processed.
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