Condition Monitoring And Fault Diagnosis Of Main Bearing Of I.C.E. Based On Noise Analysis

Fault diagnosis is important to the reducing of machinery accidents and economic loss in manufacture and practice. Scientists have researched a lot in this area these years, and have got many precious achievements; In this paper, based on the studies of former achievements, a lot of theoretical and practical researches in test method and diagnosis theory have been made by means of theory analysis and experiment.The experiment goal of this paper is main bearing abrasion fault diagnosis and condition monitoring for 2100-BI diesel engine. The moving manner of reciprocating I.C.E. shows its working processing of shock characteristic, simultaneously generates wild vibration and noise signal and often changes its operate speed. So it's difficult to get unique and simple spectrum unlike a rotary machine. The signals that indicate the faults of I.C.E. are usually being concealed by the normal working signal. The key work of condition monitoring and fault diagnosis should be finding out the fault feature parameters from disorder working signals.Firstly, based on the developed vibration testing method, a method for monitoring abrasion fault on main bearing of reciprocating engine using noise measurement is presented. It can avoid the trouble of vibration method that needs disassemble engines for placing acceleration sensors and selection of the position of sensors. On data processing, a order domain analysis method based on DSP technique is presented along with several concrete realizing methods. Time domain synchronous averaging can reduce the signal not coherent to rotation frequency effectively, and extract the repetitive signals directly related to machine operation condition. Both methods have some similar characteristics that this paper has introduced. After comparing with a few traditional methods, I present a type of high-speed DSP circuit and a mathematic algorithm to process order domain analysis and time domain synchronous averaging processing for vibration and noise signal. It improves the accuracy and tracking speed comparing to PLL based method.On the aspect of extracting fault feature frequency, a method for monitoring abrasion fault of main bearing using noise and orthogonal wavelet transform is presented. Wavelet analysis is used to translate noise signal of the engine into time-frequency domain. Selecting appropriate frequency band to pick up and making its envelope spectrum, then two feature frequency-0.5x speed frequency and 2x speed frequency that represent the abrasion condition can be found.In addition, a new diagnosis method of image processing technique is proposed. We illustrate the wavelet package analysis that can obtain the time-frequency distribution figure of noise signal, which can be used to define gray-image. Through image-average, we can get the standard time-frequency distribution images of different main bearing gap. Based on that, a main bearing wear fault diagnosis model using image matching is set up.A intranet-based mechanism fault diagnosis & monitoring software is also be compiled. Its main functions include: client/server signal acquisition and monitoring> FFT spectrum x envelope spectrum N wavelet /wavelet package analysis N neural networkintraining and mode recognition database manage > network communication and so on.Based on above software system, a web-based remote equipment monitoring and maintenance system is presented. Even though there are many successful Web-based applications, as today, designated Web-based system for maintenance is seldom. One of the main reasons is the difficulty in developing such a system as it involves huge database, intensive signal processing, and near real-time data communication for monitoring and control of equipment. Most importantly, the expert, who has experience in designing both the Web-based virtual instruments and automated system for equipment fault diagnosis, is difficult to be found. Nevertheless, the benefits provided by the Web-based maintenance system are substantial. Expensive hardware based instrumen...