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Wound rotor induction motors (WRIM) are widely used in a vast number of high output power industrial applications due to their capability of reaching high start torques while maintaining low inrush currents. Nonetheless, these machines are very prone to require early maintenance, and the possibility of presenting rotor winding asymmetry failures is high due to their more complex rotor circuit. Although some recent works have proposed techniques that overcome the drawbacks of conventional methods, an additional research effort for the development of alternative approaches able to enhance their performance and reliability is desirable. In this work, a new method for the diagnosis of rotor asymmetries in WRIM is presented. The proposed methodology is based on a feed-forward neural network (FFNN) feed by suitable fault severity indicators, which rely on the maximum energy density of higher order harmonics (amplified by the failure) at strategic regions of the time-frequency maps obtained from stray flux signals under startup transient. Experimental results for three different levels of rotor asymmetries in a 11 KW WRIM prove the reliability of the enhanced proposed system in comparison to conventional approaches that only rely on the main sideband fault-harmonic amplitudes.

Tool condition monitoring (TCM) is one of the most relevant tasks during a machining process. Latest high-quality productivity standards make it essential to monitor the cutting tool wearing. Current TCM methodologies demand the installation of sensors near the working area, which in practical terms, it is not the most optimal solution since the final diagnosis can be disturbed by noisy signals and direct interferences with the machining process. This paper proposes a novel non-invasive methodology based on the time-frequency analysis of the stray flux captured around the spindle-motor to detect and estimate the wearing level in cutting tools. Moreover, a new fault indicator based on this quantity is introduced through the application of the discrete wavelet transform (DWT). The results obtained are promising and demonstrates the effectiveness of the proposal to become a complementary source of information to classical approaches. This is validated with a Fanuc Oi mate Computer Numeric Control (CNC) turning machine for three different cutting tool wearing levels and different cutting depths

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.

Kinematic chains have a fundamental role in the modern industry thanks to the great variety of applications where they can be found. For this reason the development of new fault detection methods has gained traction in recent years. Different physical signals have been used for the diagnosis of kinematic chains such as electric current, mechanical vibrations, or stray flux; nonetheless, the analysis of temperature signals measured by thermographic cameras has proven to be an effective way to detect certain types of failures complementing the existing work on this area. This paper presents the development of an infrared image processing system for the detection of electromechanical faults (misalignment, unbalance, broken bars, bearing defects, and gearbox wear) on a kinematic chain composed by an induction motor, an output pulley, a plastic transmission band, and an alternator. The system is based on the automatic segmentation of a region of interest associated to the kinematic chain through the implementation of image processing techniques and the calculation of statistical characteristics from the histogram of a thermal image acquired by a low-cost thermographic camera.

Gearboxes are very important transmission components widely used for industrial productions in aerospace, automotive and heavy industry due to their high transmission efficiency and strong load-bearing capacity. During their service life, gearboxes are subjected to constant stresses, which causes an imminent and gradually wear on the tooth gear. Most current methodologies for gearbox fault diagnosis are based on vibration and current signals, which relies on the feature of specific fault harmonics. However, their respective time-frequency and power spectrum representations are blurred due to noisy signals, compromising the final diagnosis. This paper presents a novel methodology for uniform tooth wear in gearboxes with two main contributions: the proposal is based on stray flux signals, and their transient analysis with a high-resolution MUSIC method. This solution mitigates the effects of processing noisy signals and evidences only larger frequency components. The obtained results are promising and demonstrates the effectiveness of the proposal to become an excellent alternative to classical approaches.

[EN] Electric motors have been widely used as fundamental elements for driving kinematic chains on mechatronic systems, which are very important components for the proper operation of several industrial applications. Although electric motors are very robust and efficient machines, they are prone to suffer from different faults. One of the most frequent causes of failure is due to a degradation on the bearings. This fault has commonly been diagnosed at advanced stages by means of vibration and current signals. Since low-amplitude fault-related signals are typically obtained, the diagnosis of faults at incipient stages turns out to be a challenging task. In this context, it is desired to develop non-invasive techniques able to diagnose bearing faults at early stages, enabling to achieve adequate maintenance actions. This paper presents a non-invasive gradual wear diagnosis method for bearing outer-race faults. The proposal relies on the application of a linear discriminant analysis (LDA) to statistical and Katz¿s fractal dimension features obtained from stray flux signals, and then an automatic classification is performed by means of a feed-forward neural network (FFNN). The results obtained demonstrates the effectiveness of the proposed method, which is validated on a kinematic chain (composed by a 0.746 KW induction motor, a belt and pulleys transmission system and an alternator as a load) under several operation conditions: healthy condition, 1 mm, 2 mm, 3 mm, 4 mm, and 5 mm hole diameter on the bearing outer race, and 60 Hz, 50 Hz, 15 Hz and 5 Hz power supply frequencies., This work was supported by the Spanish Ministerio de Ciencia Innovación y Universidades and FEDER program in the framework of the `Proyectos de I+D de Generación de Conocimiento del Programa Estatal de Generación de Conocimiento y Fortalecimiento Científico y Tecnológico del Sistema de I+D+i, Subprograma Estatal de Generación de Conocimiento¿ (ref: PGC2018-095747-B-I00), and Consejo Nacional de Ciencia y Tecnología (CONACyT) under scholarship 652815.

The analysis of motor currents under starting has proven to be a powerful method for enhancing the reliability of the diagnosis of several electromechanical failures in induction motors. Among other advantages, it enables to avoid false indications of conventional methods and, the same time, it provides a robust diagnosis regardless of the operating conditions or of the constructive characteristics of the machine. In particular, the method has proven its usefulness to detect rotor asymmetries, not only in cage motors (CM) but also in wound rotor induction motors (WRIM), in which this type of faults are more frequent. Nowadays, several research lines are oriented toward studying the extrapolation of this methodology for the detection of other types of faults. The present paper expands the applicability of the method to detect stator asymmetries in WRIM. This is done by using a field motor case, in which the application of the transient-based method enabled to detect the presence of certain levels of asymmetry both in the rotor and in the stator winding. The timely detection of this anomaly, thanks to the application of these methods, enabled to avoid a potential outage of the field WRIM with subsequent catastrophic consequences.

Magnetic flux analysis is a condition monitoring technique that is drawing the interest of many researchers and motor manufacturers. The great enhancements and reduction in the costs and dimensions of the required sensors, the development of advanced signal processing techniques that are suitable for flux data analysis, along with other inherent advantages provided by this technology are relevant aspects that have allowed the proliferation of flux-based techniques. This paper reviews the most recent scientific contributions related to the development and application of flux-based methods for the monitoring of rotating electric machines. Particularly, aspects related to the main sensors used to acquire magnetic flux signals as well as the leading signal processing and classification techniques are commented. The discussion is focused on the diagnosis of different types of faults in the most common rotating electric machines used in industry, namely: squirrel cage induction machines (SCIM), wound rotor induction machines (WRIM), permanent magnet machines (PMM) and wound field synchronous machines (WFSM). A critical insight of the techniques developed in the area is provided and several open challenges are also discussed.

During a machining process, it is very important to carry out the corresponding tasks using cutting tools in a healthy condition, since the quality of the manufactured component can be seriously deteriorated when using worn tools, leading to a possible increase of production costs. Current methodologies are capable of monitoring and diagnosing the wear on the cutting tool adequately. However, in most of them the use of sensors located near the working area is necessary and indispensable, which in practical terms is not the most optimal solution since it is desirable to rely on non-invasive techniques. In this work, the impact of the use of worn cutting tools on the stray flux captured around the induction motor (IM) driving the spindle chuck is studied using time-frequency decomposition mathematical tools (and, more specifically, the short-time Fourier transform (STFT)). The experimentally obtained results show the appearance of some specific frequency components having considerable amplitudes for worn cutting tools, and also demonstrates the potential that this technique has to provide highly relevant information to determine the presence of worn cutting tools. Therefore, it becomes an excellent informational source for the diagnosis of these types of failures, taking advantage of its non-invasive nature.

The most recent trend in the electric motor condition monitoring area relies on combining the information obtained from different machine quantities in order to reach a more reliable conclusion about the motor’s health. This knowledge is of critical importance nowadays, especially in industrial applications in which unexpected outages can lead to severe repercussions. This paper presents a new intelligent sensor that combines, in a single unit, the information obtained from the analysis of different quantities. In its current version the diagnostic provided by the sensor is obtained from the analysis of stray fluxes (both axial and radial) and currents. Unlike other solutions, the sensor is based on the application of advanced signal processing tools that are adapted to the analysis of these quantities under transient conditions. The combination of these new tools with the classical steady-state analysis of such quantities enables to obtain a more reliable conclusion on the motor health. The experiments included in the paper demonstrate the reliability provided by the sensor, which is being prepared to incorporate a third input based on infrared data.

Induction motors are essential and widely used components in many industrial processes. Although these machines are very robust, they are prone to fail. Nowadays, it is a paramount task to obtain a reliable and accurate diagnosis of the electric motor health, so that a subsequent reduction of the required time and repairing costs can be achieved. The most common approaches to accomplish this task are based on the analysis of currents, which has some well-known drawbacks that may lead to false diagnosis. With the new developments in the technology of the sensors and signal processing field, the possibility of combining the information obtained from the analysis of different magnitudes should be explored, in order to achieve more reliable diagnostic conclusions, before the fault can develop into an irreversible damage. This paper proposes a smart-sensor that explores the weighted analysis of the axial, radial, and combination of both stray fluxes captured by a low-cost, easy setup, non-invasive, and compact triaxial stray flux sensor during the start-up transient through the short time Fourier transform (STFT) and characterizes specific patterns appearing on them using statistical parameters that feed a feature reduction linear discriminant analysis (LDA) and then a feed-forward neural network (FFNN) for classification purposes, opening the possibility of offering an on-site automatic fault diagnosis scheme. The obtained results show that the proposed smart-sensor is efficient for monitoring and diagnosing early induction motor electromechanical faults. This is validated with a laboratory induction motor test bench for individual and combined broken rotor bars and misalignment faults.

[EN] In the induction motor predictive maintenance area, there is a continuous search for new techniques and methods that can provide additional information for a more reliable determination of the motor condition. In this context, the analysis of the stray flux has drawn the interest of many researchers. The simplicity, low cost and potential of this technique makes it attractive for complementing the diagnosis provided by other well-established methods. More specifically, the study of this quantity under the starting has been recently proposed as a valuable tool for the diagnosis of certain electromechanical faults. Despite this fact, the research in this approach is still incipient and the employed signal processing tools must be still optimized for a better visualization of the fault components. Moreover, the development of advanced algorithms that enable the automatic identification of the resulting transient patterns is another crucial target within this area. This article presents an advanced algorithm based on the combined application of MUSIC and neural networks that enables the automatic identification of the time-frequency patterns created by the stray flux fault components under starting as well as the subsequent determination of the fault severity level. Two faults are considered in the work: rotor problems and misalignments. Also, different positions of the external coil sensor are studied. The results prove the potential of the intelligent algorithm for the reliable diagnosis of electromechanical faults., This work was supported in part by the Spanish "Ministerio de Ciencia Innovacion y Universidades" and in part by FEDER program in the "Proyectos de I+D de Generacion de Conocimiento del Programa Estatal de Generacion de Conocimiento y Fortalecimiento Cientifico y Tecnologico del Sistema de I+D+i, Subprograma Estatal de Generacion de Conocimiento" (PGC2018-095747-B-I00).

Induction motors are fundamental elements in the industry field since they perform several tasks under a broad variety of conditions, a situation that affects its performance making them susceptible to fail despite its robustness. Over several decades, many techniques and methodologies to assess the healthiness state of electrical motors have been proposed and implemented successfully. In this regard new advances in the signal processing field has taken a great importance since these emerging tools are used to accomplish fault diagnosis tasks in order to increase the reliability of such processes. So, it is very important to explore the use of new signal processing techniques to detect and diagnose in a timely manner faults in electrical motors. In this work, it is proposed to use the wavelet entropy of the stray flux signal captured by a coil sensor to estimate the winding insulation status of induction motors, a very common failure presented on this type of drives, that if not attended on time, it can end in a catastrophic and irreversible fault in a matter of minutes. The proposal uses suitable time-frequency decomposition (TFD) tools to isolate the studied fault. The results obtained show that the methodology proposed here can be considered as an excellent alternative for estimating the winding insulation healthiness state of an induction motor online and efficiently, as well as being possible to implement it in a programmable logic device.

The analysis of the external magnetic field has been proven to be an effective way to diagnose different types of faults in induction motors. Classical methods based on permanent regime are being complemented by recent approaches that rely on the analysis of the magnetic field during transient operation of the machine. Most of these approaches have been applied with success to detect failures in cage induction motors, but few works have dealt with their wound rotor counterparts. This paper proposes the analysis of the magnetic field in the vicinity of the machine to detect rotor failures in wound rotor induction motors. Classical methods based on the Fast Fourier transform of steady-state signals are compared with recent methods relying on the analysis of signals captured under starting. Different positions for the considered flux sensors are considered and novel indicators are presented for the determination of the level of fault severity.

[EN] Over recent decades, the detection of faults in induction motors (IMs) has been mainly focused in cage motors due to their extensive use. However, in recent years, wound-rotor motors have received special attention because of their broad use as generators in wind turbine units, as well as in some large power applications in industrial plants. Some classical approaches perform the detection of certain faults based on the fast Fourier transform analysis of the steady state current (motor current signature analysis); they have been lately complemented with new transient time-frequency-based techniques to avoid false alarms. Nonetheless, there is still a need to improve the already existing methods to overcome some of their remaining drawbacks and increase the reliability of the diagnostic. In this regard, emergent technologies are being explored, such as the analysis of stray flux at the vicinity of the motor, which has been proven to be a promising option to diagnose the motor condition. Recently, this technique has been applied to detect broken rotor bar failures and misalignments in cage motors, offering the advantage of being a noninvasive tool with simple implementation and even avoiding some drawbacks of well-established tools. However, the application of these techniques to wound rotor IMs (WRIMs) has not been studied. This article explores the analysis of the external magnetic field under the starting to detect rotor winding asymmetry defects in WRIMs by using advanced signal processing techniques. Moreover, a new fault indicator based on this quantity is introduced, comparing different levels of fault and demonstrating the potential of this technique to quantify and monitor rotor winding asymmetries in WRIMs., This work was supported by the Spanish "Ministerio de Ciencia Innovacion y Universidades" and Fondo Europeo de Desarrollo Regional program in the framework of the "Proyectos de I+D de Generacion de Conocimiento del Programa Estatal de Generacion de Conocimiento y Fortalecimiento Cientifico y Tecnologico del Sistema de I+D+i, Subprograma Estatal de Generacion de Conocimiento" under Grant PGC2018-095747-B-I00.