RESUMEN

Multi-phase systems are becoming more popular for applications requiring high power and precise motor control, even if single-phase AC power is still frequently utilized in households and some enterprises. While both systems have benefits over single-phase, there are trade-offs associated with each. Because of its balanced operation and effective power transfer, the three-phase (3-Φ) system is the most widely used multi-phase system. Nevertheless, different phase values can be investigated for particular applications where reducing torque ripple and harmonic content is essential. Using odd numbers of phases (such as 5-Φ) that are not multiples of three is one method. This design has the ability to reduce torque ripple by producing a more balanced magnetic field as compared with even-numbered phases. But adding more phases also makes the system design and control circuitry more complex. Systems with five phases (5-Φ) provide a compromise between performance and complexity. Applications such as electric ship propulsion, rocket satellites, and traction systems may benefit from their use. Nevertheless, choosing a multi-phase system necessitates carefully weighing the requirements unique to each application, taking into account elements like cost, power transmission, control complexity, and efficiency. The increasing popularity of electric vehicles and renewable energy technologies has led to the need for inverters in current electric applications. Conventional inverters provide square wave outputs, which cause the drive system to become noisy and cause harmonics. Multi-phase multilevel inverters can be used to enhance inverter functioning and produce an improved sinusoidal output. This study focuses on an induction motor drive powered by a five-phase multilevel cascaded H-Bridge inverter. With less torque and current ripples in the motor rotor, the power conversion harmonics are reduced and the switching components of the inverter are under less stress. However, in comparison to traditional inverters, it does require a greater number of legs. Because the switches needed for the cascaded H-Bridge inverter are less expensive in five-phase systems, they are favoured over higher phase orders. Furthermore, the suggested inverter removes 5th order harmonics, something that is not possible with traditional inverters. A five-phase induction motor appropriate for variable speed driving applications is also suggested by this research. Lastly, utilizing pulse width modulation (PWM) converters and an FPGA controller, an experimental study is carried out to assess the dynamic performance of the suggested induction motor drive. Particular attention is paid to the In-Phase Opposition Disposition (IPD) PWM technique.

RESUMEN

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.

RESUMEN

Three-phase induction motors are widely used in various industrial sectors and are responsible for a significant portion of the total electrical energy consumed. To ensure their efficient operation, it is necessary to apply control systems with specific algorithms able to estimate rotation speed accurately and with an adequate response time. However, the angular speed sensors used in induction motors are generally expensive and unreliable, and they may be unsuitable for use in hostile environments. This paper presents an algorithm for speed estimation in three-phase induction motors using the chaotic variable of maximum density. The technique used in this work analyzes the current signals from the motor power supply without invasive sensors on its structure. The results show that speed estimation is achieved with a response time lower than that obtained by classical techniques based on the Fourier Transform. This technique allows for the provision of motor shaft speed values when operated under variable load.

RESUMEN

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%.

RESUMEN

Induction motors have been widely used in industry, agriculture, transportation, national defense engineering, etc. Defects of the motors will not only cause the abnormal operation of production equipment but also cause the motor to run in a state of low energy efficiency before evolving into a fault shutdown. The former may lead to the suspension of the production process, while the latter may lead to additional energy loss. This paper studies a fuzzy rule-based expert system for this purpose and focuses on the analysis of many knowledge representation methods and reasoning techniques. The rotator fault of induction motors is analyzed and diagnosed by using this knowledge, and the diagnosis result is displayed. The simulation model can effectively simulate the broken rotator fault by changing the resistance value of the equivalent rotor winding. And the influence of the broken rotor bar fault on the motors is described, which provides a basis for the fault characteristics analysis. The simulation results show that the proposed method can realize fast fault diagnosis for rotators of induction motors.

RESUMEN

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.

RESUMEN

The paper addresses the effects of high-order space harmonics on the steady-state performance of five-phase induction machines operating under unbalance. We show that the airgap harmonic fields with orders higher than three, although usually disregarded, can produce a significant increase in the torque pulsation and in the Joule losses. We propose a model based on symmetric components which is used to demonstrate that the field produced by each rotor harmonic current is related to two stator sequence currents. Further, the proposed model allows determining additional losses and harmonic torques produced by airgap harmonic fields with orders higher than three, which up to now have not been addressed elsewhere. As a case study, we applied the model to two five-phase induction machines with different designs and assessed the influence of high space harmonics under operation at steady state with one open phase in the stator. The model has been validated by comparing analytical results with results obtained through finite element analysis. Finally, the model validation was also based on experimental results obtained from tests with two prototypes under many different conditions.

Asunto(s)

RESUMEN

Motor faults, especially mechanical faults, reflect eminently faint characteristic amplitudes in the stator current. In order to solve the issue of the motor current lacking effective and direct signal representation, this paper introduces a visual fault detection method for an induction motor based on zero-sequence current and an improved symmetric dot matrix pattern. Empirical mode decomposition (EMD) is used to eliminate the power frequency in the zero-sequence current derived from the original signal. A local symmetrized dot pattern (LSDP) method is proposed to solve the adaptive problem of classical symmetric lattice patterns with outliers. The LSDP approach maps the zero-sequence current to the ultimate coordinate and obtains a more intuitive two-dimensional image representation than the time-frequency image. Kernel density estimation (KDE) is used to complete the information about the density distribution of the image further to enhance the visual difference between the normal and fault samples. This method mines fault features in the current signals, which avoids the need to deploy additional sensors to collect vibration signals. The test results show that the fault detection accuracy of the LSDP can reach 96.85%, indicating that two-dimensional image representation can be effectively applied to current-based motor fault detection.

RESUMEN

Multiple fault identification in induction motors is essential in industrial processes due to the high costs that unexpected failures can cause. In real cases, the motor could present multiple faults, influencing systems that classify isolated failures. This paper presents a novel methodology for detecting multiple motor faults based on quaternion signal analysis (QSA). This method couples the measured signals from the motor current and the triaxial accelerometer mounted on the induction motor chassis to the quaternion coefficients. The QSA calculates the quaternion rotation and applies statistics such as mean, variance, kurtosis, skewness, standard deviation, root mean square, and shape factor to obtain their features. After that, four classification algorithms are applied to predict motor states. The results of the QSA method are validated for ten classes: four single classes (healthy condition, unbalanced pulley, bearing fault, and half-broken bar) and six combined classes. The proposed method achieves high accuracy and performance compared to similar works in the state of the art.

Asunto(s)

RESUMEN

This paper studies an observer-based neural network position tracking control scheme for induction motors system operating under a field-oriented control scheme with the problem of stochastic disturbance. Firstly, the angular velocity is estimated by the constructed reduced-order observer. Then, the nonlinear functions are approximated by the neural networks and the stochastic Lyapunov functions are chosen to analyze the stability of the system. Besides, the "complexity of computation" existed in traditional backstepping control is solved by using the dynamic surface control technique. At last, the results of the comparison simulation experiments show that the proposed control scheme can reduce the influence of stochastic disturbance, and have faster tracking speed smaller tracking error. The designed observer can estimate the signals effectively.

RESUMEN

Cyclostationary analysis has been strongly recognized as an effective demodulation tool in identifying fault features of rotating machinery based on vibration signature analysis. This study improves two current mature cyclostationary approaches, cyclic modulation spectrum (CMS) and fast spectral correlation (Fast-SC), combined with the novel frequency-domain application of Teager Kaiser energy operator (TKEO). They can enhance fault feature identification with the lower computational burden. Firstly, the raw vibration signal is transformed into the time-frequency domain through the short-time Fourier transform (STFT) to realize the conversion of the multi-carrier signal to a multiple signal-carrier signal. Secondly, the TKEO is utilized to enhance the fault feature by taking full advantage of demodulating the mono-component. Finally, the spectral coherence and enhanced envelope spectrum (EES) are calculated to effectively exhibit fault features. The superiority of the proposed methods is successfully validated by the simulation study and diagnosing the broken rotor bar (BRB) and bearing outer race faults of induction motors (IMs) under various operating conditions.

RESUMEN

Induction motors (IM) are key components of any industrial process; hence, it is important to carry out continuous monitoring to detect incipient faults in them in order to avoid interruptions on production lines. Broken rotor bars (BRBs), which are among the most regular and most complex to detect faults, have attracted the attention of many researchers, who are searching for reliable methods to recognize this condition with high certainty. Most proposed techniques in the literature are applied during the IM startup transient, making it necessary to develop more efficient fault detection techniques able to carry out fault identification during the IM steady state. In this work, a novel methodology based on motor current signal analysis and contrast estimation is introduced for BRB detection. It is worth noting that contrast has mainly been used in image processing for analyzing texture, and, to the best of the authors' knowledge, it has never been used for diagnosing the operative condition of an induction motor. Experimental results from applying the approach put forward validate Unser and Tamura contrast definitions as useful indicators for identifying and classifying an IM operational condition as healthy, one broken bar (1BB), or two broken bars (2BB), with high certainty during its steady state.

Asunto(s)

RESUMEN

A novel framework of model-based fault detection and identification (MFDI) for induction motor (IM)-driven rotating machinery (RM) is proposed in this study. A data-driven subspace identification (SID) algorithm is employed to obtain the IM state-space model from the voltage and current signals in a quasi-steady-state condition. This study aims to improve the frequency-domain fault detection and identification (FDI) by replacing the current signal with a residual signal where a thresholding method is applied to the residual signal. Through the residual spectrum and threshold comparison, a binary decision is made to find fault signatures in the spectrum. The statistical Q-function is used to generate the fault frequency band to distinguish between the fault signature and the noise signature. The experiment in this study is performed on a wastewater pump in an existing industrial facility to verify the proposed FDI. Two faulty conditions with mathematically known and mathematically unknown faulty signatures are experimented with and diagnosed. The study results present that the residual spectrum demonstrated to be more sensitive to fault signatures compare to the current spectrum. The proposed FDI has successfully shown to identify the fault signatures even for the mathematically unknown faulty signatures.

RESUMEN

Sensorless speed estimation has been extensively studied for its use in control schemes. Nevertheless, it is also a key step when applying Motor Current Signature Analysis to induction motor diagnosis: accurate speed estimation is vital to locate fault harmonics, and prevent false positives and false negatives, as shown at the beginning of the paper through a real industrial case. Unfortunately, existing sensorless speed estimation techniques either do not provide enough precision for this purpose or have limited applicability. Currently, this is preventing Industry 4.0 from having a precise and automatic system to monitor the motor condition. Despite its importance, there is no research published reviewing this topic. To fill this gap, this paper investigates, from both theoretical background and an industrial application perspective, the reasons behind these problems. Therefore, the families of sensorless speed estimation techniques, mainly conceived for sensorless control, are here reviewed and thoroughly analyzed from the perspective of their use for diagnosis. Moreover, the algorithms implemented in the two leading commercial diagnostic devices are analyzed using real examples from a database of industrial measurements belonging to 79 induction motors. The analysis and discussion through the paper are synthesized to summarize the lacks and weaknesses of the industry application of these methods, which helps to highlight the open problems, challenges and research prospects, showing the direction in which research efforts have to be made to solve this important problem.

Asunto(s)

RESUMEN

Induction motors are broadly used as drivers of a large variety of industrial equipment. A proper measurement of the motor rotation speed is essential to monitor the performance of most industrial drives. As an example, the measurement of rotor speed is a simple and broadly used industrial method to estimate the motor's efficiency or mechanical load. In this work, a new low-cost non-intrusive method for in-field motor speed measurement, based on the spectral analysis of the motor audible noise, is proposed. The motor noise is acquired using a smartphone and processed by a MATLAB-based routine, which determines the rotation speed by identifying the rotor shaft mechanical frequency from the harmonic spectrum of the noise signal. This work intends to test the hypothesis that the emitted motor noise, like mechanical vibrations, contains a frequency component due to the rotation speed which, to the authors' knowledge, has thus far been disregarded for the purpose of speed measurement. The experimental results of a variety of tests, from no load to full load, including the use of a frequency converter, found that relative errors on the speed estimation were always lower than 0.151%. These findings proved the versatility, robustness, and accuracy of the proposed method.

Asunto(s)

RESUMEN

The reliable and cost-effective condition monitoring of the bearings installed in water pumps is a real challenge in the industry. This paper presents a novel strong feature selection and extraction algorithm (SFSEA) to extract fault-related features from the instantaneous power spectrum (IPS). The three features extracted from the IPS using the SFSEA are fed to an extreme gradient boosting (XBG) classifier to reliably detect and classify the minor bearing faults. The experiments performed on a lab-scale test setup demonstrated classification accuracy up to 100%, which is better than the previously reported fault classification accuracies and indicates the effectiveness of the proposed method.

Asunto(s)

RESUMEN

The problem of detecting and quantifying bar breakage harmonics in inverter-fed induction motors has not been solved by the time-frequency transforms present in the technical literature. The paper proposes a new transform, called dragon transform, to solve this problem. The dragon atoms are defined with shapes perfectly adapted to the harmonic trajectories in the time-frequency plane, no matter how complex they are, enabling the precise tracing of the harmonics to be detected. A quantification method is also proposed, which obtains for the first time in the technical literature, the time evolutions of the harmonic amplitudes during a complex transient such as the start-up and the steady state of an inverter-fed motor. The transform performance is validated testing the induction motor under different load levels.

RESUMEN

The data presented in this article was used to assess and compare the most important methods used to estimate the efficiency during the operation of induction motors at different loads and power supply conditions. The experiment was developed in a test bench including a three-phase induction motor of 1.1 kW (De Lorenzo DL 1021). In addition, an adjustable voltage source, a variable-frequency drive, a resistor, and a magnetic powder brake control unit to regulate the load were used during the experiments. A power quality and energy analyzer (Fluke 435 series 6) was used to measure the electric variables during the experiments. Moreover, for the mechanical measures, the sensors of the brake control unit (De Lorenzo DL 1054TT) and a magnetic powder brake (De Lorenzo DL 1019P) were used. In total, 11 load factors were measured at different operation conditions, including balanced sinusoidal voltage, balanced harmonic voltage, unbalanced sinusoidal voltage and unbalanced harmonic voltage. A total of 10 measures were taken for each load factor at each operation condition. The data presented in this paper can be useful in the development and evaluation of new efficiency estimation methods for induction motors, considering different operation conditions and load factors. Moreover, it can serve to assess the impact of the energy quality on the efficiency of induction motors. The data is related to the manuscript "Assessment of the energy efficiency estimation methods on induction motors considering real-time monitoring" [1].

RESUMEN

Efficient fault diagnosis of electrical and mechanical anomalies in induction motors (IMs) is challenging but necessary to ensure safety and economical operation in industries. Research has shown that bearing faults are the most frequently occurring faults in IMs. The vibration signals carry rich information about bearing health conditions and are commonly utilized for fault diagnosis in bearings. However, collecting these signals is expensive and sometimes impractical because it requires the use of external sensors. The external sensors demand enough space and are difficult to install in inaccessible sites. To overcome these disadvantages, motor current signal-based bearing fault diagnosis methods offer an attractive solution. As such, this paper proposes a hybrid motor-current data-driven approach that utilizes statistical features, genetic algorithm (GA) and machine learning models for bearing fault diagnosis. First, the statistical features are extracted from the motor current signals. Second, the GA is utilized to reduce the number of features and select the most important ones from the feature database. Finally, three different classification algorithms namely KNN, decision tree, and random forest, are trained and tested using these features in order to evaluate the bearing faults. This combination of techniques increases the accuracy and reduces the computational complexity. The experimental results show that the three classifiers achieve an accuracy of more than 97%. In addition, the evaluation parameters such as precision, F1-score, sensitivity, and specificity show better performance. Finally, to validate the efficiency of the proposed model, it is compared with some recently adopted techniques. The comparison results demonstrate that the suggested technique is promising for diagnosis of IM bearing faults.

RESUMEN

This paper presents a simple method to estimate the steady-state performance of three-phase induction motors using only measurements of stator voltages and currents, acquired during a no-load startup test and without speed acquisition. The procedure consists of two steps: (a) firstly, the parameters of the single-cage model are estimated while considering the rotor resistance and the leakage inductances variable with the slip; and (b) secondly, the steady-state performance is estimated from the equivalent circuit using the estimated parameters. The proposed method is experimentally validated through tests involving 229 medium-power motors with power ranging from 22 to 90 kW; the estimated performance is then compared with measurements obtained through standardized laboratory tests. In addition, we also estimated the performance using a known double-cage model with constant parameters and compare the results with those obtained with the proposed model. We demonstrate that the proposed method provides accurate estimates for the main performance characteristics, such as efficiency and rated torque, which usually cannot be correctly assessed without direct measurements. The method makes it possible to estimate the performance of medium-power induction motors within acceptable accuracy through quick and low-cost tests requiring few sensors, making it a potential substitute for expensive and labor-intensive laboratory tests.