Juan Carlos García-Prada

Condition Assessment of Power OLTC by Vibration Analysis Using Wavelet Transform

The suitable condition of an onload tap changer (OLTC) is essential for the operation of a power transformer. It is extremely desirable to have some indicators to assess the OLTC condition, especially if these indicators are capable of being used in an online monitoring system that does not affect the normal operation of the transformer. This paper describes a methodology by using envelope analysis based on the Hilbert transform and orthogonal decomposition of the wavelet to determine the main diagnostic parameters for power transformer OLTC condition assessment by means of vibration measurements during the tap changer operation.

An intelligent system for faulty-bearing detection based on vibration spectra

In this paper, a new detection and classification system of faulty bearings is presented. This system is based on artificial intelligent techniques and vibration signals in the frequency domain produced by the faulty bearings. The system consists of several neuro-fuzzy systems in cascade, along with measurement equipment for the vibration spectral data. These neuro-fuzzy systems have been used as bi-classifiers. That is, each neuro-fuzzy system is specialized in the classification between two different types of rolling bearing status. A careful selection process for rules has been included in the learning algorithm. Moreover, the demodulated vibration signal has been used as input to the neuro-fuzzy systems based on the Sugeno approach. Several trials were carried out, taking into account the vibration spectral data collected by the measurement equipment for each bearing. Different results with three types of faulty bearings using the proposed approach are shown, where satisfactory results have been achieved.

Local model for magnet–superconductor mechanical interaction: Experimental verification

Several models exist for calculating superconducting repulsion forces in the Meissner state that are based on the method of images. The method of images, however, is limited to a small number of geometrical configurations that can be solved exactly, and the physical interpretation of the method is under discussion. A general local model based on the London equations and Maxwell’s equations has been developed to describe the mechanics of the superconductor-permanent magnet system. Due to its differential form, this expression can be easily implemented in a finite elements analysis and, consequently, is easily applicable to any shape of superconductor in the Meissner state. It can solve both forces and torques. This paper reports different experiments undertaken in order to test the model’s validity. The vertical forces and the angle of equilibrium between a magnet and a superconductor were measured, and a positive agreement between the experiments and theoretical calculations was found.

Vibration Analysis Using Envelope Wavelet for Detecting Faults in the OLTC Tap Selector

An onload tap changer (OLTC) in suitable conditions is essential for the operation of a power transformer. It is extremely desirable to have indicators that help assess the conditions of the OLTC, especially if these indicators can be used in an on-line monitoring system that does not affect the normal operation of the transformer. This paper describes a method for detecting faults in the tap selector by means of vibration measurements during tap changer operation, using envelope analysis based on Hilbert transform and wavelet decomposition. Different failures at the tap selector can be distinguished in the vibration signature as they are reflected in different parts of that signature. The diagnosis parameters allowing the most selective failure classification are found.

Finite-size-induced stability of a permanent magnet levitating over a superconductor in the Meissner state

The force between a magnetic dipole and a finite superconductor in the Meissner state (H<Hc1) is calculated by using an expression based on London’s and Maxwell’s equations. It demonstrates the existence of attractiveness and stability. The finiteness of a piece of superconductor therefore suffices to explain a stability for the levitation of a magnet over it or even the suspension of one of them under the other one. This does not contradict the existence of flux penetration. However, this makes the flux penetration not necessary to explain both stability and attractiveness, as has been assumed until now.

Automatic detection of cracked rotors combining multiresolution analysis and artificial neural networks

In the maintenance of motor driven systems, detection of cracks in shafts play a critical role. Condition monitoring and fault diagnostics detect and distinguish different kinds of machinery faults, and provide a significant improvement in maintenance efficiency. In this study, we apply the discrete wavelet transform theory and multiresolution analysis (MRA) to vibration signals to find characteristic patterns of shafts with a transversal crack. The feature vectors generated are used as input to an intelligent classification system based on artificial neural networks (ANNs). Wavelet theory provides signal timescale information, and enables the extraction of significant features from vibration signals that can be used for damage detection. The feature vectors generated for every fault condition feed a radial basis function neural network (ANN-RBF) and apply supervised learning designed and adapted for different fault crack conditions. Together, MRA and RBF constitute an automatic monitoring system with a fast diagnosis online capability. The proposed method is applied to simulated numerical signals to prove its soundness. The numerical data are acquired from a modified Jeffcott Rotor model with four transverse breathing crack sizes. The results demonstrate that this novel diagnostic method that combines wavelets and an artificial neural network is an efficient tool for the automatic detection of cracks in rotors.

Automatic condition monitoring system for crack detection in rotating machinery

Abstract Maintenance is essential to prevent catastrophic failures in rotating machinery. A crack can cause a failure with costly processes of reparation, especially in a rotating shaft. In this study, the Wavelet Packets transform energy combined with Artificial Neural Networks with Radial Basis Function architecture (RBF-ANN) are applied to vibration signals to detect cracks in a rotating shaft. Data were obtained from a rig where the shaft rotates under its own weight, at steady state at different crack conditions. Nine defect conditions were induced in the shaft (with depths from 4% to 50% of the shaft diameter). The parameters for Wavelet Packets transform and RBF-ANN are selected to optimize its success rates results. Moreover, ‘Probability of Detection’ curves were calculated showing probabilities of detection close to 100% of the cases tested from the smallest crack size with a 1.77% of false alarms.

Review of Recent Advances in the Application of the Wavelet Transform to Diagnose Cracked Rotors

Wavelet transform (WT) has been used in the diagnosis of cracked rotors since the 1990s. At present, WT is one of the most commonly used tools to treat signals in several fields. Understandably, this has been an area of extensive scientific research, which is why this paper aims to summarize briefly the major advances in the field since 2008. The present review considers advances in the use and application of WT, the selection of the parameters used, and the key achievements in using WT for crack diagnosis.

Incipient Fault Detection in Bearings Through the use of WPT Energy and Neural Networks

Bearings are one of the more widely used elements in rotating machinery, reason why they have focused the attention of many researches in the last decades. The aim is to obtain a methodology that allows a reliable diagnosis of this kind of elements without dismounting them from the machine, and detecting the failure in incipient stages before a critical failure occurs. This manuscript develops and improvement of a technique showed in [1] of automated diagnosis of bearings through vibration signals, using the coefficients of the Multirresolution Analysis (MRA) and Multilayer Perceptron (MLP) neural network (NN). Data were obtained from a quasi-real industrial machine, where bearings were supporting axial and radial loads while rotating at different speeds. This technique offered very good results when diagnosing healthy and faulty bearings, nevertheless the reliability decreased when distinguishing between different kinds of failures. The novel technique showed in the present work, increases the success rates obtained using the same data: not only allows detecting early faults but also their location with high accuracy. The methodology exposed in this work is based on the use of the relative energy of the Wavelet Packets Transform (WPT), and NN, concretely, the RBF.

EMD-Based Methodology for the Identification of a High-Speed Train Running in a Gear Operating State

An efficient maintenance is a key consideration in systems of railway transport, especially in high-speed trains, in order to avoid accidents with catastrophic consequences. In this sense, having a method that allows for the early detection of defects in critical elements, such as the bogie mechanical components, is a crucial for increasing the availability of rolling stock and reducing maintenance costs. The main contribution of this work is the proposal of a methodology that, based on classical signal processing techniques, provides a set of parameters for the fast identification of the operating state of a critical mechanical system. With this methodology, the vibratory behaviour of a very complex mechanical system is characterised, through variable inputs, which will allow for the detection of possible changes in the mechanical elements. This methodology is applied to a real high-speed train in commercial service, with the aim of studying the vibratory behaviour of the train (specifically, the bogie) before and after a maintenance operation. The results obtained with this methodology demonstrated the usefulness of the new procedure and allowed for the disclosure of reductions between 15% and 45% in the spectral power of selected Intrinsic Mode Functions (IMFs) after the maintenance operation.