Search

This research presents a comparative analysis of two broken rotor bar (BRB) fault identification techniques for closed-loop induction motors (IMs). Both motor current signal analysis and Hilbert transform (HT) rely on spectrum analysis by means of fast fourier transform (FFT). Both approaches have shown their ability to identify BRBs under varying loads. In contrast, the HT is deemed more efficient than the motor current signature analysis (MCSA) approach when the motor is working without load. To maintain a high-performance speed control and to compensate for BRBs effect on the mechanical speed, the approach of control used is direct torque control (DTC). Utilizing a real-time implementation in MATLAB/Simulink with the real-time interface (RTI) based on the dSPACE 1104 board, the efficacy of the two techniques was evaluated.

International audience; Power converters' usage is expanding ever in industrial applications as they provide flexibility, high level of performances and new functionalities. However, with increased complexity come new constraints with respect to reliability. This chapter covers a study on reliability of a lab-scale power electronic module taken here as a vehicle. The downsizing of converters and new application-related operating constraints are accompanied by an increase in current density. The use of Silicon Carbide wide-gap technology in power modules is therefore attracting but remains a challenge because this technology is not yet mature and does not benefit from the deep knowledge established about Silicon counterpart. Therefore, health monitoring has naturally emerged as an effective way to implement a reliability assessment. After a brief description of the expected failure modes, an experimental failure monitoring bench will be presented. The choice and implementation of failure indicators through a classification using a neural network will be discussed and presented.

The global energy crisis has allowed marine currents to enter the field of vision of all countries. Marine current turbine (MCT) is a kind of deep-sea equipment that converts marine current energy into electric power, and its safe and reliable operation is very important. In order to facilitate monitoring of the blade status of MCTs in multiple scenarios, the chapter deals with the diagnosis method of MCT blade followed by different methods. First, a review of the MCT blade fault diagnosis method has been presented. Then two different methods are discussed in this chapter. One is the signal processing method based on the stator current, which includes VMD denoising and proposed novel LDA classifier; the another one is the image semantic segmentation technique based on the MCT image, which includes semantic segmentation and adaptive recognition technical. These two methods can be organically combined under different biofouling cases. When the biofouling is low to affect the output torque of the turbine, the method based on image processing can be useful. On the contrary, the method based on current signal is more convenient and effective. The experimental results show that the two methods proposed in this chapter can effectively detect MCT biofouling in different scenarios. It also proposes several trends for a handle with biofouling problem in conclusion.

This work presents a new approach for analyzing the propagation mechanism of fault signatures in squirrel cage induction motor drives. The used method is based on the analysis of different currents of solid states converters and the fault signatures are investigated when motor is supplied by the volt-per-hertz control mode. Analytical expressions of currents at different location for broken bar, mixed air-gap eccentricity, unbalanced voltage supply are developed using switching functions of both inverter and diode rectifier. Experimental results are provided throughout the paper, to underpin the theoretical analysis. The results show that capacitor current spectrum can give useful information about the state of squirrel cage induction motor. However, due to the low-pass filter composed by capacitor only the fault signatures at low frequency range can be detected from the rectifier supply line current spectrum.

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.

International audience; Computation of the internal electric field induced in the human body by low-frequency power systems is time consuming. We compute an approximate solution in nearly-real time by using a reduced basis for the internal field. The basis is computed once (off-line) by approximating the source by multipoles, the coefficients of which can be computed for any position of the human body thanks to analytic translation and rotation formulas. The results are validated with respect of Finite Element (FEM) computation.

International audience; As they are a part of the energy transmission chain, bearings are considered as important mechanical components in rotating machines. This importance requires a special attention in order to avoid expensive production shutdown due to the appearance of failures. It is therefore necessary to anticipate the appearance of faults by implementing an appropriate prediction model. There exist in the literature several examples of prediction models able to estimate the remaining useful life (RUL) of bearings. These models are based on the principle of the long-term prediction without considering the degradation state of bearings or by defining a degradation threshold arbitrarily. It should be noted that, in addition to a reliable prediction model, identifying the degradation states is an important parameter in the estimation of the RUL. The presented paper proposes a particular approach based on the artificial intelligence (AI) principle. The proposed approach is composed of two model-based AI inspired by the reasoning of the human for the RUL estimation. This reasoning is modeled via the neural networks for time-series prediction. These models are called the adaptive-neuro fuzzy inference system and the neo-fuzzy neuron. The proposed approach is also composed by a third model-based AI. This model is inspired by the behavior of ants to identify the different degradation states of bearings. The combination of these two types of AI provides reliable and robust prediction results.

[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).

International audience; The condition monitoring of induction motors (IM), is an important concern for industry due to the widespread use of these machines. Magnetic Flux Analysis, has been proven to be a reliable method of diagnosing these motors. Among the IM types, squirrel-cage motors (SCIM) are one of the most commonly used. In many industrial applications, the IM are driven by different types of starters, quite often by soft-starters. Despite rotor damages are more prone to occur in line-started motors, these kind of failures have been also reported in those ones driven by soft-starters. Related to this, the use of these type of starters may introduce some harmonic components, that could veil the magnetic flux signature of the different rotor faults. So, the aim of this study is to confirm if the Stray Flux Analysis technique maintains its reliability in these cases. Thus, this article presents the results of soft-started induction motors start-up tests, both in healthy and faulty motors. The fault components are detected by analyzing the stray flux during the starting and the study is complemented by analyzing the stray flux during the steady-state. In addition to the failure patterns, numerical indicators have been found so the identification of the failures is not only qualitative, but also quantitative. The results confirm the potential of the technique for detecting electromechanical failures in soft-started SCIMs.

International audience; 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 external magnetic field 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 during transient operation of the motor (e.g. 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 and, therefore, for a better determination of the machine condition. This paper presents an advanced algorithm based on MUSIC for enhancing the visualization of the harmonics caused by different motor failures in the electromotive force signals induced by the external magnetic field. 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 MUSIC algorithm for the reliable diagnosis of electromechanical faults.

International audience; The reliability and safety operation of an industrial system are the main objectives of industrial companies to remain competitive in a constantly growing market. Unexpected shutdowns can often lead to physical hazards as well as economic consequences in key sectors. Hence, fault prediction emerges as an important focus of the industry. Thus, this paper aims to detail the prognostic aspect and provides a state of the art of existing data-driven prognostic methods used in the literature. This paper shows the diversity of possible prognostic methods and the choice of one among them that will define a framework for industrials.

[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.

[EN] The stray flux that is present in the vicinity of an induction motor is a very interesting information source to detect several types of failures in these machines. The analysis of this quantity can be employed, in some cases, as a supportive tool to complement the diagnosis provided by other quantities. In other cases, when no other motor quantities are available, stray flux analysis can become one of the few alternatives to evaluate the motor condition. Its non-invasive nature, low cost and easy implementation makes it a very interesting option that requires further investigation. The aim of this work is to evaluate the suitability of the stray flux analysis under the starting transient as a way to detect certain faults in induction motors (broken rotor bars and misalignments), even when these types of faults coexist in the motor. To this end, advanced signal processing tools will be applied. Several positions of the flux sensors are considered in this study. Also, for the first time, a fault indicator based on the stray flux analysis under the starting is introduced and its sensitivity is compared versus other indicators relying on other quantities. It must be emphasized that, since the capture of the transient and steady-state flux signals can be carried out in the same measurement, the application of the approach presented in this work is straightforward and its derived information may become crucial for the diagnosis of some faults., This work was supported in part by the Spanish "Ministerio de Economia y Competitividad" and FEDER program in the framework of the "Proyectos I+D del Subprograma de Generacion de Conocimiento, Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia" (ref: DPI2014-52842-P).

International audience; This paper deals with the analysis of the effect of broken bars fault in stator currents spectrums of a dual three phase induction motor drives. The influence of the unbalanced current between to sets resulting from the leakage inductances and phase winding resistances unbalances on the well-known characteristic frequencies of broken bars and the rotor slot harmonic (RSH) is discussed. The results show that the use of (x, y) current controllers allows not only to provide better performance of dual three phase induction motor but also to avoid error in the assess fault severity. Experimental results have been presented to validate the analytical and simulation results.

International audience; The importance of monitoring machine health increases with the aim of reducing maintenance costs and improving availability and quality of service. In this context, the main challenge for researchers is to find a convenient and effective method of monitoring gearboxes in a non-invasive manner. Because of busy industrial environments, background noise reduces the sensitivity of vibration signals and acoustic emissions. Consequently, the Motor Current Signal Analysis (MCSA) is a promising non-intrusive alternative to vibration measurement for gearbox monitoring. On the other hand, the monitoring of bearing faults is very important in maintenance programs since these problems account for over 40% of the total amount of failures in the induction motor. Hence, this article presents the use of the Discrete Wavelet Transform (DWT) as well as the K-means clustering technique applied to bearing and gear faults indicators. The Discrete Wavelet Transform is used as a filtering method in order to extract the various frequency bands containing the defect profile. The indicators used in this work are descriptors of geometric shapes that are obtained from Clarke transformation. These indicators provide a particular shape in the presence of a mechanical fault. The results of the carried out experiments demonstrate the efficiency of the Discrete Wavelet Transform combined with the Clarke's shape indicators for bearings and gears fault classification.

International audience; New GaN, SiC and diamond based devices are able to work up to 275°C with transients reaching 100V/nS. These specifications require a new design to supply the gate driver. At these temperatures, under these isolation constraints, the power transmission has to be realized without any magnetic material, via a magnetic field using inductive coupling between coils of wire. However, this kind of isolated power supply is currently limited by the area needed for the windings. In order to minimize the space used, this paper highlights the impact of each converter element. To this end, a model of the whole system is presented and validated experimentally. Based on the analytical expression of each element of the system, we demonstrate that single turn coils allow the power transmitted to be maximized for a given space used. Based on the proposed design, we transmitted 200mW through a 9cm2 prototype.

International audience; Despite their great improvements, reliability and availability of power electronic devices remain always a focus. In safety-critical equipment, where the occurrence of faults can generate catastrophic losses, health monitoring of most critical components is absolutely needed to avoid and prevent breakdowns. In this paper, a noninvasive health monitoring method is proposed. It is based on fuzzy logic and the neural network to estimate and predict the equivalent series resistance (ESR) and the capacitance (C) of capacitors and supercapacitors. This method, based on the neo-fuzzy neuron model, performs a real-time processing (time series prediction) of the measured device impedance and the degradation data provided by accelerated ageing tests. To prove the efficiency of the proposed method, two experiments were performed. The first one was dedicated to the estimation of the ESR and C for a set of 8 polymer film capacitors, while the second one was dedicated to the prediction of the ESR and C for a set of 18 supercapacitors. The obtained results showed that combining fuzzy logic and the neural network is an accurate approach for the health monitoring of capacitors and supercapacitors.