Research On The Key Technology And Condition Monitor System For Turbo Compressor Predictive Maintenance

As an essential consisting facility in process industry, compressor's safe and efficient operation plays a critical role in the production and running in process industry. Maintenance, as an important technique measure to ensure the safe, stable and efficient operation of compressor, has been widely concerned by academic and business circles. How to construct advanced maitenance strategy of compressor and the corresponding maintenance supporting tools is an important research subject. Sponsored by National Natural Science Foundation of China, SINOPEC Scientific and Technologic Project and Science and Technology Plan of Changsha, the present paper did a profound and systematic research on nonlinear fault machenism, fault diagnosis methods, intelligent fault diagnosis technique and condition prediction technique. Consisdering demand of predictive maintenance of typical compressor unit in the cooperation enterprise, a corresponding monitoring system has been developed. The main research work and original research achievements are:1. Based on the establishment of nonlinear dynamic model of rotor-bearing system, the influence of system feedback forms and the stability caused by parameter variation is analyzed, which would thanks of computer simulation. The following rules have been revealed:a. The pumping frequency, eccentric quantity and quality of bearing spider in compressor's rotor-bearing system have a determining influence on the system's bifurcating and chaotic motion. In given conditions, system feedback will be periodic motion, approximate periodic motion, chaos and other complex motions. The ways to and off chaos for rotor system are mainly period doubling bifurcation, popping bifurcation and approximate periodic motion;b. The oil membrane force of sliding bearing has an essential influence on the system motion status. There are period doubling bifurcation, popping bifurcation and other bifurcations, which may change the motion nature of each system in system steady-state period feedbacks. Period doubling bifurcation and approximate periodic motion is one of the ways for the system directing to and off chaos;c. From the system bifurcation figure, we can see that the system feedback is sensitive to parameter variation. A tiny parameter variation may arise a great change in system feedback forms.The above rules provide theoretical principal and foundation for rotor system fault mechanism and feature analysis.2. Aiming at the sensitive feature of fractural dimension to noises, a fault diagnosis method for rotor system is proposed in this paper, in which correlation dimension and EMD denosing were combined. The analysis results from simulation signals show the validity of proposed method, which provide a new idea for the diagnosis of compressor loose, compressor collision and friction, and compressor loose combined with collion and friction. Aiming at the feature that general demodulation time frequency analytical method is suitable for frequency-modulated and amplitude-modulated signals of multiple components, a compressor gear fault diagnosis method was put forward based on general demodulation time frequency analysis. The analysis results from simulation and experiment signals in gear starting procedure show that this method is superior to that of the wavelet method in extracting modulation characteristics from gear vibration signals, which provides a new method for gear fault diagnosis.3. Intelligent compressor diagnosis based on case reasoning was studied, as well. Consisdeing that cases from case reasoning techniques in intelligent diagnosis application cannot share and integrate, the reasoning process is not reusable, lacking in reasoning searching and other disadvantages, case presenting method based on ontology and corresponding searching mechanism have been designed, which realized intelligent plant fault disgnosis based on case reasoning. Take a compressor unit from certain petrol business as an object, the intelligent diagnosis technique has been applied to real industry and has achieved favorable result.4. Aiming at the prediction of compressor condition, the present paper proposed a condition prediction method to combine supporting vector machine and empirical mode decomposition. This method not only applies to statictical machine learning of small samples, but also has remarkable prediction results in the condition of multi-step prediction, which is applicable to lots of fields. The prediciton on real monitoring data of compressor shows that this method, compared with supporting vector mechine prediction method, is much more accurate.5. In order to accomplish the predictive maintenance application of plant key units, group monitoring technique of plant key units has been studies. Making use of plant units group's similarity in structure features, operation situation and technical parameter, the technique improves the accuracy and economy of condition monitoring. Take K5403 compressor from the cooperation enterprise as a monitoring object, with the help of software configurating technique, virtual instrument technique, and object-oriented technique, a compressor group monitoring system has been designed and developed. This system enjoys stable operation, complete function and advanced structure expandability, which can be effectively applied to compressor group monitoring system in process industry.