Fulei Chu

Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography

Abstract The application of the wavelet transform for machine fault diagnostics has been developed for last 10 years at a very rapid rate. A review on all of the literature is certainly not possible. The purpose of this review is to present a summary about the application of the wavelet in machine fault diagnostics, including the following main aspects: the time–frequency analysis of signals, the fault feature extraction, the singularity detection for signals, the denoising and extraction of the weak signals, the compression of vibration signals and the system identification. Some other applications are introduced briefly as well, such as the wavelet networks, the wavelet-based frequency response function, etc. In addition, some problems in using the wavelet for machine fault diagnostics are analysed. The prospects of the wavelet analysis in solving non-linear problems are discussed.

Polynomial Chirplet Transform With Application to Instantaneous Frequency Estimation

In this paper, a new time-frequency analysis method known as the polynomial chirplet transform (PCT) is developed by extending the conventional chirplet transform (CT). By using a polynomial function instead of the linear chirp kernel in the CT, the PCT can produce a time-frequency distribution with excellent concentration for a wide range of signals with a continuous instantaneous frequency (IF). In addition, an effective IF estimation algorithm is proposed based on the PCT, and the effectiveness of this algorithm is validated by applying it to estimate the IF of a signal with a nonlinear chirp component and seriously contaminated by a Gaussian noise and a vibration signal collected from a rotor test rig.

Using genetic algorithms and finite element methods to detect shaft crack for rotor-bearing system

Shaft crack is a very dangerous and frequent fault in rotating machine, but how to locate and configure it is just an inverse problem and not easy to tackle. In this paper, a genetic algorithms based method for shaft crack detection is proposed and described, which formulates the shaft crack detection as an optimization problem by means of finite element method and utilizes genetic algorithms to search the solution. Using genetic algorithms avoids some of the weaknesses of traditional gradient based analytical search methods including the difficulty in constructing well-defined mathematical models directly from practical inverse problems. The numerical experiments suggest that good predictions of the shaft crack locations and configuration are possible and the proposed method is feasible. The study also indicates that the proposed method has the potential to solve a wide range of inverse identification problems in a systematic and robust way.

Analytical model of bolted disk–drum joints and its application to dynamic analysis of jointed rotor

Bolted joints are widely used in aero-engines. One of the common applications is to connect the rotor disks and drums. An analytical model for the bending stiffness of the bolted disk–drum joints is developed. The joint stiffness calculated using the analytical model shows sound agreement with the calculation obtained based on finite element analyses. The joint stiffness model is then implemented into the dynamic model of a simple rotor connected through the bolted disk–drum joint. Finally, the whirling characteristics and steady-state response of the jointed rotor are investigated to evaluate the influence of the joint on the rotor dynamics, where the harmonic balance method is employed to calculate the steady-state response to unbalance force. The simulation results show that the joint influence on the whirling characteristics of the rotor system can be neglected; whereas, the presence of the bolted disk–drum joint may lead to a decrease in the rotor critical speeds due to the softening of the joint stiffness. The proposed analytical model for the bolted disk–drum joints can be adopted conveniently for different types of rotor systems connected by bolted disk–drum joints.

RETRACTED: Using genetic algorithms to detect and configure shaft crack for rotor-bearing system

This article has been retracted at the request of the Editors. Reason: The above mentioned article was published in virtually identical form in another journal prior to appearing in Computer Methods in Applied Mechanics and Engineering. Papers submitted to the journal are required to be original works and, therefore, should not be submitted elsewhere. The duplication occurred without the knowledge of the editors, and they wish to withdraw the article from publication. The original article by Yongyong He, Dan Guo and Fulei Chu, entitled “Using genetic algorithms and finite element methods to detect shaft crack for rotor-bearing system”, was published in Mathematics and Computers in Simulation, 57 (2001) 95–108.

Design of fuzzy controller for smart structures using genetic algorithms

In this paper, the genetic algorithm is used to optimize the membership functions of a fuzzy logic controller for smart structure systems. With the performance requirement of vibration control, a new encoding method and a fitness function with variable factors are put forward for the genetic algorithm. A constraint problem, which the new encoding method will face, is discussed and solved. The effectiveness of the genetic algorithm is demonstrated with a cantilever beam attached with piezoelectric materials.

Gear Damage Assessment Based on Cyclic Spectral Analysis

With regard to the AMFM characteristics, and especially the cyclostationarity of gear vibrations, cyclic spectral analysis is used to extract the modulation features of gearbox vibration signals to detect and assess localized gear damage. The explicit equation for the cyclic spectral density in a closed form for AMFM signals is deduced, and its properties in the joint cyclic frequency-frequency domain are summarized. The ratio between the sum of the cyclic spectral density magnitude along the frequency axis at the cyclic frequencies of modulating frequency and 0 Hz varies monotonically with the amplitude modulation magnitude. Hence it is useful to track modulation magnitude. Localized gear damage generates periodic impulses, and its growth increases the magnitude of periodic impulses. Consequently, the amplitude modulation magnitude of gear AMFM vibration signals increases. Hence the ratio can be used as an indicator of the health condition of gearboxes. The analysis of both gear crack simulation vibration signals and gearbox lifetime experiments shows a globally monotonic increase as gear damage severity increases. The proposed approach has the potential to assess the health of gearboxes, and predict severe damage.

Condition monitoring and fault diagnostics of wind turbines

Condition monitoring and fault diagnostics is important for wind turbines to ensure safety and reliability. However, the application in wind turbine is still in initial stage. This paper analyses the frequent faults in wind turbines and determines the sensor configuration of a wind turbine. Architecture of condition monitoring and fault diagnostics system is built to monitor a wind farm. Methodology and algorithms based on vibration, noise and acoustic emission signals have been discussed to diagnose the fault of wind turbines.

Evolutionary strategy for classification problems and its application in fault diagnostics

Abstract Genetic algorithms (GAs) based evolutionary strategy is proposed for classification problems, which includes two aspects: evolutionary selection of the training samples and input features, and evolutionary construction of the neural network classifier. For the first aspect, the GA based k -means-type algorithm (GKMT) is proposed, which combines GA and k -means-type (KMT) to achieve the optimal selection of the training samples and input features simultaneously. By this algorithm, the “singular” samples will be eliminated according to the classification accuracy and the features that facilitate the classification will be enhanced. On the opposite, the useless features will be suppressed and even eliminated. For the second aspect, the hierarchical evolutionary strategy is proposed for the construction and training of the neural network classifier (HENN). This strategy uses the hierarchical chromosome to encode the structure and parameters of the neural network into control genes and parameter genes respectively, designs and trains the network simultaneously. Finally, the experimental study pertained to the fault diagnostics for the rotor-bearing system is given and the results presented show that the proposed evolutionary strategy for the classification problem is feasible and effective.

Vibration control of a rotor–bearing system using shape memory alloy: II. Experimental study

The companion paper, part I, presents a self-optimizing support system in theory. A pseudo self-optimizing support system is proposed for a rotor-bearing system based on shape memory alloy (SMA). A numerical simulation is given to verify the theoretical model. This paper presents the experimental study to test and verify the theoretical model further. The experiment process and experimental results are presented in detail, and some analyses and comparisons are given.