Humberto Henao

A High-Resolution Frequency Estimation Method for Three-Phase Induction Machine Fault Detection

Fault detection in alternating-current electrical machines that is based on frequency analysis of stator current has been the interest of many researchers. Several frequency estimation techniques have been developed and are used to help the induction machine fault detection and diagnosis. This paper presents a technique to improve the fault detection technique by using the classical multiple signal classification (MUSIC) method. This method is a powerful tool that extracts meaningful frequencies from the signal, and it has been widely used in different areas, which include electrical machines. In the proposed application, the fault sensitive frequencies have to be found in the stator current signature. They are numerous in a given frequency range, and they are affected by the signal-to-noise ratio. Then, the MUSIC method takes a long computation time to find many frequencies by increasing the dimension of the autocorrelation matrix. To solve this problem, an algorithm that is based on zooming in a specific frequency range is proposed with MUSIC in order to improve the performances of frequency extraction. Moreover, the method is integrated as a part of MUSIC to estimate the frequency signal dimension order based on classification of autocorrelation matrix eigenvalues. The proposed algorithm has been applied to detect a rotor broken bar fault in a three-phase squirrel-cage induction machine under different loads and in steady-state condition.

Diagnosis of Broken Bar Fault in Induction Machines Using Discrete Wavelet Transform without Slip Estimation

The aim of this paper is to present a wavelet-based method for broken bar fault detection in induction machines. The frequency-domain methods which are commonly used need speed information or accurate slip estimation for frequency components localization in any spectrum. Nevertheless, the fault frequency bandwidth can be well defined for any induction machine due to numerous previous investigations. The proposed approach consists in the energy evaluation of this known bandwidth with time-domain analysis using the discrete wavelet transform (DWT). Then, it has been applied to the stator current space vector magnitude and the instantaneous magnitude of the stator current signal for different broken bar fault severities and load levels.

Trends in Fault Diagnosis for Electrical Machines: A Review of Diagnostic Techniques

The fault diagnosis of rotating electrical machines has received an intense amount of research interest during the last 30 years. Reducing maintenance costs and preventing unscheduled downtimes, which result in losses of production and financial incomes, are the priorities of electrical drives manufacturers and operators. In fact, both correct diagnosis and early detection of incipient faults lead to fast unscheduled maintenance and short downtime for the process under consideration. They also prevent the harmful and sometimes devastating consequences of faults and failures. This topic has become far more attractive and critical as the population of electric machines has greatly increased in recent years. The total number of operating electrical machines in the world was around 16.1 billion in 2011, with a growth rate of about 50% in the last five years [1].

Bearing Fault Detection by a Novel Condition-Monitoring Scheme Based on Statistical-Time Features and Neural Networks

Bearing degradation is the most common source of faults in electrical machines. In this context, this work presents a novel monitoring scheme applied to diagnose bearing faults. Apart from detecting local defects, i.e., single-point ball and raceway faults, it takes also into account the detection of distributed defects, such as roughness. The development of diagnosis methodologies considering both kinds of bearing faults is, nowadays, subject of concern in fault diagnosis of electrical machines. First, the method analyzes the most significant statistical-time features calculated from vibration signal. Then, it uses a variant of the curvilinear component analysis, a nonlinear manifold learning technique, for compression and visualization of the feature behavior. It allows interpreting the underlying physical phenomenon. This technique has demonstrated to be a very powerful and promising tool in the diagnosis area. Finally, a hierarchical neural network structure is used to perform the classification stage. The effectiveness of this condition-monitoring scheme has been verified by experimental results obtained from different operating conditions.

Analytical approach of the stator current frequency harmonics computation for detection of induction machine rotor faults

The aim of this paper is to analyze theoretically and experimentally the stator current of a three-phase squirrel-cage induction machine in order to show how it is influenced by electrical rotor faults. The approach used for this study analyzes the modification introduced by n broken rotor bars in the rotor cage magnetomotive force and then estimates the resulting frequency spectrum in the stator current. This approach is validated in a 3-kW 230-V/400-V 50-Hz 2850-r/min two-pole three-phase induction machine, showing the sensitive frequency components to rotor fault condition.

Review of failures and condition monitoring in wind turbine generators

Increasing wind power generation quantity in power systems needs obviously reliable operation. Therefore, accurate condition monitoring and fault diagnosis are almost mandatory. This paper aims to report recent works on condition monitoring and fault diagnosis for wind turbine generators. Wind turbines are subjected to different sort of failures, thus before stating condition monitoring and fault diagnosis methods it is necessary to identify what kind of failures can be found in the real world. As a result, the gearbox is one of the most critical components of the wind turbine in which failures could stop generation for a long time. Recently, several condition monitoring and fault diagnosis techniques have been introduced in order to minimize downtime and maintenance cost while increasing energy availability and life time service of the wind farms. Vibration sensors have been used for long time in wind turbine condition monitoring systems to collect data of the generator health. Actually, using such sensors is expensive, difficult to install and sometimes impossible to be implemented in already installed wind turbines. Therefore, researchers have been focused on using electrical signature analysis from sensors in order to detect wind turbines faults and generator health in a cheap, easy to install and accessible way.

A Web-Based Remote Laboratory for Monitoring and Diagnosis of AC Electrical Machines

This paper deals with the development of a virtual platform for a Web-based remote application dedicated to condition monitoring and fault detection for ac electrical machines. The platform is based on several tools developed by using the LabVIEW software. Various techniques of condition monitoring and diagnosis of electrical and mechanical faults in ac electrical machines have been integrated such as the broken rotor bar, winding short circuit, bearing damage, or static/dynamic eccentricities. The main features are related to a user-friendly interface, a low-maintenance source code, and a standardized database for ac electrical machine diagnosis. The platform architecture, as well as three different test-rig configurations, has been described. The complete system can be controlled in both local and remote modes by using a simple Internet connection. Some remote experiences have been carried out between the University of Picardie “Jules Verne,” Amiens, France, and the University of Bologna, Bologna, Italy, to verify the effectiveness of the proposed system. The direct applications of this original package are based on diagnosis techniques applied to ac electrical machine faults. Some examples of rotor broken bar detection using classical techniques have been presented to show the effectiveness of the proposed platform. Further information will soon be available on the Open European Laboratory on Electrical Machines Web site: www.oelem.org.

Torsional Vibration Effects on Induction Machine Current and Torque Signatures in Gearbox-Based Electromechanical System

The monitoring of heavy-duty electromechanical systems is crucial for their preventive maintenance planning. In these systems, the mechanical anomalies such as load troubles, great torque dynamic variations, and torsional oscillations lead to shaft fatigue and aging of other mechanical parts such as bearings and gearboxes. In this paper, a gearbox-based electromechanical system is investigated. Initially, a simple gearbox dynamic model is used to show the effects of rotating input, output, and mesh frequency components on the electromagnetic torque and consequently on the stator current signature. By this model, the influence of transmission error, eccentricities of pinion/wheel, and teeth contact stiffness variation is demonstrated for a healthy gearbox. Then, it is shown that the electrical machine can be considered as a torque sensor through electromagnetic torque estimation for torsional vibration monitoring without any extra mechanical sensor. A test-rig based on a 5.5-kW three-phase squirrel-cage induction motor connected to a wound-rotor 4-kW induction generator via a one-stage gearbox has been used to validate the proposed method.

An equivalent internal circuit of the induction machine for advanced spectral analysis

The aim of this paper is to develop a method to validate an equivalent internal circuit of the three-phase squirrel-cage induction machine for advanced signal processing including fault diagnosis. The proposed method is based on the computation of the stator and rotor current spectra. An experimental setup for an 11-kW induction machine was developed in order to get numerical data for voltages and currents from the stator side. The comparison between the analytical computation, the simulation, and the experimental results, shows the model capability to reproduce the electromagnetic phenomena in the induction machine with mixed time and space harmonics. The proposed model can be used to design electrical fault detection devices with low cost and noninvasive sensors.

Diagnosis of broken-bars fault in induction machines using higher order spectral analysis.

Detection and identification of induction machine faults through the stator current signal using higher order spectra analysis is presented. This technique is known as motor current signature analysis (MCSA). This paper proposes two higher order spectra techniques, namely the power spectrum and the slices of bi-spectrum used for the analysis of induction machine stator current leading to the detection of electrical failures within the rotor cage. The method has been tested by using both healthy and broken rotor bars cases for an 18.5 kW-220 V/380 V-50 Hz-2 pair of poles induction motor under different load conditions. Experimental signals have been analyzed highlighting that bi-spectrum results show their superiority in the accurate detection of rotor broken bars. Even when the induction machine is rotating at a low level of shaft load (no-load condition), the rotor fault detection is efficient. We will also demonstrate through the analysis and experimental verification, that our proposed proposed-method has better detection performance in terms of receiver operation characteristics (ROC) curves and precision-recall graph.