Fault Diagnosis For Vibration Of Turbo Generator Based On Artifitial Neural Network

As the equipment that generates power by revolving, How much a turbo generator vibrates is an important symbol to judge its running status. Analyzing the external vibration signals of a turbo generator enables the diagnosis of internal faults, and this is commonly used in modern diagnosis technologies.This paper elaborates the technology of processing vibration signal and diagnosing the vibration faults of turbo generators based on the artificial neural network, and for taking advantage of diagnosing resources reasonably, this paper explores a remote diagnosis method to find out the internal faults of turbo generators.The first part of this paper introduces the fault mechanism of turbo generator's vibration, as well as common on-line diagnosis methods based on fault mechanism and characteristics. Besides, it provides the pros and cons of each method. The second part elaborates the technology of processing fault information. In the aspects of time field and frequency field, the effect on the measure accuracy of the vibration signals from non-full-cycle sampling is elaborated. At the same time, it also details how to reduce the effect on the signal measure accuracy and spectrum analysis from the synchronous error. The third part describes how the multilayer forward nerve network is applied in the diagnosis of turbo generator's vibration fault. What's more, the genetic algorithm is adopted to train the nerve network conducting fault diagnosis. It is proved that when diagnosing vibration faults, a trained nerve network performs better as far as recognition accuracy is concerned, and has certain anti-noise ability. The fourth part introduces a turbo generator's fault monitoring and diagnosing system based on the Internet. The system consists of three parts: data sampling and preprocessing, on-line fault recognition and control, and remote diagnosis.Due to the restrictions on time and experimental conditions, this paper only realizes sample management and neural network training, sample data reporting (simulation), and on-line recognition and control. In this way, the method is proved to be feasible. This system is developed in Java to ensure it is transplantable between different operating systems.It should be mentioned that the innovation of this paper is to describe the effect on the measure accuracy of the vibration signals from non-full-cycle sampling in the aspects of time field and frequency field, and it also introduce the technology of combining the artificial neural network with GA in the turbo generator's fault diagnosis. By gathering the fault characteristics, constructing study samples, and training theartificial neural network with GA, the method enables good study effect and high recognition accuracy, and certain anti-noise ability. By using the pattern of client/server (C/S) of the network technology, on-line monitoring applications support multiple monitoring clients gather and report fault information simultaneously. After on-line recognition, control commands are sent according to recognition results. Java-based development enables the applications to run stably in Windows XP, UNIX, and Linux.