Induction motors (IMs) are essential components in industrial applications. These motors have to perform numerous tasks under a wide variety of conditions, which affects performance and reliability and gradually brings faults and efficiency losses over time. Nowadays, the industrial sector demands the necessary integration of smart-sensors to effectively diagnose faults in these kinds of motors before faults can occur. One of the most frequent causes of failure in IMs is the degradation of turn insulation in windings. If this anomaly is present, an electric motor can keep working with apparent normality, but factors such as the efficiency of energy consumption and mechanical reliability may be reduced considerably. Furthermore, if not detected at an early stage, this degradation could lead to the breakdown of the insulation system, which could in turn cause catastrophic and irreversible failure to the electrical machine. This paper proposes a novel methodology and its application in a smart-sensor to detect and estimate the healthiness of the winding insulation in IMs. This methodology relies on the analysis of the external magnetic field captured by a coil sensor by applying suitable time-frequency decomposition (TFD) tools. The discrete wavelet transform (DWT) is used to decompose the signal into different approximation and detail coefficients as a pre-processing stage to isolate the studied fault. Then, due to the importance of diagnosing stator winding insulation faults during motor operation at an early stage, this proposal introduces an indicator based on wavelet entropy (WE), a single parameter capable of performing an efficient diagnosis. A smart-sensor is able to estimate winding insulation degradation in IMs using two inexpensive, reliable, and noninvasive primary sensors: a coil sensor and an E-type thermocouple sensor. The utility of these sensors is demonstrated through the results obtained from analyzing six similar IMs with differently induced severity faults.
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.
Manufacturing processes are of great relevance nowadays, when there is a constant claim for better productivity with high quality at low cost. The contribution of this work is the development of a fused smart-sensor, based on FPGA to improve the online quantitative estimation of flank-wear area in CNC machine inserts from the information provided by two primary sensors: the monitoring current output of a servoamplifier, and a 3-axis accelerometer. Results from experimentation show that the fusion of both parameters makes it possible to obtain three times better accuracy when compared with the accuracy obtained from current and vibration signals, individually used.
Automatic tool selection in milling operation has become a very important step in the manufacturing and planning processes for 2.5D piece machining. The main contribution of this article is the development of a new method based on directional morphological approaches, applied to automatic tool selection in computer numerical control milling machines for machining a 2.5D of a geometry piece provided of three-dimensional model of computer-aided design or from an image taken with other devices. First, the image is preprocessed by applying several image processing techniques. Later, mathematical morphology as erosion or dilation to create structural element with the shape of the cutting tool is used. The method displaces a structural element throughout the entire image with the values of the lengths of the piece boundary and the cutting tool to select the correct cutting tool and tool path. Besides, with the same structural element, the zig and zig-zag contour trajectories are obtained in standard computer numerical control code. Results from these experiments show that the method makes it possible to obtain good performance in automatic tool selection when several types of pieces are processed.
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