Ofsea English Editing

 

客户原文中、英文混杂，英文无任何语法，部分中、英文内容混乱，因而需要与客户做多次的沟通以帮助其理清想法。因为该文章发表于Proceedings Annual Reliability and Maintainability Symposium (2009)，版权归IEEE，海戴在此只公开其中的SUMMARY & CONCLUSIONS部分，作为示例。

 

下文为海戴的留美博士编辑部分翻译加部分修改润色的第二稿：

 

SUMMARY & CONCLUSIONS

Industrial process systems that are different from discrete manufacturing systems are composed of many interlocking units that couple with each other tightly. Hence, whenever an arbitrary small part of a unit functions poorly, it would influence or cause sufficiently the whole system to function abnormally. Under this circumstance, how to determine or locate abnormal or failed elements is a very formidable task. By introducing the Bayesian Network into the tracking of faults in industrial process systems, a new method on fault source tracing based on Bayesian Networks was proposed, and a model was set up correspondingly. To guarantee the accuracy of this modeling process, a series of formalized rules by which must be abided were defined, and subsequently a mapping between the Bayesian Network and the elements of the model was formed. Furthermore, to make better use of this new Bayesian Network model, the probability characteristics of it and the problem-solving method exploiting it were expanded and explored in depth, and a complete process of reasoning for fault source tracing was illustrated. In the end, an example was provided to demonstrate the modeling process, and to verify the practicality and validity of this model in trailing abnormalities in chemical systems.

 

下文为海戴的留美博士编辑部分翻译加部分修改润色的第十三稿，也是这一环节的终稿。可以看到该段的第一句的意思被海戴的编辑完全修正了。

 

SUMMARY & CONCLUSIONS

Process systems are different from discrete manufacturing systems in that they are composed of many interlocking subsystems that consist of various tightly coupling units. Hence, whenever a small unit of a subsystem functions badly, it could influence or cause the whole system to function abnormally.  Under this circumstance, how to determine or locate abnormal or failed units will be a very formidable task.  By introducing Bayesian networks into the tracking of fault source in process systems, a new method on fault source tracing is proposed, and a model is set up correspondingly.  To guarantee the accuracy of the modeling process, a series of rules by which must be abided are defined.  And, a mapping between the Bayesian network and the units of a process system is developed.  Furthermore, to make full use of this new Bayesian network model, its probability characteristics and the problem-solving method exploiting it are expanded and investigated in depth.  Additionally, a complete reasoning for the fault source tracing based on the Bayesian network is described.  Finally, an example is provided to demonstrate the modeling and reasoning processes, and therefore verifies the practicality and validity of this model in trailing abnormalities in process systems.

 

下文为海戴的美国科学家编辑的修改润色终稿，其中红色字体为修改润色痕迹。该稿完美无缺，顺利被IEEE的Proceedings Annual Reliability and Maintainability Symposium (2009)所录用。

SUMMARY & CONCLUSIONS

Process systems are different from discrete manufacturing systems in that they are composed of many interlocking subsystems that consist of various tightly coupled units.  Hence, whenever a small unit of a subsystem functions badly, it could influence or cause the whole system to function abnormally.  Under this circumstance, how to locate abnormal or failed units will be a very formidable task.  By introducing Bayesian networks into the tracking of fault sources in process systems, a new method of fault source tracing is proposed, and a model is set up accordingly.  To guarantee the accuracy of the modeling process, a series of rules which must be abided by are defined.  And, a mapping between the Bayesian network and the units of a process system is developed.  Furthermore, to make full use of this new Bayesian network model, its probability characteristics and the problem-solving method exploiting it are expanded and investigated in depth.  Additionally, a complete reasoning for the fault source tracing based on the Bayesian network is described.  Finally, an example is provided to demonstrate the modeling and reasoning processes, and thereby verifies the practicality and validity of this model in tracing abnormalities in process systems.

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