Fault Diagnosis of Train Axle Box Bearing Based on Multifeature Parameters

Failure of the train axle box bearing will cause great loss. Now, condition-based maintenance of train axle box bearing has been a research hotspot around the world. Vibration signals generated by train axle box bearing have nonlinear and nonstationary characteristics. The methods used in traditional bearing fault diagnosis do not work well with the train axle box. To solve this problem, an effective method of axle box bearing fault diagnosis based on multifeature parameters is presented in this paper. This method can be divided into three parts, namely, weak fault signal extraction, feature extraction, and fault recognition. In the first part, a db4 wavelet is employed for denoising the original signals from the vibration sensors. In the second part, five time-domain parameters, five IMF energy-torque features, and two amplitude-ratio features are extracted. The latter seven frequency domain features are calculated based on the empirical mode decomposition and envelope spectrum analysis. In the third part, a fault classifier based on BP neural network is designed for automatic fault pattern recognition. A series of tests are carried out to verify the proposed method, which show that the accuracy is above 90%

Bearing fault diagnosis via kernel matrix construction based support vector machine

A novel approach on kernel matrix construction for support vector machine (SVM) is proposed to detect rolling element bearing fault efficiently. First, multi-scale coefficient matrix is achieved by processing vibration sample signal with continuous wavelet transform (CWT). Next, singular value decomposition (SVD) is applied to calculate eigenvector from wavelet coefficient matrix as sample signal feature vector. Two kernel matrices i.e. training kernel and predicting kernel, are then constructed in a novel way, which can reveal intrinsic similarity among samples and make it feasible to solve nonlinear classification problems in a high dimensional feature space. To validate its diagnosis performance, kernel matrix construction based SVM (KMCSVM) classifier is compared with three SVM classifiers i.e. classification tree kernel based SVM (CTKSVM), linear kernel based SVM (L-SVM) and radial basis function based SVM (RBFSVM), to identify different locations and severities of bearing fault. The experimental results indicate that KMCSVM has better classification capability than other methods

Fault diagnosis for rotating machinery based on multi-differential empirical mode decomposition

The fault diagnosis of rotating machinery has crucial significance for the safety of modern industry, and the fault feature extraction is the key link of the diagnosis process. As an effective time-frequency method, Empirical Mode Decomposition (EMD) has been widely used in signal processing and feature extraction. However, the mode mixing phenomenon may lead to confusion in the identification of multi frequency signals and restricts the applications of EMD. In this paper, a novel method based on Multi-Differential Empirical Mode Decomposition (MDEMD) was proposed to extract the energy distribution characteristics of fault signals. Firstly, multi-order differential signals were deduced and decomposed by EMD. Then, their energy distribution characteristics were extracted and utilized to construct the feature matrix. Finally, taking the feature matrix as input, the classifiers were applied to diagnosis the existence and severity of rotating machinery faults. Simulative and practical experiments were implemented respectively, and the results demonstrated that the proposed method, i.e. MDEMD, is able to eliminate the mode mixing effectively, and the feature matrix extracted by MDEMD has high separability and universality, furthermore, the fault diagnosis based on MDEMD can be accomplished more effectively and efficiently with satisfactory accuracy

A fault diagnosis methodology for rolling element bearings based on advanced signal pretreatment and autoregressive modelling

This study proposes a methodology for rolling element bearings fault diagnosis which gives a complete and highly accurate identification of the faults present. It has two main stages: signals pretreatment, which is based on several signal analysis procedures, and diagnosis, which uses a pattern-recognition process. The first stage is principally based on linear time invariant autoregressive modelling. One of the main contributions of this investigation is the development of a pretreatment signal analysis procedure which subjects the signal to noise cleaning by singular spectrum analysis and then stationarisation by differencing. So the signal is transformed to bring it close to a stationary one, rather than complicating the model to bring it closer to the signal. This type of pre-treatment allows the use of a linear time invariant auto-regressive model and improves its performance when the original signals are non-stationary. This contribution is at the heart of the proposed method, and the high accuracy of the diagnosis is a result of this procedure. The methodology emphasizes the importance of preliminary noise cleaning and stationarisation. And it demonstrates that the information needed for fault identification is contained in the stationary part of the measured signal. The methodology is further validated using three different experimental setups, demonstrating very high accuracy for all of the applications. It is able to correctly classify nearly 100 percent of the faults with regard to their type and size. This high accuracy is the other important contribution of this methodology. Thus, this research suggests a highly accurate methodology for rolling element bearing fault diagnosis which is based on relatively simple procedures. This is also an advantage, as the simplicity of the individual processes ensures easy application and the possibility for automation of the entire process

CLASSIFICATION OF BEARING FAULTS USING EXTREME LEARNING MACHINE ALGORITHMS

Roller element bearing fault diagnosis is crucial for industry to maintain machine in good condition so that there is no delay of work due to machine breaks down. This project implements the bearing fault diagnosis that classifies the bearing data into four classes which are healthy bearing, inner race defect bearing, outer race defect bearing, and roller element defect bearing. Most of existing bearing fault diagnosis are done using Back Propagation (BP) algorithm which take a long time to train the neural network resulting in inefficiency of training the Single Hidden Layer Feedforward Neural Network (SLFN). Therefore, this project introduces three learning algorithms which are Extreme Learning Machine (ELM), Finite Impulse Response Extreme Learning Machine (FIR-ELM) and Discrete Fourier Transform Extreme Learning Machine (DFT-ELM) to improve the bearing fault diagnosis accuracy and shorten the time used to train and test the neural network

Fault Diagnosis of Gas Turbine Engines by Using Multiple Model Approach

The field of fault detection and isolation (FDI) has attracted much attention in control theory during the last three decades which has resulted in development of sophisticated FDI algorithms. However, increasing the complexity of FDI algorithms is not necessarily feasible. Particularly for on-line FDI, the FDI unit must have the minimum possible computation cost to prevent any long delays in fault detection. In this research, we try to address the FDI problem of a single spool jet engine by using a modified linear multiple model (MM). We first develop a novel symbolic computation-based method for linearization purposes such that the obtained linear models are subjected to the symbolic fault variables. By substituting certain values for these symbolic variables, one can obtain different linear models, which describe mathematically the healthy and faulty models. In order to select the operating point, we use this fact that for a given constant fuel flow (W_f), the system reaches a steady state, that is varying for different values of W_f. Therefore, the operating points for linearization can be determined by the level of the Power Level Angel (PLA) (different values of W_f). These operating points are selected such that an observer, which is designed as a candidate for the healthy mode, can accurately estimates the states of the system in healthy scenario and the number of false alarm then would be kept to minimum. If the system works at different operating points one can then discretize the W_f into different intervals such that in each interval a linear model represents the behavior of the original system. By using the obtained models for different operating points, one designs the corresponding FDI units. Second, we provide a modified multiple model (MM) approach to investigate the FDI problem of a single spool jet engine. The main advantage of this method lies in the fact that the proposed MM consists of a certain set of linear Kalman filter banks rather than using nonlinear Kalman filters such as the Extended Kalman Filter which requires more computational cost. Moreover, a hierarchical structural multiple model is used to detect and isolate multiple faults. The simulation results show the capability of the proposed method when it is applied to a single spool jet engine model

Experimental validation of model for fault by wear parameter identification in lubricated bearings

Orientador: Katia Lucchesi Cavalca DediniTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia MecânicaResumo: Máquinas rotativas são utilizadas em uma vasta gama de processos industriais onde, de maneira geral, o aumento da eficiência está diretamente relacionado ao aumento da velocidade de rotação. Por este motivo, mancais hidrodinâmicos são comumente utilizados devido a sua elevada capacidade de carga e aplicabilidade a altas rotações. No entanto, este tipo de mancal pode inserir anisotropia no sistema ocasionando o surgimento de instabilidades fluído induzidas. Assim, rotores de grande porte são submetidos a testes de estabilidade na fase de comissionamento e ao longo de sua vida útil visando uma operação segura. O presente trabalho se insere neste contexto em duas vertentes. Primeiramente, é a apresentado um método para a identificação dos parâmetros de desgaste em mancais hidrodinâmicos (profundidade e posição angular); um dos problemas inerentes a este tipo de mancal. O método é baseado no ajuste das dFRFs (Directional Frequency Response Function) do modelo desenvolvido em função das curvas obtidas experimentalmente. A dFRF experimental é obtida através da aplicação, por um atuador magnético, de um ruído-branco, e pela medição do deslocamento do eixo nos mancais. A dFRF simulada é obtida a partir do modelo em elementos finitos do eixo contendo os coeficientes de rigidez e amortecimento dos mancais hidrodinâmicos, contemplando casos com ou sem falha por desgaste. A presença do desgaste apresenta grande influência nos termos cruzados da dFRF, devido à intensificação do caráter anisotrópico dos mancais, sendo estes mais eficazes na identificação dos parâmetros do desgaste se comparados à FRF em coordenadas físicas. As vantagens do método proposto são a aplicabilidade em sistemas reais e a robustez na identificação dos parâmetros de desgaste de mancais hidrodinâmicos. A segunda vertente do trabalho é a apresentação do método MOBAR (Multiple Output Backward Autoregressive Method) como uma alternativa na obtenção do fator de amortecimento de rotores sustentados por mancais lubrificados, podendo ser utilizado nos testes de comissionamento e manutenção preditiva. O método é baseado em um modelo autorregressivo que utiliza apenas a resposta transiente do sistema, obtida a partir da aplicação de uma força rotativa utilizando-se um atuador magnético. Com as modificações propostas neste trabalho, o método apresentou-se consistente na identificação dos parâmetros de amortecimento, enquanto que a duração dos testes experimentais é da ordem de segundos, reduzindo custos de paradas programadasAbstract: Rotating machines are used in a wide range of industrial processes and, in general, a higher efficiency is directly related to a higher rotational speed. For this reason, hydrodynamic bearings are frequently used due to its high load capacity and applicability to higher rotational speeds. However, this kind of bearing may introduce anisotropy in the system leading to the appearance of fluid-induced instabilities. Thus, large rotors are submitted to stability tests at the commissioning stage and during its operational life aiming a secure operation. The present work is inserted in this context in two segments. First, a method for wear parameters identification in journal bearings (depth and angular position) , one of the inherent problems of such bearings, is presented. The method is based in adjust the dFRFs (Directional Frequency Response Function) of the developed model as a function of the experimental obtained curves. The experimental dFRF is obtained through the application of a white noise, by a magnetic actuator, and the displacement measurements of the shaft in the bearings. The simulated dFRF is obtained from a finite element model of the shaft containing the stiffness and damping coefficients of the lubricated bearings, with or without wear. The presence of wear has a high influence in the crossed terms of the dFRF, due to the increase of the anisotropy in the bearings, being more suitable for the wear parameters identification than the FRF in physical coordinates. The advantages of the proposed method are its applicability in real systems and the robustness of the wear parameters identification in hydrodynamic bearings. The second segment of this work consists of presenting the MOBAR method (Multiple Output Backward Autoregressive Method) as an alternative to obtain the damping factor of journal bearing supported rotors, which can be applied in tests for commissioning and predictive maintenance. The method is based in an autoregressive model, which uses only the transient response of the system, obtained through the application of a rotational force using a magnetic actuator. With the modifications proposed in this work, the method has presented consistence in the damping factor identification, while the tests duration are in the order of seconds, reducing the costs of programmed stopsDoutoradoMecanica dos Sólidos e Projeto MecanicoDoutor em Engenharia Mecânica5553/11-3CAPE

A sensitivity comparison of Neuro-fuzzy feature extraction methods from bearing failure signals

This thesis presents an account of investigations made into building bearing fault classifiers for outer race faults (ORF), inner race faults (IRF), ball faults (BF) and no fault (NF) cases using wavelet transforms, statistical parameter features and Artificial Neuro-Fuzzy Inference Systems (ANFIS). The test results showed that the ball fault (BF) classifier successfully achieved 100% accuracy without mis-classification, while the outer race fault (ORF), inner race fault (IRF) and no fault (NF) classifiers achieved mixed results

Study of Computational and Experimental Methodologies for Cracks Recognition of Vibrating Systems using Modal Parameters

Mostly the structural members and machine elements are subjected to progressive static and dynamic loading and that may cause initiation of defects in the form of crack. The cause of damage may be due to the normal operation, accidents or severe natural calamities such as earthquake or storm. That may lead to catastrophic failure or collapse of the structures. Thereby the importance of identification of damage in the structures is not only for leading safe operation but also to prevent the loss of economy and lives. The condition monitoring of the engineering systems is attracted by the researchers and scientists very much to invent the automated fault diagnosis mechanism using the change in vibration response before and after damage. The structural steel is widely used in various engineering systems such as bridges, railway coaches, ships, automobiles, etc. The glass fiber reinforced epoxy layered composite material has become popular for constructing the various engineering structures due to its valuable characteristics such as higher stiffness and strength to weight ratio, better damage tolerance capacity and wear resistance. Therefore, layered composite and structural steel have been taken into account in the current study. The theoretical analysis has been performed to measure the vibration signatures (Natural Frequencies and Mode Shapes) of multiple cracked composite and structural steel. The presence of the crack in structures generates an additional flexibility. That is evaluated by strain energy release rate given by linear fracture mechanics. The additional flexibility alters the dynamic signatures of cracked beam. The local stiffness matrix has been calculated by the inverse of local dimensionless compliance matrix. The finite element analysis has been carried out to measure the vibration signatures of cracked cantilever beam using commercially available finite element software package ANSYS. It is observed from the current analysis, the various factors such as the orientation of cracks, number and position of the cracks affect the performance and effectiveness of damage detection techniques. The various automated artificial intelligent (AI) techniques such as fuzzy controller, neural network and hybrid AI techniques based multiple faults diagnosis systems are developed using vibration response of cracked cantilever beams. The experiments have been conducted to verify the performance and accuracy of proposed methods. A good agreement is observed between the results