Dengji Zhou

An Information Fusion Model Based on Dempster–Shafer Evidence Theory for Equipment Diagnosis

An abnormal operating effect can be caused by different faults, and a fault can cause different abnormal effects. An information fusion model, with hybrid-type fusion frame, is built in this paper,...

A Dynamic RCM Analysis Method for Natural Gas Compressor Station Based on Diagnostic and Prognostic Technology

The availability requirement of natural gas compressors is high. Thus, current maintenance architecture, combined periodical maintenance and simple condition based maintenance, should be improved. In this paper, a new maintenance method, Dynamic Reliability-centered Maintenance (DRCM), is proposed for equipment management. It aims at expanding the application of Reliability-centered Maintenance (RCM) in maintenance schedule making to preventive maintenance decision making online and seems suitable for maintenance of natural gas compressor stations. A decision diagram and a maintenance model are developed for DRCM. Then three application cases of DRCM for actual natural gas compressor stations are shown to validate this new method.Copyright

Parametric Analysis on Hybrid System of Solid Oxide Fuel Cell and Micro Gas Turbine With CO2 Capture

The solid oxide fuel cell–micro gas turbine hybrid system with CO2 capture seems to be a prospective system with high efficiency and low emissions. Three hybrid systems with/without CO2 capture are designed and simulated based on the IPSEPro simulation platform. The performance on the design point shows that case 2 is a better one, whose system efficiency is 59% and CO2 capture rate is 99%; thus, case 2 is suitable to build a quasi-zero carbon emission plant. However, case 3 is more suitable to rebuild an existing plant. Then the off-design point performance and the effect of the capture rate on the system performance of cases 2 and 3 are investigated. The suggested capture rate for cases 2 and 3 is given based on the result, taking both economic factors and carbon emissions into consideration.

The Dynamic Modeling and Simulation on a Shell Gasifier

As the conventional model of gasifier, the model based on the lumped slag layer method can only give the lumped parameters in gasifier. However, the distribution characteristics of the various parameters in gasifier is obvious. To remedy the defect in conventional model, this paper presents a compartment-slag layer model in the modeling of the shell gasifier, which takes the advantage of the lumped slag layer method and the compartment method used in the simulation of the CWS gasifier. The simulation results were validated by the experimental data. The dynamic results show that the new model can reflect the response of some key parameters. This could provide references for the future investigation of the dynamic characteristics of the IGCC system.Copyright

A Novel Information Fusion Model Based on D-S Evidence Theory for Equipment Diagnosis

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

Performance Analysis of a Compressed Humid Air Energy Storage System

With the growing need for the use of electricity, power plants sometimes cannot generate enough power during the high demand periods. Thus various methods are introduced to solve this situation. Compressed air energy storage (CAES) technology seems to be a good solution to both peaking power demand and intermittent energy utilization transformed from renewable energy source like wind energy. Utilization of heat generated from the air compression process is a crucial problem of this technology. A compressed air energy storage system, with humid air as working fluid, is designed in this paper. In this system, heat of compressing air is transformed to the latent heat of water vapour, decreasing the power consumption of compressor and increasing energy generated per volume of storage. A Compressed Humid Air Energy Storage (CHAES) system model is established in this paper to simulate the performance of this system. Then the performance of this new system is evaluated by comparison to conventional CAES system, based on the simulation result. The result of this paper confirm the growing interest to CAES as a solution to peaking power demand and intermittent energy utilization, and indicates that CHAES system, as a great improvement of CAES system, has huge potential in the future.Copyright

A New Gas Path Fault Diagnostic Method of Gas Turbine Based on Support Vector Machine

As a crucial section of gas turbine maintenance decision-making process, to date, gas path fault diagnostic has gained a lot of attention. However, model-based diagnostic methods, like non-linear gas path analysis (GPA) and genetic algorithms, need an accurate gas turbine model, and diagnostic methods without gas turbine model, like artificial neural networks, need a large number of experimental data. Both are difficult to gain. Support vector machine (SVM), a novel computational learning method with excellent performance, seems to be a good choice for gas path fault diagnostic of gas turbine without engine model. In this paper, SVM is employed to diagnose a deteriorated gas turbine. And the diagnostic result of SVM is compared to the result of artificial neural networks. The comparing result confirms that SVM has an obvious advantage over artificial neural networks method based on a small sample of data, and can be employed to gas path fault diagnostic of gas turbine. Additionally, SVM with radial basis kernel function is the best choice for gas turbine gas path fault diagnostic based on small sample.Copyright