Hailian Du

Coal mass estimation of the coal mill based on two-step multi-sensor fusion

In the fossil power plant, it is rather difficult to measure the coal mass of the coal mill exactly, in order to make the coal mill work on the optimal active state, multi-sensor are used to fuse multiple signal, and the qualitative estimation of the coal mass is gotten from the algorithm. The neural network has the ability of self-organize, self-learn, and disposing the nonlinear problems, strong fault tolerant and robustness, D-S evidential theory can solve the uncertainty problem, but the evident is hard to get. Two-step fusion method combined the merit of the neural network and the evidential theory, the neural network is on the first step, the second step uses the normalization result as the evident. When this algorithm is simulated on the computer, the result proves that the method can estimate the coal mass qualitatively, according to the historical record of coal mill.

The Electric Actuator's Fault Diagnosis Based on Information Fusion

The method based on information fusion fault diagnosis is put forward and the two-step fusion diagnosis model of neural network and D-S evidence theory is given against the complexity and multiplicity when the fault of control system happens; the fault detection laboratory platform is erected based on the information fusion through the analysis of the electric actuators principle and typical faults, and on this basis, the fault of the electric actuator is diagnosed by the experiment and computer simulation. This method overcomes the uncertainty of the neural network fault diagnosis and improves the accuracy of system diagnosis, and the experiment result verified the methods validity.

Fault characteristics extraction of motor's vibratory signal based on wavelet transform

The fault in motor arises with the change in vibration, which shows on the vibration form, amplitude size and frequency composition. So if a different frequency composition and the corresponding time of vibration signal are detected, we can efficiently diagnosis the faults type, extent and time. Fault characteristics extraction of motors vibratory signal based on wavelet transform makes good use of the advantage of wavelet transform singularity detection and multi-resolution analysis remove the noise from the signal, and extract the fault characteristics. The computer emulation verifies the method is practical.

The Transformer Fault Diagnosis Based on AdaBoost Least Square Support Vector Machine

Least square support vector machine integrated with adaptive boost algorithm was applied to the transformer fault diagnosis. In order to obtain the training sample, characteristic gases dissolved in the faulty transformer oil were collected and normalized, then a number of different classifiers are to be constructed though adaptive boost algorithm on the same training set. Subsequently, least squares support vector machine is used as the base classifier, which was fast in calculation and was improved by iteration in classification ability. The fault diagnosis results show that the method was simple and flexible, it has high accuracy rate of fault diagnosis. To a certain extent,this method makes up for the deficiencies of three-ratio method, such as code missing and boundary absolute.

The Electric Actuator’s Fault Diagnosis Based on Date Fusion Technology

: The method based on data fusion fault diagnosis is put forward and the two-step fusion diagnosis model of neural network and D-S evidence theory is given against the complexity and multiplicity when the fault of control system happens; the fault detection laboratory platform is erected based on the data fusion through the analysis of the electric actuator’s principle and typical faults, and on this basis, the fault of the electric actuator is diagnosed by the experiment and computer simulation. This method overcomes the uncertainty of the neural network fault diagnosis and improves the accuracy of system diagnosis, and the experiment result verified the method’s validity.

The fault character of the motors identified based wavelet transform

Based on the contrasting of the basic characteristics wavelet transform, this paper studies the method about getting the fault information by the singularity of the signals identified by wavelet, and the simulation from the computer for the fault signal model of the motors proves that the wavelet transform cannot only separate noise from the useful signal effectively, but also reflect the character and the time when the fault appears.