Machine Learning Based Method for Impedance Estimation and Unbalance Supply Voltage Detection in Induction Motors.

Induction motors (IMs) are widely used in industrial applications due to their advantages over other motor types. However, the efficiency and lifespan of IMs can be significantly impacted by operating conditions, especially Unbalanced Supply Voltages (USV), which are common in industrial plants. Detecting and accurately assessing the severity of USV in real-time is crucial to prevent major breakdowns and enhance reliability and safety in industrial facilities. This paper presented a reliable method for precise online detection of USV by monitoring a relevant indicator, denominated by negative voltage factor (NVF), which, in turn, is obtained using the voltage symmetrical components. On the other hand, impedance estimation proves to be fundamental to understand the behavior of motors and identify possible problems. IM impedance affects its performance, namely torque, power factor and efficiency. Furthermore, as the presence of faults or abnormalities is manifested by the modification of the IM impedance, its estimation is particularly useful in this context. This paper proposed two machine learning (ML) models, the first one estimated the IM stator phase impedance, and the second one detected USV conditions. Therefore, the first ML model was capable of estimating the IM phases impedances using just the phase currents with no need for extra sensors, as the currents were used to control the IM. The second ML model required both phase currents and voltages to estimate NVF. The proposed approach used a combination of a Regressor Decision Tree (DTR) model with the Short Time Least Squares Prony (STLSP) technique. The STLSP algorithm was used to create the datasets that will be used in the training and testing phase of the DTR model, being crucial in the creation of both features and targets. After the training phase, the STLSP technique was again used on completely new data to obtain the DTR model inputs, from which the ML models can estimate desired physical quantities (phases impedance or NVF).

Online Monitoring of Sensor Calibration Status to Support Condition-Based Maintenance.

Condition-Based Maintenance (CBM), based on sensors, can only be reliable if the data used to extract information are also reliable. Industrial metrology plays a major role in ensuring the quality of the data collected by the sensors. To guarantee that the values collected by the sensors are reliable, it is necessary to have metrological traceability made by successive calibrations from higher standards to the sensors used in the factories. To ensure the reliability of the data, a calibration strategy must be put in place. Usually, sensors are only calibrated on a periodic basis; so, they often go for calibration without it being necessary or collect data inaccurately. In addition, the sensors are checked often, increasing the need for manpower, and sensor errors are frequently overlooked when the redundant sensor has a drift in the same direction. It is necessary to acquire a calibration strategy based on the sensor condition. Through online monitoring of sensor calibration status (OLM), it is possible to perform calibrations only when it is really necessary. To reach this end, this paper aims to provide a strategy to classify the health status of the production equipment and of the reading equipment that uses the same dataset. A measurement signal from four sensors was simulated, for which Artificial Intelligence and Machine Learning with unsupervised algorithms were used. This paper demonstrates how, through the same dataset, it is possible to obtain distinct information. Because of this, we have a very important feature creation process, followed by Principal Component Analysis (PCA), K-means clustering, and classification based on Hidden Markov Models (HMM). Through three hidden states of the HMM, which represent the health states of the production equipment, we will first detect, through correlations, the features of its status. After that, an HMM filter is used to eliminate those errors from the original signal. Next, an equal methodology is conducted for each sensor individually and using statistical features in the time domain where we can obtain, through HMM, the failures of each sensor.

Speed Estimation of Six-Phase Induction Motors, Using the Rotor Slot Harmonics.

Multiphase machines have recently been promoted as a viable alternative to traditional three-phase machines. Most experts are looking for strategies to estimate the rotation speed of such complex systems, since speed data are required for high-performance control purposes. Traditionally, electromechanical sensors were used to detect the rotor speed of electric motors. These devices are extremely accurate, but they are also delicate and costly to deploy. New speed estimating algorithms must be created for these situations. This paper looks at how to estimate rotor speed in symmetrical six-phase induction motors (IMs) using a novel strategy for rotor speed estimation based on the Short Time Fourier Transform (STFT) method. The technique is based on tracking the frequencies of the rotor slot harmonics (RSH) seen in most squirrel-cage IM stator currents, thus assuring a broad range of applications. To monitor the RSH, the STFT employs a sliding window to perform the discrete Fourier transform technique, making it more suitable for online use with noisy and nonstationary signals. Experimental tests demonstrate the effectiveness of the suggested approach.

High-performance vector control without AC phase current sensors for induction motor drives: Simulation and real-time implementation.

The main purpose of this paper is to develop a high-level performance, and low-cost current sensorless control strategy for Induction Motor (IM) drives. Therefore, a new phase-current regeneration method, for current sensorless vector control in induction motor drives is introduced. The idea is based on the reconfiguration of the Luenberger adaptive observer for currents estimation, using the information provided by the dc-link voltage sensor. The basis of the proposed control and the theoretical study of the modified adaptive observer are presented. Several simulation and experimental tests were performed on an induction motor of 1.1 kW working under different operating conditions. The obtained results prove and testify the relevance, workability, and practicability of the suggested currents sensorless vector control strategy.

Batch tests on mineral deposit formation due to co-mingling of leachates derived from municipal solid wastes and waste-to-energy combustion residues.

Deposit formation in leachate collection systems can be problematic for landfill operations. Deposits from municipal solid waste (MSW) derived leachates are impacted by microbial activity and biofilm development, whereas leachates generated from co-disposal of MSW with combustion residues (CR) from waste-to-energy (WTE) facilities and other mineral-rich waste materials are more prone to forming dense mineral deposits dominated by calcium carbonate. In this study, leachates from laboratory lysimeters containing either WTE-CR or shredded MSW were mixed at different volumetric ratios. The mixed leachates were incubated for 5 weeks in batch tests to evaluate the potential for formation of precipitates. Although mineral precipitates have been reported to form in landfills with no co-disposal practices, in this study mineral precipitates did not form in either the WTE-CR derived leachate or the MSW derived leachate, but formed in all leachate mixtures. Mineral precipitates consisted of calcium carbonate particles, with the highest yield from a 1:1 combination of the WTE-CR derived leachate mixed with the MSW derived leachate. The introduction of gaseous carbon dioxide or air into WTE-CR derived leachate resulted in the production of particles of similar chemical composition but different morphology. Operation of landfills to prevent co-mingling of mineral-rich leachates with microbially active leachates and/or to control leachate exposure to sources of carbon dioxide may help to prevent this type of precipitate formation in leachate collection systems.

Batch test assessment of waste-to-energy combustion residues impacts on precipitate formation in landfill leachate collection systems.

Disposal practices for bottom ash and fly ash from waste-to-energy (WTE) facilities include emplacement in ash monofills or co-disposal with municipal solid waste (MSW) and residues from water and wastewater treatment facilities. In some cases, WTE residues are used as daily cover in landfills that receive MSW. A recurring problem in many landfills is the development of calcium-based precipitates in leachate collection systems. Although MSW contains varying levels of calcium, WTE residues and treatment plant sludges have the potential to contribute concentrated sources of leachable minerals into landfill leachates. This study was conducted to evaluate the leachability of calcium and other minerals from residues generated by WTE combustion using residues obtained from three WTE facilities in Florida (two mass-burn and one refuse-derived fuel). Leaching potential was quantified as a function of contact time and liquid-to-solid ratios with batch tests and longer-term leaching tests using laboratory lysimeters to simulate an ash monofill containing fly ash and bottom ash. The leachate generated as a result of these tests had total dissolved solid (TDS) levels ranging from 5 to 320 mg TDS/g ash. Calcium was a major contributor to the TDS values, contributing from 20 to 105 g calcium/kg ash. Fly ash was a major contributor of leachable calcium. Precipitate formation in leachates from WTE combustion residues could be induced by adding mineral acids or through gas dissolution (carbon dioxide or air). Stabilization of residual calcium in fly ashes that are landfilled and/or the use of less leachable neutralization reagents during processing of acidic gases from WTE facilities could help to decrease the calcium levels in leachates and help to prevent precipitate formation in leachate collection systems.

Lysimeter comparison of the role of waste characteristics in the formation of mineral deposits in leachate drainage systems.

A common operational problem in leachate collection systems is clogging due to the formation of deposits within pore spaces and collection pipes. The role of co-disposal of municipal solid waste (MSW) and combustion residues from waste-to-energy (WTE) facilities in clogging is evaluated in this paper. Five parallel lysimeters were operated in monofill or co-disposal mode using MSW, WTE combustion residues, and water/wastewater treatment byproducts. Leachate was applied to each lysimeter to simulate sequential flooding and draining and leachates were characterized over a 7-month period. Waste composition and the presence/absence of biological activity influenced the redox potential, pH, and alkalinity, which impacted the rate and extent of biological degradation and chemical solubility. Calcium carbonate was identified as the most abundant chemical precipitate. Leachates from ash monofills were highly alkaline (pH > 11) and had higher ionic strength due to relatively higher levels of calcium and other minerals, while carbonate levels were limited due to the lack of biological activity. The MSW monofill generated leachates with high levels of biological activity, lower concentrations of calcium, and a rich carbonate system. Co-disposal of MSW, combustion and treatment process residues generated leachates that were not limited in either calcium or carbonate, creating ideal conditions for formation of precipitates.