Stray Flux Research Articles

Diagnosis of Multiple Defects Within Large Hydroelectric Generator Using Stray Flux and Air Gap (Distance and Flux) Measurements

Diagnosis of Multiple Defects Within Large Hydroelectric Generator Using Stray Flux and Air Gap (Distance and Flux) Measurements

Start-Up and Steady-State Regimes Automatic Separation in Induction Motors by Means of Short-Time Statistics

Induction motors are widely used machines in a variety of applications as primary components for generating rotary motion. This is mainly due to their high efficiency, robustness, and ease of control. Despite their high robustness, these machines can experience failures throughout their lifespan due to various mechanical, electrical, and environmental factors. To prevent irreversible failures and all the implications and costs associated with breakdowns, various methodologies have been developed over the years. Many of these methodologies have focused on analyzing various physical quantities, either during start-up transients or during steady-state operations. This involves the use of specific techniques depending on the focus of the methodology (start-up transients or steady-state) to obtain optimal results. In this regard, it is of great importance to develop methods capable of separating and detecting the start-up transient of the motor from the steady state. This will enable the development of automatic diagnostic methodologies focused on the specific operating state of the motor. This paper proposes a methodology for the automatic detection of start-up transients in induction motors by using magnetic stray flux signals and processing by means of statistical indicators in time-sliding windows, the calculation of variances with a proposed method, and obtaining optimal values for the design parameters by using a Particle Swarm Optimization (PSO). The results obtained demonstrate the effectiveness of the proposed method for the start-up and steady-state regimes automatic separation, which is validated on a 0.746 kW induction motor supplied by a variable frequency drive (VFD).

Current and Stray Flux Combined Analysis for Sparking Detection in DC Motors/Generators Using Shannon Entropy.

Brushed DC motors and generators (DCMs) are extensively used in various industrial applications, including the automotive industry, where they are critical for electric vehicles (EVs) due to their high torque, power, and efficiency. Despite their advantages, DCMs are prone to premature failure due to sparking between brushes and commutators, which can lead to significant economic losses. This study proposes two approaches for determining the temporal and frequency evolution of Shannon entropy in armature current and stray flux signals. One approach indirectly achieves this through prior analysis using the Short-Time Fourier Transform (STFT), while the other applies the Stockwell Transform (S-Transform) directly. Experimental results show that increased sparking activity generates significant low-frequency harmonics, which are more pronounced compared to mid and high-frequency ranges, leading to a substantial rise in system entropy. This finding enables the introduction of fault-severity indicators or Key Performance Indicators (KPIs) that relate the current condition of commutation quality to a baseline established under healthy conditions. The proposed technique can be used as a predictive maintenance tool to detect and assess sparking phenomena in DCMs, providing early warnings of component failure and performance degradation, thereby enhancing the reliability and availability of these machines.

Electromagnetic Signal Analysis for Electrical Fault Diagnosis in Synchronous Generators

This study investigates the enhancement of fault diagnostics in synchronous generators by incorporating electromagnetic signal analysis with conventional diagnostic methodologies. The critical role of synchronous generators in maintaining power system stability and efficiency in industrial and power plant environments is underscored. Finite element modeling (FEM) is employed to simulate various fault conditions, such as stator and rotor winding faults. This study proposes a method that integrates stator current and stray magnetic flux analysis to identify five distinct types of short-circuit faults in stator and rotor windings, thereby enhancing the diagnostic capabilities for electrical faults in synchronous generators. This approach successfully identifies these electrical faults using non-invasive methods, offering a cost-effective solution that enhances fault detection. These findings are based on simulation results and serve as a preliminary stage for further validation through experimental studies. This integration is crucial for the development of efficient diagnostic systems that are capable of adapting to complex fault patterns, reducing human intervention, and streamlining maintenance operations, thus improving the reliability of synchronous generators globally.

Detection of Contamination and Failure in the Outer Race on Ceramic, Metallic, and Hybrid Bearings through AI Using Magnetic Flux and Current

Bearings are one of the most essential elements in an induction motor, and they are built with different materials and constructions according to their application. These components are usually one of the most failure-prone parts of an electric motor, so correct and accurate measurements, instrumentation, and processing methods are required to prevent and detect the presence of different failures. This work develops a methodology based on the fusion of current and magnetic stray flux signals, calculation of statistical and non-statistical indicators, genetic algorithms (GAs), linear discriminant analysis (LDA), and neural networks. The proposed approach achieves a diagnostic effectiveness of 99.8% for detecting various damages in the outer race at 50 Hz frequency and 96.6% at 60 Hz. It also demonstrates 99.8% effectiveness for detecting damages in the presence of contaminants in lubrication at 50 Hz and 97% at 60 Hz. These results apply across metallic, ceramic, and hybrid bearings.

Broken Rotor Bar Detection Based on Steady-State Stray Flux Signals Using Triaxial Sensor with Random Positioning.

This paper investigates the detection of broken rotor bar in squirrel cage induction motors using a novel approach of randomly positioning a triaxial sensor over the motor surface. This study is conducted on two motors under laboratory conditions, where one motor is kept in a healthy state, and the other is subjected to a broken rotor bar (BRB) fault. The induced electromotive force of the triaxial coils, recorded over ten days with 100 measurements per day, is statistically analyzed. Normality tests and graphical interpretation methods are used to evaluate the data distribution. Parametric and non-parametric approaches are used to analyze the data. Both approaches show that the measurement method is valid and consistent over time and statistically distinguishes healthy motors from those with BRB defects when a reference or threshold value is specified. While the comparison between healthy motors shows a discrepancy, the quantitative analysis shows a smaller estimated difference in mean values between healthy motors than comparing healthy and BRB motors.

Handling Stray Flux Signals for Stator Turn Insulation Degradation Monitoring

Handling Stray Flux Signals for Stator Turn Insulation Degradation Monitoring

Automatic Methodology for Multiple Fault Detection in Induction Motor Under Periodic Low-Frequency Fluctuating Load Based on Stray Flux Signals

Automatic Methodology for Multiple Fault Detection in Induction Motor Under Periodic Low-Frequency Fluctuating Load Based on Stray Flux Signals

Eccentricity fault detection in surface-mounted permanent magnet synchronous motors by analytical prediction, FEM evaluation, and experimental magnetic sensing of the stray flux density

The paper discusses the impact of rotor eccentricity in permanent magnet synchronous motors (PMSMs), which leads to increased motor vibration and unbalanced magnetic pull, resulting in accelerated aging of motor parts. Undetected eccentricity in the motor can exacerbate these issues and lead to a higher risk of stator-rotor contact and forced outages. The study proposes a technique for detecting eccentricity faults using an analog magnetic field sensor outside the motor, without requiring the sensor to be installed within the motor. The proposed technique was evaluated using analytical calculations, 2-D finite elements, and experimental results. The proposed method was evaluated in different scenarios and it was found to be capable of providing detection and identification of parallel and inclined eccentricity. The size of the magnetic field at a specific point in the outer space of the motor was estimated to be 26μT, 30μT, and 43μT, respectively, through analytical modeling in healthy conditions, 10% and 30% eccentricity, and it was confirmed by experimental testing. Since the proposed method does not require installing sensors or search coils within the motor, such a method is more appropriate for practical applications and improving the cost-performance in online condition monitoring. In addition, with some specific modifications, the suggested concept becomes useful for fault detection in other rotating machinery types.

Automatic Classification of Field Winding Faults in Synchronous Motors Based on Bicoherence Image Segmentation and Higher Order Statistics of Stray Flux Signals

In this work, the application of the bicoherence (a squared normalized version of the bispectrum) of the stray flux signal is proposed as a way of detecting faults in the field winding of synchronous motors. These signals are analyzed both under the starting and at steady state regime. Likewise, two quantitative indicators are proposed, the first one based on the maximum values of the asymmetry and the kurtosis of the bicoherence matrix obtained from the flux signals and the second one relying on an algorithm based on the bicoherence image segmentation of the obtained pattern for each analyzed state. The results are analyzed through a comparative study for the two considered motor regimes, obtaining satisfactory results that sustain the potential application of the proposed methodology for the automatic field winding fault detection in real applications.

Physical Variable Measurement Techniques for Fault Detection in Electric Motors

Induction motors are widely used worldwide for domestic and industrial applications. Fault detection and classification techniques based on signal analysis have increased in popularity due to the growing use of induction motors in new technologies such as electric vehicles, automatic control, maintenance systems, and the inclusion of renewable energy sources in electrical systems, among others. Hence, monitoring, fault detection, and classification are topics of interest for researchers, given that the presence of a fault can lead to catastrophic consequences concerning technical and financial aspects. To detect a fault in an induction motor, several techniques based on different physical variables, such as vibrations, current signals, stray flux, and thermographic images, have been studied. This paper reviews recent investigations into physical variables, instruments, and techniques used in the analysis of faults in induction motors, aiming to provide an overview on the pros and cons of using a certain type of physical variable for fault detection. A discussion about the detection accuracy and complexity of the signals analysis is presented, comparing the results reported in recent years. This work finds that current and vibration are the most popular signals employed to detect faults in induction motors. However, stray flux signal analysis is presented as a promising alternative to detect faults under certain operating conditions where other methods, such as current analysis, may fail.

Condition Monitoring of Rotating Diodes in Synchronous Machines Through the Exciter Stray Flux Analysis

Brushless excitation systems have many advantages mainly related to the low maintenance. A failure in the rotating diodes leads to an increase in the exciter field current and an unbalance in the currents supplied by the exciter armature. This produces additional losses in the exciter and, in some cases, severe damage in the machine if operated for a long time under fault conditions. The supervision of the condition of the rotating diodes is, therefore, essential for the protection of the exciter and the machine. In this paper, monitoring of the diodes is carried out via the analysis of the exciter´s stray flux. Numerous experimental tests have been performed on a synchronous machine with axial and radial flux measurements under healthy condition, under one open diode fault and finally under a shorted diode. The experimental results prove that a failure in the diodes can be reliably detected due to the axial and radial stray flux signatures which significantly increase in comparison to the healthy condition.

FPGA-Flux Proprietary System for Online Detection of Outer Race Faults in Bearings

Online fault detection in industrial machinery, such as induction motors or their components (e.g., bearings), continues to be a priority. Most commercial equipment provides general measurements and not a diagnosis. On the other hand, commonly, research works that focus on fault detection are tested offline or over processors that do not comply with an online diagnosis. In this sense, the present work proposes a system based on a proprietary field programmable gate array (FPGA) platform with several developed intellectual property cores (IPcores) and tools. The FPGA platform together with a stray magnetic flux sensor are used for the online detection of faults in the outer race of bearings in induction motors. The integrated parts comprising the monitoring system are the stray magnetic flux triaxial sensor, several developed IPcores, an embedded processor for data processing, and a user interface where the diagnosis is visualized. The system performs the fault diagnosis through a statistical analysis as follows: First, a triaxial sensor measures the stray magnetic flux in the motor’s surroundings (this flux will vary as symptoms of the fault). Second, an embedded processor in an FPGA-based proprietary board drives the developed IPcores in calculating the statistical features. Third, a set of ranges is defined for the statistical features values, and it is used to indicate the condition of the bearing in the motor. Therefore, if the value of a statistical feature belongs to a specific range, the system will return a diagnosis of whether a fault is present and, if so, the severity of the damage in the outer race. The results demonstrate that the values of the root mean square (RMS) and kurtosis, extracted from the stray magnetic field from the motor, provide a reliable diagnostic of the analyzed bearing. The results are provided online and displayed for the user through interfaces developed on the FPGA platform, such as in a liquid crystal display or through serial communication by a Bluetooth module. The platform is based on an FPGA XC6SLX45 Spartan 6 of Xilinx, and the architecture of the modules used are described through hardware description language. This system aims to be an online tool that can help users of induction motors in maintenance tasks and for the early detection of faults related to bearings.

Methodology for Tool Wear Detection in CNC Machines Based on Fusion Flux Current of Motor and Image Workpieces

In the manufacturing industry, computer numerical control (CNC) machine tools are of great importance since the processes in which they are used allow the creation of elements used in multiple sectors. Likewise, the condition of the cutting tools used is paramount due to the effect they have on the process and the quality of the supplies produced. For decades, methodologies have been developed that employ various signals and sensors for wear detection, prediction and monitoring; however, this field is constantly evolving, with new technologies and methods that have allowed the development of non-invasive, efficient and robust systems. This paper proposes the use of magnetic stray flux and motor current signals from a CNC lathe and the analysis of images of machined parts for wear detection using online and offline information under the variation in cutting speed and tool feed rate. The information obtained is processed through statistical and non-statistical indicators and dimensionally reduced by linear discriminant analysis (LDA) and a feed-forward neural network (FFNN) for wear classification. The results obtained show a good performance in wear detection using the individual signals, achieving efficiencies of 77.5%, 73% and 89.78% for the analysis of images, current and stray flux signals, respectively, under the variation in cutting speed, and 76.34%, 73% and 63.12% for the analysis of images, current and stray flux signals, respectively, under the variation of feed rate. Significant improvements were observed when the signals are fused, increasing the efficiency up to 95% for the cutting speed variations and 82.84% for the feed rate variations, achieving a system that allows detecting the wear present in the tools according to the needs of the process (online/offline) under different machining parameters.

Methodology for Eddy Current Losses Calculation in Linear Variable Differential Transformers (LVDTs).

Linear variable differential transformer (LVDT) is a commonly used linear displacement sensor because of its good measurement characteristics. When using laminated ferromagnetic cores in LVDTs, it is very important to take eddy currents into the account during design phase of the sensor. Particularity of the open-type core means that the eddy currents induced by the stray magnetic flux that flow in large loops tangential to the lamination surfaces take on significant values. Due to the open-type core a typical LVDT has, depending on the core material, it is, therefore, very important to take eddy currents into the account when designing the sensor. This paper's goal is to present a methodology for calculating LVDT eddy current losses that can be applied to LVDT design in order to optimize the dimensions and help with selection of materials of the LVDTs, in order to achieve the highest measurement accuracy. Presented approach using an AτA-formulation with elimination of redundant degrees of freedom exhibits rapid convergence. In order to calculate the relationship between eddy current losses and core displacement, frequency, and material characteristics, a number of 3D finite element method (FEM) simulations was performed. Analysis of the obtained results using presented methodology for eddy current losses calculation in LVDTs enables the designer optimize the design of the LVDT.

Open-Circuit Radial Stray Magnetic Flux Density Based Noninvasive Diagnosis for Mixed Eccentricity Parameters of Interior Permanent Magnet Synchronous Motors in Electric Vehicles

By measuring the open-circuit radial stray magnetic flux density (RSMFD) of three spatial points, a novel diagnostic method for the eccentricity of interior permanent magnetic motors (IPMSMs) in electric vehicles is proposed. Outperforming traditional methods, the proposed method can noninvasively diagnose not only the severities of static eccentricity (SE), dynamic eccentricity (DE), and mixed eccentricity (ME), but also the eccentricity location. In this article, diagnosis results are described through three eccentricity parameters: SE ratio, DE ratio, and SE angle. First, an analytical model of the open-circuit RSMFD of the IPMSM is proposed, with its accuracy validated by experiments. The pattern of the impact of the eccentricity parameters on the open-circuit RSMFD is analyzed to form the basis of the proposed method. Then, an eccentricity prototype motor, which can continuously emulate the eccentricity according to any listed eccentricity parameters, is developed. A test rig is built to collect the open-circuit RSMFD and validate the analytical calculation results. Finally, an eccentricity diagnostic model based on the open-circuit RSMFD is established. The diagnosis results proved the noninvasiveness, effectiveness, and viability of the proposed method.

Non-Invasive Detection of Rotor Inter-Turn Short Circuit in Large Hydrogenerators by Using Stray Flux Measurement Combined With Convolutional Variational Autoencoder Analysis (CVAE)

Non-Invasive Detection of Rotor Inter-Turn Short Circuit in Large Hydrogenerators by Using Stray Flux Measurement Combined With Convolutional Variational Autoencoder Analysis (CVAE)

Multifractal Spectrum and Complex Cepstrum Analysis of Armature Currents and Stray Flux Signals for Sparking Detection in DC Motors

Multifractal Spectrum and Complex Cepstrum Analysis of Armature Currents and Stray Flux Signals for Sparking Detection in DC Motors

NuSTAR Spectral Analysis beyond 79 keV with Stray Light

Due to the structure of the NuSTAR telescope, photons at a large off axis (>1°) can reach the detectors directly (stray light), without passing through the instrument optics. At these off-axis angles NuSTAR essentially turns into a collimated instrument and the spectrum can extend to energies above the Pt K edge (79 keV) of the multilayers, which limits the effective area bandpass of the optics. We present the first scientific spectral analysis beyond 79 keV using a Cygnus X-1 observation in StrayCats: the catalog of stray light observations. This serendipitous stray light observation occurred simultaneously with an INTEGRAL observation. When the spectra are modeled together in the 30–120 keV energy band, we find that the NuSTAR stray light flux is well calibrated and constrained to be consistent with the INTEGRAL flux at the 90% confidence level. Furthermore, we explain how to treat the background of the stray light spectral analysis, which is especially important at high energies.

Resolving Current Transformer Partial Saturation Problem: An Analytical Approach to Design the Flux Equalizing Winding

Stray fluxes passing through the current transformer (CT) core produced by adjacent bus-bars and conductors or eccentric installation of primary conductors results in asymmetric magnetic flux distribution along the core. This may lead to exceeding the magnetic flux from its appropriate saturation level in some parts of the core, which is called partial saturation (PS) phenomenon. Under such a condition, CT error increases, and some relays maloperations may take place. This paper proposes a novel analytical approach to design flux equalizing winding (EW) for resolving such a problem. Moreover, PS occurrence in a core balance CT (CBCT) is analyzed using the finite element method (FEM), and its negative influence on a sensitive earth fault (SEF) relay is scrutinized as a case study. Simulation studies reveal that the proposed approach can prevent PS-related undesirable effects in a real power system.