Dynamic Evaluation Model and Application Methods for Engineering Machine Maintenance Quality

It is an important content of equipment management to keep the engineering machine well. Based on the theory of component technology and grey related algorithm arithmetic, the requirements and procedures of engineering machine maintenance predicting process are analyzed, and a support object evaluation system is provided. The qualitative and quantitative indexes of evaluating process are fully taken into consideration to provide scientific methods and ways for proper evaluation and decision.

Subsurface analytics: Contribution of artificial intelligence and machine learning to reservoir engineering, reservoir modeling, and reservoir management

Traditional Numerical Reservoir Simulation has been contributing to the oil and gas industry for decades.The current state of this technology is the result of decades of research and development by a large number of engineers and scientists.Starting in the late 1960s and early 1970s,advances in computer hardware along with development and adaptation of clever algorithms resulted in a paradigm shift in reservoir studies moving them from simplified analogs and analytical solution methods to more mathematically robust computational and numerical solution models.

Progress of remanufacturing engineering and future technology expectation

After development for decades, abroad remanufacturing has formed a complete industrial system. At present, the research emphases are on marking logistics management and market cultivation theory of remanufacturing products, and so on. The Chinese remanufacturing starts fairly late. After 10 years of development, it formed a remanufacturing mode with Chinese characteristics that is sustained by high-tech industries, using the surface engineering technology to restore the size and improve properties, and combining manufacturing, study and research together. The remanufacturing mode is not only circular but also economic. With the development of science and technology, future remanufacturing technology will break the previous limits, explore and understand the limits of micro machining. It will carry out the waste product remanufacturing in the micro-nano scale, and extend the remanufacturing industry to a more broad space.

An adaptive turbo-shaft engine modeling method based on PS and MRR-LSSVR algorithms

In order to establish an adaptive turbo-shaft engine model with high accuracy, a new modeling method based on parameter selection （PS） algorithm and multi-input multi-output recursive reduced least square support vector regression （MRR-LSSVR） machine is proposed. Firstly, the PS algorithm is designed to choose the most reasonable inputs of the adaptive module. During this process, a wrapper criterion based on least square support vector regression （LSSVR） machine is adopted, which can not only reduce computational complexity but also enhance generalization performance. Secondly, with the input variables determined by the PS algorithm, a mapping model of engine parameter estimation is trained off-line using MRR-LSSVR, which has a satisfying accuracy within 5＆. Finally, based on a numerical simulation platform of an integrated helicopter/ turbo-shaft engine system, an adaptive turbo-shaft engine model is developed and tested in a certain flight envelope. Under the condition of single or multiple engine components being degraded, many simulation experiments are carried out, and the simulation results show the effectiveness and validity of the proposed adaptive modeling method.