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Classification systems based on machine learning (ML) models, critical in predictive maintenance and fault diagnosis, are subject to an error rate that can pose significant risks, such as unnecessary downtime due to false alarms. Propagating the uncertainty of input data through the model can define confidence bands to determine whether an input is classifiable, preferring to indicate a result of unclassifiability rather than misclassification. This study presents an electrical fault diagnosis system on asynchronous motors using an artificial neural network (ANN) model trained with vibration measurements. It is shown how vibration analysis can be effectively employed to detect and locate motor malfunctions, helping reduce downtime, improve process control and lower maintenance costs. In addition, measurement uncertainty information is introduced to increase the reliability of the diagnosis system, ensuring more accurate and preventive decisions.
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In traditional nonintrusive load monitoring (NILM) systems, the measurement device is installed upstream of an electrical system to acquire the total aggregate absorbed power and derive the powers absorbed by the individual electrical loads. Knowing the energy consumption related to each load makes the user aware and capable of identifying malfunctioning or less-efficient loads in order to reduce consumption through appropriate corrective actions. To meet the feedback needs of modern home, energy, and assisted environment management systems, the nonintrusive monitoring of the power status (ON or OFF) of a load is often required, regardless of the information associated with its consumption. This parameter is not easy to obtain from common NILM systems. This article proposes an inexpensive and easy-to-install monitoring system capable of providing information on the status of the various loads powered by an electrical system. The proposed technique involves the processing of the traces obtained by a measurement system based on Sweep Frequency Response Analysis (SFRA) through a Support Vector Machine (SVM) algorithm. The overall accuracy of the system in its final configuration is between 94% and 99%, depending on the amount of data used for training. Numerous tests have been conducted on many loads with different characteristics. The positive results obtained are illustrated and commented on.
Asunto(s)
Algoritmos , Aprendizaje Automático , Máquina de Vectores de Soporte , ElectricidadRESUMEN
Asynchronous motors represent a large percentage of motors used in the electrical industry. Suitable predictive maintenance techniques are strongly required when these motors are critical in their operations. Continuous non-invasive monitoring techniques can be investigated to avoid the disconnection of the motors under test and service interruption. This paper proposes an innovative predictive monitoring system based on the online sweep frequency response analysis (SFRA) technique. The testing system applies variable frequency sinusoidal signals to the motors and then acquires and processes the applied and response signals in the frequency domain. In the literature, SFRA has been applied to power transformers and electric motors switched off and disconnected from the main grid. The approach described in this work is innovative. Coupling circuits allow for the injection and acquisition of the signals, while grids feed the motors. A comparison between the transfer functions (TFs) of healthy motors and those with slight damage was performed with a batch of 1.5 kW, four-pole induction motors to investigate the technique's performance. The results show that the online SFRA could be of interest for monitoring induction motors' health conditions, especially for mission-critical and safety-critical applications. The overall cost of the whole testing system, including the coupling filters and cables, is less than EUR 400.
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Three-phase induction motors are widely diffused in the industrial environment. Many times, the rated power of three-phase induction motors is not properly chosen causing incorrect operating conditions from an energetic point of view. Monitoring the mechanical dimension of a new motor is helpful, should an existing motor need to be replaced. This paper presents an IoT sensors network for monitoring the mechanical power produced by three-phase induction motors, adopting an indirect measuring method. The proposed technique can be easily adopted to monitor the mechanical power using only one line of current transducer, reducing the cost of the monitoring system. The proposed indirect measurement technique has been implemented on a low-cost IoT system, based on a Photon Particle SoC. The results show that the proposed IoT system can estimate the mechanical power with a relative error of within 8%.
RESUMEN
AIMS: Recently, it has been proposed that obstructive sleep apnea syndrome (OSAS) is characterized by an imbalance in autonomic nervous tone. Pupil size has been considered a valid test for studying the autonomic nervous system (ANS). Pupillometry is a simple and non-invasive tool to assess the size and dynamics of the pupil. The purpose of this study was to evaluate, by pupillometry, the hypothesis that subjects with OSAS present ANS dysregulation. METHODS: The study group included 10 males aged between 40 and 50 years with polysomnographic diagnoses of mild OSAS. The control group included 10 males with similar ages with an apnea-hypopnea index (AHI) of less than 5, after polysomnography. Pupillometry was performed by digital infrared pupillometer (25 frame/s). Recordings were processed to measure the area of the pupil frame by frame. The subjects underwent four subsequent recordings: infrared light at rest mandible position (RP); infrared light at forced habitual occlusion (FHO); yellow-green light at RP; and yellow-green light at FHO. According to literature, linear and non-linear information was extracted from the recordings. RESULTS: As expected, the two groups did not differ statistically in age and body mass index (BMI), while there was a significant difference in the AHI. In the within-group comparison of pupil size, there were significant differences between RP and FHO under infrared conditions in the control group. There was a significant difference in the determinism percentage (Det%) in the RP infrared condition between the control and OSAS groups. CONCLUSIONS: The results of the current study confirm ANS dysregulation in OSAS patients and provide a new possible strategy for studying this pathology by using pupillometry through linear and non-linear mathematical models.