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Induction motors (IM) play a fundamental role in the industrial sector because they are robust, efficient, and low-cost machines. Changes in the environment, installation errors, or modifications to working conditions can generate faults in induction motors. The trend on IM fault detection is focused on the design techniques and sensors capable of evaluating multiple faults with various signals using non-invasive analysis. The methodology is based on processing electric current signals by applying the short-time Fourier transform (STFT). Additionally, the computation of the mean and standard deviation of infrared thermograms is proposed as main indicators. The proposed system combines both parameters by means of Support Vector Machine and k-nearest-neighbor classifiers. The development of the diagnostic system was done with digital hardware implementations using a Xilinx PYNQ Z2 card that integrates an FPGA with a microprocessor, thus taking advantage of the acquisition and processing of digital signals and images in hardware. The proposed method has proved to be effective for the classification of healthy (HLT), misalignment (MAMT), unbalance (UNB), damaged bearing (BDF), and broken rotor bar (BRB) faults with an accuracy close to 99%. [ABSTRACT FROM AUTHOR]

The former Tekchem Industrial Unit located in the city of Salamanca, Mexico, constitutes an environmental liability in which the presence of high levels of organochlorine pesticides (OCPs) has been reported. In the present study, levels of OCPs were quantified using gas chromatography–mass spectrometry in 52 soil samples and in 88 blood samples from school-age children in the city of Salamanca. A median concentration of 70.6 ng/g (6.93–3299) was obtained for total OCPs in soil, while for the total sum of dichlorodiphenyltrichloroethane (DDT) the value was 49.6 ng/g (6.93–3276). In children, the median level of the total sum of OCPs was 390 ng/g lipid (7.34–14,895), and for the total sum of DDT was 175 ng/g lipid (< LOD—14,802). The OCPs that resulted in highest concentrations in soil were DDT and its metabolites, as well as aldrin and heptachlor epoxide, while in blood the highest levels corresponded to 4,4′-dichlorodiphenyltrichloroethane (4,4′-DDT) and its metabolites, followed by heptachlor and heptachlor epoxide. The spatial distribution of the concentrations of OCPs in soil shows that the facilities of Tekchem may be a significant potential source for the dispersion of these compounds toward the metropolitan area of Salamanca. The results obtained in the present study demonstrate the presence of OCPs in soil and in child population, providing important bases to study the problem from a broader perspective, while reiterating the importance of continuing efforts to generate resolute and precautionary measures with respect to the environmental liability of Tekchem. [ABSTRACT FROM AUTHOR]

Due to their robustness, versatility and performance, induction motors (IMs) have been widely used in many industrial applications. Despite their characteristics, these machines are not immune to failures. In this sense, breakage of the rotor bars (BRB) is a common fault, which is mainly related to the high currents flowing along those bars during start-up. In order to reduce the stresses that could lead to the appearance of these faults, the use of soft starters is becoming usual. However, these devices introduce additional components in the current and flux signals, affecting the evolution of the fault-related patterns and so making the fault diagnosis process more difficult. This paper proposes a new method to automatically classify the rotor health state in IMs driven by soft starters. The proposed method relies on obtaining the Persistence Spectrum (PS) of the start-up stray-flux signals. To obtain a proper dataset, Data Augmentation Techniques (DAT) are applied, adding Gaussian noise to the original signals. Then, these PS images are used to train a Convolutional Neural Network (CNN), in order to automatically classify the rotor health state, depending on the severity of the fault, namely: healthy motor, one broken bar and two broken bars. This method has been validated by means of a test bench consisting of a 1.1 kW IM driven by four different soft starters coupled to a DC motor. The results confirm the reliability of the proposed method, obtaining a classification rate of 100.00% when analyzing each model separately and 99.89% when all the models are analyzed at a time. [ABSTRACT FROM AUTHOR]