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Natural gas compressor units, as the core equipment in natural gas transportations, play a crucial role in daily industrial production. Meanwhile, with the increase of equipment operating time, the performance of each component will inevitably decline and even fail in severe cases. Apparently, the unit simulation model, also known as the mechanism model with the characteristic maps, lays foundation for remotely monitoring the conditions of this rotating machine and giving the suggestions for equipment maintenance. And it should be accurate and adaptive according to the performance degradation. To achieve this goal, some thermodynamic parameters and their data samples are decided to represent this model with the Grey Relational Analysis and the optimal Latin Hypercube Sampling. Then, a data-driven model based on Gaussian process is established and the Bayesian calibration method is applied to obtain the model uncertainty, which means the degradation uncertainty in this study. This approach can realize the uncertainty quantification and the result can be adopted to judge and correct the characteristic map in the simulation model. A case study is implemented on an industrial compressor unit to demonstrate this method’s effectiveness. The corrected model outputs are compared with the actual data to acquire the model accuracy and their variance is 0.993, which points out their similarities. This approach avoids the disadvantages of the traditional model correction methods such as low utilization rate of operational data, non-use of prior information, and long fitting time. It makes full use of prior information and numerous data. Besides, it can reduce the diagnostic error, provide new possibility for the real-time monitor and promote the development of health management in the industrial equipment.

For long-term operation, blades start to show some defects with increasing operating hours, such as fouling, erosion, corrosion, damage and tip clearance. As the basic unit components of gas turbines, the health conditions of blades directly affect the energy conversation efficiency and service life of the whole equipment. The process from first installation to scrap is blades’ whole operation life cycle. It is an effective way to establish the whole operation life cycle model of blades for real-time monitoring, troubleshooting and prevention, so as to improve the management of equipment. The current research on the whole operation life cycle model is mostly limited to a single subject, such as thermal effects or stress effects. It lacks a profound analysis of this issue from the multi-disciplinary perspective. Meanwhile, the deterioration of blades influence on geometry variation of the blade surface is not taken into consideration in detail. Therefore, the current blade life model is not accurate enough to represent the actual situation. In this paper, the typical gas path deterioration is characterized by blade profile parameters, including the increment of the blade leading edge thickness, the increment of the blade trailing edge, and the change of the blade surface roughness in the whole operation life cycle model of blades. The influencing factors of aerodynamics and strain are synthetically characterized through the study of their multi-disciplinary influence mechanism. And the relationship between the corresponding influencing factors and the variation of blade profile parameters is established. Thus, the numerical simulation model under multi-physics is built to reveal its distribution and trends of the flow field and stress in the gas path. The result shows that it can protect the blades, ensure safe and stable operation, and reduce the deterioration rate.

Digital power plant is the theory and method to improve the operating quality of power plant by quantifying, analyzing, controlling and deciding the physical and working objects of power plants in the whole life cycle. Signals and management information of power plants will be digitized, so as to realize information exchange reliably and accurately and large-scale distributed resource sharing based on the network technology. Then optimization decisions and scientific guidance for plant operation will be proposed by intelligent expert system based on the digital resources. Therefore, the foundation of digital power plant is system modeling and performance analysis. However, there are some problems in the process of the modeling performance analysis of digital power plant. For instance, each unit of the system model has different dimensions and different type of mathematical description, and the data or information used for modeling are defined differently and belong to different enterprises, who do not want to share their information. Metamodeling is potential to solve these problems. It defines the specification to describe a unit and the relationship between different elements. Compared with traditional modeling methodologies for thermal systems, metamodeling makes the model more standardized, and the relationship of the model elements is more explicit and better understood by the co-simulation partners. In this paper, the collaborative modeling and simulation platform for digital power plant has been established based on the metamodeling method and the performance of the target plant has been analyzed from different aspects via field data. The metamodeling method consists of three parts: syntax definition, model development and algorithm definition. The result shows that the collaborate modeling and simulation platform can be used to reduce costs, decrease equipment failure rate, and improve plant output, so as to guarantee the safety and increase economics.

Humid Air Turbine (HAT) cycle is one of the most advanced gas turbine cycles in the world. It has drawn great attention due to its high efficiency and good environmental compatibility. The saturator is the key component in the HAT cycle, which utilizes liquid water to humidify compressed air to improve the system performance and make it possible to recover low temperature waste heat in the HAT cycle system. Therefore, saturator temperature control occupies very important position in HAT cycle, and it is essential to study the control logic in saturator. Saturator temperature control is a control strategy that adjusts the water flow rate to fit the designed temperature. In this paper, the HAT cycle test rig of Shanghai Jiao Tong University is taken as research object and a complete HAT cycle model using global heat and mass transfer coefficient is built to analyze the influence of saturator temperature control on both steady-state and dynamic performance of HAT cycle system. The system efficiency increases by 0.071% after considering saturator temperature control on 75% load. The dynamic response of output power changes little, while the saturator component can achieve stable faster. The research in this paper can lay the foundation for operation and control of the HAT cycle demonstration plants.

The scientific reliability assessment is important for motor-driven compressor units, to evaluate the reliability standard, find the performance deficiency and provide references for operation and maintenance. Besides, it can reduce the test costs and accelerate the development process. Classical reliability assessment method is not suitable for the complex and reliable equipment, like motor-driven compressor units, because it is faced with the challenge of application of the multi-source information. A multi-source information based reliability assessment method is proposed in this paper. The fusion resources of this method consist of design information and operation & maintenance information. Component based and function based reliability assessment can be completed using component tests and design information. While quality evaluation based and life model based reliability assessment can be completed using operation information mainly. Finally, the unit reliability can be assessed by fusing the characteristic parameters above based on D-S evidence theory. A case study is conducted to evaluate a 20MW-class motor-driven compressor unit by this method. There are four information resources, component tests, design information, operation data and simulation data in the case. The unit reliability is assessed as 99.32% by the fusion of four reliability assessment results, acquired at different aspects. This assessment result is validated by the statistical assessment result based on long-term shutdown reports. This application points out the existing weakness in the motor-driven compressor unit and indicates directions for improving the design and operation technology. It reveals that this reliability assessment result can provide support for making a new production plan and strengthen construction of data network. Meanwhile, it has good expansibility, which may be used in more fields.

Since the late 1930s, gas turbine has begun to develop rapidly. To improve the economic and safety of gas turbine, new types were generated frequently by Original Equipment Manufacture (OEM). In this paper, a hybrid GRA-SVM prediction model is established to predict the main design parameters of new type gas turbines, based on the combination of Grey Relational Analysis (GRA) and Support Vector Machine (SVM). The parameters are classified into two types, system performance parameters reflecting market demands and technology development, and component performance parameters reflecting technology development and coupling connections. The regularity based on GRA determines the prediction order, then new type gas turbine parameters can be predicted with known system parameters. The model is verified by the application to SGT600. In this way, the evolution rule can be obtained with the development of gas turbine technology, and the improvement potential of several components can be predicted which will provide supports for overall performance design.

With the wide-scale use of mechanical equipment, more and more faults occur. At the same time, data deluge about the conditions of machines come into being with the development of sensor technology and information technology. It provides opportunities and challenges to solve the fault problems of mechanical equipment. Information fusion seems to be a useful solution, which is the process of integration of multiple data and knowledge representing the same object into a consistent, accurate, and useful representation. A novel information fusion model, with hybrid-type fusion architecture, is built in this paper. This model consists of data layer, feature layer and decision layer, based on a new Dempster/Shafer (D-S) evidence algorithm. After the data preprocessing based on event reasoning in data layer and feature layer, the information will be fused based on the new algorithm in feature layer and decision layer. Application of this information fusion model in fault diagnosis is beneficial in two aspects, diagnostic applicability and diagnostic accuracy. An effect can be caused by different faults. This information fusion model can solve this problem and increase the number of recognizable faults, to expand the range of fault diagnosis. Additionally, this model can overcome the uncertainty of information and equipment to increase diagnostic accuracy. Two case studies are implemented by this information fusion model to evaluate it. In the first case, fault probabilities calculated by different methods are adopted as inputs to diagnose a fault, which is quite different to be detected based on the information from a single system. The second case is about sensor fault diagnosis. Fault signals are planted into the measured parameters for the diagnostic system, to test the ability to consider the uncertainty of measured parameters. The case study result shows that the model can identify the fault more effectively and accurately. Meanwhile, it has good expansibility, which may be used in more fields.Copyright © 2016 by ASME