This issue of the journal contains 67 papers from the conference, grouped under the following headings: plasma diagnostics;biocompatible materials;plasma asisted chemical vapor deposition;erosion and corrosion;polymer...
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This issue of the journal contains 67 papers from the conference, grouped under the following headings: plasma diagnostics;biocompatible materials;plasma asisted chemical vapor deposition;erosion and corrosion;polymers;processcontrol;ion beam technology--modification and mixing;ion beam assisted deposition;thin film characterization;sputtering;electron cyclotron resonance;amorphous semiconducting films;substrate treatment;electrical applications;and microelectronic applications. Some topics discussed by the papers are here cited as examples: Langmuir probe plasma diagnostics during TiNx deposition;biocompatibility of TiN preclinical and clinical investigations;high-thermal-resistant dielectric coating deposited by plasma polymerization;plasma immersion ion implantation of steels;applications of ion-beam-assisted deposition;structural study of titanium nitride coating interfaces related to plasma diagnostics;and insulation and passivation of three-dimensional substrates by plasma-CVD thin films using silicon-organic compounds. All papers are abstracted separately.
Researchers at the Westinghouse Science and Technology Center in Pittsburgh have developed a unique nondestructive inspection concept for the assessment of advanced composites and other hard-to-test materials. The tec...
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ISBN:
(纸本)093899462X
Researchers at the Westinghouse Science and Technology Center in Pittsburgh have developed a unique nondestructive inspection concept for the assessment of advanced composites and other hard-to-test materials. The technique known as 'Tagging' involves adding a small amount of tiny ferromagnetic particles to a material during manufacture such that the resulting electromagnetic signature can be related to material condition. In the most simple applications, the tagging signal can reveal important material parameters such as composition, material density, and the presence of voids. This 'embedded diagnostics' concept offers an optimum life cycle inspection capability that promises to enhance the reliability and serviceability of advanced material systems. The basic concepts underlying tagging for improved inspection are described along with the results of proof of principle experiments involving a wide range of potential applications including adhesive joining and composite qualification. In addition to inspection options, the applicability of tagging to manufacturing processcontrol is also discussed.
Taking advantage of the improvements made in computer tools, Electricite de France is undertaking the overall renovation of its power control centers in order to optimize the operation of the Generation - Transmission...
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Taking advantage of the improvements made in computer tools, Electricite de France is undertaking the overall renovation of its power control centers in order to optimize the operation of the Generation - Transmission System from the savings and security point of view. The future National control System of EDF will provide the operator with a far more accurate view of the power system, which implies increased data flow. This data flow is far in excess of the human capacity of integration. Alarm processing, which aim is to detect and display any substantial changes in Generation - Transmission System, is therefore a basic element of the future control system. In view of this, the existing principle of management in which the information (elementary teleinformation) is displayed in a time order, can no longer be relevant. The alarm processing function has been set into a consistent whole with different synthesis levels in order to show the operator only the significant events. This process has needed an ergonomic analysis of operator activity. From the results of this analysis we have defined the processing operations to assist the operator in understanding the situation and when possible to aid fonctions for decision making. The first level is event identification which takes into account unprocessed data from the system (telesignalling, for example), plus results of developed models (voltage collapse indicator, security analysis,...), and also diagnostics transmitted by the Regional control Centers. A limited number of well-identified 'real-time' disturbed conditions, require a rapid intervention processed by a more developed synthesis level. This level contains an explanatory search using the event identification and an operator aid function. Other operating conditions are linked to less synthetic and more conventional alarms, which are more classically list-managed. The specified management of the different levels, incorporates the lowest levels in the more develo
A three-level process monitoring and diagnostics system has been developed for a plasma etching cell. The physical and logical layout of this system is designed to co-exist with a proposed hierarchical cell control sy...
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A three-level process monitoring and diagnostics system has been developed for a plasma etching cell. The physical and logical layout of this system is designed to co-exist with a proposed hierarchical cell control system. The cell control system design incorporates generic cell controller concepts and some of the SEMI Generic Equipment Model (GEM) specifications. At the lowest level of the system, equipment and process parameter monitoring is achieved. An AT-compatible computer serves as an information collection/router agent as well as an interpreter (gateway) between the SECS/RS-232 formatted messages received from the equipment controller system and the higher-level expert systems commands. At the top of the hierarchy, an expert systems package that resides on a SUN SPARC station and operates on an X-Windows environment collects information on the equipment and process from the AT compatible. The many system capabilities that result include: (1) pseudo real-time equipment and process monitoring, (2) equipment diagnostics and alarm reporting, (3) alarm logging; and (4) real-time equipment control.< >
A real-time fault diagnostics system that is applicable for diagnosing large-scale plants is described. It uses a multiple aspect model of the plant including the hierarchical process model, the hierarchical component...
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A real-time fault diagnostics system that is applicable for diagnosing large-scale plants is described. It uses a multiple aspect model of the plant including the hierarchical process model, the hierarchical component model, and the hierarchical fault model (HFM). HFM represents the spatial and temporal aspects of faulty behavior in the form of a hierarchical fault propagation digraph. The reasoning algorithm is based on the structural and temporal constraint enforcement, and is migrated to lower levels of HFM hierarchy. It is able to guarantee response times, perform nonmonotonic and temporal reasoning, operate continuously, accept asynchronous data, generate requests, perform time and diagnostic resolution tradeoffs, and diagnose single and most multiple fault cases.< >
The general technical diagnostic of water distribution systems tends to become necessary in providing adequate planning of rehabilitation, reinforcement and extension as well as optimisation of the system operation. S...
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The general technical diagnostic of water distribution systems tends to become necessary in providing adequate planning of rehabilitation, reinforcement and extension as well as optimisation of the system operation. Suitably calibrated models together with efficient software are privileged tools for this purpose, by permetting the verification of the network structure and the hydraulic behaviour, the modelling process leads to the detection of weak points and to the definition of detailed diagnostic and rehabilitation operations. New applications of simulations softwares now extend to water quality analysis, both normal and exceptional circumstances. Practical examples are shown as below.
The patterns exhibited in the recent history of the process input variable can indicate impending useful excitation data. A general method for detecting excitation based on an analysis of the process input is detailed...
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The patterns exhibited in the recent history of the process input variable can indicate impending useful excitation data. A general method for detecting excitation based on an analysis of the process input is detailed, along with the use of this data to update an adaptive controller internal model. The method, applicable to a number of adaptive strategies, is demonstrated using an adaptive PI controller with Smith predictor. Because unmeasured disturbances can occur during excitation events and corrupt the resultant model, model gains are collected as they are computed and used in an automated procedure to generate a continuous gain schedule function. This function provides information for measuring the success of future model fits, even as operating regime changes for processes with nonlinear gain characteristics, and provides an adaptation mechanism for controlling such processes. To minimize the expertise required to implement these methods, an automated startup procedure is detailed. The methods are demonstrated on two simulated reactor processes that possess nonlinear and s
The following three problems are addressed: 1) multiple-input single-output heteroassociative networks for signal validation for distributed sensor arrays;2) multiple-input multiple-output autoassociative networks for...
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The following three problems are addressed: 1) multiple-input single-output heteroassociative networks for signal validation for distributed sensor arrays;2) multiple-input multiple-output autoassociative networks for plant-wide monitoring of a set of process variables for diagnostics;and 3) artificial neural networks for online estimation of chemical composition from spectroscopy data. Both static and dynamic forms of the backpropagation network (BPN) have been developed and applied to the solution of these problems. Chemometric data from Raman FT (Fourier transform) spectroscopy was used to estimate chemical sample composition. Several features of network training and implementations are presented, including adaptive updating of the sigmoidal threshold function during training, an optimal choice of hidden layer nodes using Shannon's information theory approach, and automatic scaling of network inputs and outputs for data encoding and decoding. The details of the development and implementation of the multilayer perceptrons and applications to industrial problems are highlighted.
The performance of a state and input observer is evaluated on a DC spindle drive system designed and built for this purpose. The observer estimates the spindle drive load torque (a disturbance input) based on easy-to-...
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The performance of a state and input observer is evaluated on a DC spindle drive system designed and built for this purpose. The observer estimates the spindle drive load torque (a disturbance input) based on easy-to-access signals: armature voltage, current, and speed. The results show that the bandwidth and signal-to-noise ratio of the load torque estimate are as good as those obtained with an inline torque transducer for the conditions tested. The use of these observers for detecting machine tool malfunctions and abnormal process conditions related to load torque (such as tool breakage, tool wear, slide stick/slip, and bearing preload) is discussed. State and input observers appear to have good potential for machine diagnostics.
A novel associative memory (AM) knowledge base for real-time diagnostics and high-level control functions for complex large-scale dynamic systems is presented. The proposed AM architecture is called ASDM as it is an a...
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A novel associative memory (AM) knowledge base for real-time diagnostics and high-level control functions for complex large-scale dynamic systems is presented. The proposed AM architecture is called ASDM as it is an adaptive architecture based on the sparse distributed memory (SDM) first introduced by Kanerva. ASDM is proposed to overcome many limitations of SDM, while keeping most of the advantages of the original SDM. The new model is adaptive since the memory cells are renamed according to a learning/storing process. The analysis of the best match problem can be carried out in a deterministic setting. Also introduced is the concept of time-varying intensity of memory, and generalized metrics are used for determining distance between two data objects. One major application of this ASDM is that of a neural expert system. With the ASDM, the machine can 'learn' rules on top of the knowledge acquired from human experts.
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