A tool-wear monitoring system for metal turning operations is presented based on the combinative application of fuzzy logic and unsupervised neural network. A group of self-organizing map （SOM） neural networks is es...A tool-wear monitoring system for metal turning operations is presented based on the combinative application of fuzzy logic and unsupervised neural network. A group of self-organizing map （SOM） neural networks is established based on the typical cutting condition combinations, and each of networks is corresponding to a typical cutting condition. For a specifie cutting condition, the fuzzy logic method is used to select an optimum trained SOM network. The proposed monitoring system, ealled the Fuzzy-SOM-TWC, is used to classify tool states based on the in-time measurement of force, aeoustic emission（AE）, and motor eurrent signals. An approximate 98%--100% correct classification of tool-wear status is obtained by testing the system with a series data samples under freely selected cutting conditions.展开更多
This paper presents a case study about a condition monitoring project for asset management implementing an advanced decision process for maintenance and asset replacement in the Qatar electricity distribution network....This paper presents a case study about a condition monitoring project for asset management implementing an advanced decision process for maintenance and asset replacement in the Qatar electricity distribution network. It describes the principles used to produce an assessment of the health of the entire fleet of assets, together with the concepts retained to prioritize the interventions on the distribution network equipment. The paper goes through the actual steps taken for the preparation and execution mode of the overall project. It covers the following project phases： definition of the problem ＆ business objectives, process definition and preparation tasks, presentation of solutions and execution phase. The project being still in the implementation phase, the conclusions are preliminary but already demonstrate concrete and tangible benefits.展开更多
Wind energy is considered a hope in future as a clean and sustainable energy, as can be seen by the growing number of wind farms installed all over the world. With the huge proliferation of wind farms, as an alternati...Wind energy is considered a hope in future as a clean and sustainable energy, as can be seen by the growing number of wind farms installed all over the world. With the huge proliferation of wind farms, as an alternative to the traditional fossil power generation, the economic issues dictate the necessity of monitoring systems to optimize the availability and profits. The relatively high cost of operation and maintenance associated to wind power is a major issue. Wind turbines are most of the time located in remote areas or offshore and these factors increase the referred operation and maintenance costs. Good maintenance strategies are needed to increase the health management of wind turbines. The objective of this paper is to show the application of neural networks to analyze all the wind turbine information to identify possible future failures, based on previous information of the turbine.展开更多
基金Supported by the International Science and Technology Cooperation Project(2008DFA71750)the National Key Technology R&D Program(2008BAF32B00)~~
文摘A tool-wear monitoring system for metal turning operations is presented based on the combinative application of fuzzy logic and unsupervised neural network. A group of self-organizing map （SOM） neural networks is established based on the typical cutting condition combinations, and each of networks is corresponding to a typical cutting condition. For a specifie cutting condition, the fuzzy logic method is used to select an optimum trained SOM network. The proposed monitoring system, ealled the Fuzzy-SOM-TWC, is used to classify tool states based on the in-time measurement of force, aeoustic emission（AE）, and motor eurrent signals. An approximate 98%--100% correct classification of tool-wear status is obtained by testing the system with a series data samples under freely selected cutting conditions.
文摘This paper presents a case study about a condition monitoring project for asset management implementing an advanced decision process for maintenance and asset replacement in the Qatar electricity distribution network. It describes the principles used to produce an assessment of the health of the entire fleet of assets, together with the concepts retained to prioritize the interventions on the distribution network equipment. The paper goes through the actual steps taken for the preparation and execution mode of the overall project. It covers the following project phases： definition of the problem ＆ business objectives, process definition and preparation tasks, presentation of solutions and execution phase. The project being still in the implementation phase, the conclusions are preliminary but already demonstrate concrete and tangible benefits.
文摘Wind energy is considered a hope in future as a clean and sustainable energy, as can be seen by the growing number of wind farms installed all over the world. With the huge proliferation of wind farms, as an alternative to the traditional fossil power generation, the economic issues dictate the necessity of monitoring systems to optimize the availability and profits. The relatively high cost of operation and maintenance associated to wind power is a major issue. Wind turbines are most of the time located in remote areas or offshore and these factors increase the referred operation and maintenance costs. Good maintenance strategies are needed to increase the health management of wind turbines. The objective of this paper is to show the application of neural networks to analyze all the wind turbine information to identify possible future failures, based on previous information of the turbine.
