Wavelet transform based on inner product in fault diagnosis of rotating machinery: A review

Wavelet leaders multifractal features based fault diagnosis of rotating mechanism

Vibration analysis is an accepted method in condition monitoring of machines, since it can provide useful and reliable information about machine working condition. This paper surveys a new scheme for fault diagnosis of main journal-bearings of internal combustion (IC) engine based on power spectral density (PSD) technique and two classifiers, namely, K-nearest neighbor (KNN) and artificial neural network (ANN). Vibration signals for three different conditions of journal-bearing; normal, with oil starvation condition and extreme wear fault were acquired from an IC engine. PSD was applied to process the vibration signals. Thirty features were extracted from the PSD values of signals as a feature source for fault diagnosis. KNN and ANN were trained by training data set and then used as diagnostic classifiers. Variable K value and hidden neuron count (N) were used in the range of 1 to 20, with a step size of 1 for KNN and ANN to gain the best classification results. The roles of PSD, KNN and ANN techniques were studied. From the results, it is shown that the performance of ANN is better than KNN. The experimental results demonstrate that the proposed diagnostic method can reliably separate different fault conditions in main journal-bearings of IC engine.

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Comparison of Two Classifiers; K-Nearest Neighbor and Artificial Neural Network, for Fault Diagnosis on a Main Engine Journal-Bearing

Fault detection of rotating machinery by the complex and non-stationary vibration signals with noise is very difficult, especially at the early stages. Also, many failure mechanisms and various adverse operating conditions in rotating machinery involve significant nonlinear dynamical properties. As a novel method, phase space reconstruction is used to study the effect of faults on the chaotic behavior, for the first time. Strange attractors in reconstructed phase space proof the existence of chaotic behavior. To quantify the chaotic vibration for fault diagnosis, a set of new features are extracted. These features include the largest Lyapunov exponent; approximate entropy and correlation dimension which acquire more fault characteristic information. The variations of these features for different healthy/faulty conditions are very good for fault diagnosis and identification. For the first time, a new chaotic feature space is introduced for fault detection, which is made from chaotic behavior features. In this space, different conditions of rotating machinery are separated very well. To obtain more generalized results, the features are introduced into a neural network to identify different faults in rotating machinery. The effectiveness of the new features based on chaotic vibrations is demonstrated by the experimental data sets. The proposed approach can reliably recognize different fault types and have more accurate results. Also, the performance of the new procedure is robust to the variation of load values and shows good generalization capability for various load values.

Early fault detection of rotating machinery through chaotic vibration feature extraction of experimental data sets

This paper presents a finite element model of a rotor system with pedestal looseness stemming from a loosened bolt and analyzes the effects of the looseness parameters on its dynamic characteristics. When the displacement of the pedestal is less than or equal to the looseness clearance, the motion of the rotor varies from period-one through period-two and period-three to period-five with the decreasing of stiffness of the non-loosened bolts. The similar bifurcation phenomenon can be also observed during the increasing process of the rotational speed. But the rotor motion is from period-six through period-three to period-four with the decreasing of the foundation stiffness. When the stiffness of the foundation is small and the displacement of pedestal is greater than the looseness clearance, the response of the rotor exhibits period-one and high order harmonic components with the decreasing of looseness clearance, such as 2X, 3X etc. However, when the stiffness of the foundation is great, the spectrum of the response of the rotor will be from combined frequency components to the continuous spectrum with the decreasing of the looseness clearance.

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Analysis of Dynamic Characteristics for a Rotor System with Pedestal Looseness

This paper presents a new fault diagnosis procedure for rotating machinery using the wavelet packets-fractal technology and a radial basis function neural network. The main purpose is to investigate different fault conditions for rotating machinery, such as imbalance, misalignment, base looseness and combination of imbalance and misalignment. In this study, we measured the non-stationary vibration signals induced by these fault conditions. Applying wavelet packets transform to these signals, the fractal dimension of each frequency channel was extracted and the box counting dimension was used to depict the failure characteristics of the fault conditions. The failure modes were then identified by a radial basis function neural network. An experiment was conducted and the results showed that the proposed method can detect and recognize different kinds of fault conditions. Therefore, it is concluded that the combination of wavelet packets-fractal technology and neural networks can provide an effective method to diagnose fault conditions of rotating machinery.

Rotating Machinery Diagnosis Using Wavelet Packets-Fractal Technology and Neural Networks

This paper presents a new fault diagnosis procedure for rotating machinery using the wavelet packets-fractal technology and a radial basis function neural network. The faults of rotating machinery considered in this study include imbalance, misalignment, looseness and imbalance combined with misalignment conditions. When such faults occur, they usually induce non-stationary vibrations to the machine. After measuring the vibration signals, the wavelet packets transform is applied to these signals. The fractal dimension of each frequency bands is extracted and the box counting dimension is used to depict the failure characteristics of the vibration signals. The failure modes are then classified by a radial basis function neural network. An experimental study was performed to evaluate the proposed method and the results show that the method can effectively detect and recognize different kinds of faults of rotating machinery.

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Chih-Hao Chen

Papers

Rotating Machinery Diagnosis Using Wavelet Packets-Fractal Technology and Neural Networks

A New Fault Diagnosis Method of Rotating Machinery