Hasil Pencarian  ::  Simpan CSV :: Kembali

Abstrak :
Condition monitoring of machines in non-stationary operations (CMMNO) can be seen as the major challenge for research in the field of machinery diagnostics. Condition monitoring of machines in non-stationary operations is the title of the presented book and the title of the Conference held in Hammamet - Tunisia March 26 ? 28, 2012. It is the second conference under this title, first took place in Wroclaw - Poland , March 2011. The subject CMMNO comes directly from industry needs and observation of real objects. Most monitored and diagnosed objects used in industry works in non-stationary operations condition. The non-stationary operations come from fulfillment of machinery tasks, for which they are designed for. All machinery used in different kind of mines, transport systems, vehicles like: cars, buses etc, helicopters, ships and battleships and so on work in non-stationary operations. The papers included in the book are shaped by the organizing board of the conference and authors of the papers. The papers are divided into five sections, namely : Condition monitoring of machines in non-stationary operations Modeling of dynamics and fault in systems Signal processing and Pattern recognition Monitoring and diagnostic systems Noise and vibration of machines The presented book gives the back ground to the main objective of the CMMNO 2012 conference that is to bring together scientific community to discuss the major advances in the field of machinery condition monitoring in non-stationary conditions.

Abstrak :
This book promotes the various approaches gathered under the umbrella of computational intelligence to show how condition monitoring can be used to avoid equipment failures and lengthen its useful life, minimize downtime and reduce maintenance costs. The text introduces various signal-processing and pre-processing techniques, wavelets and principal component analysis, for example, together with their uses in condition monitoring and details the development of effective feature extraction techniques classified into frequency, time-frequency- and time-domain analysis. Data generated by these techniques can then be used for condition classification employing tools such as, fuzzy systems, rough and neuro-rough sets, neural and Bayesian networks, hidden Markov and Gaussian mixture models, and support vector machines.

Abstrak :
Condition monitoring of engineering plants has increased in importance as engineering processes are automated and manpower is reduced. However, electrical machinery receives attention only at infrequent intervals when plant is shut down and the application of protective relays to machines has also reduced operator surveillance. A first edition of Condition Monitoring of Electrical Machines, written by Tavner and Penman, was published in 1987. The economics of industry have now changed, as a result of the privatisation and deregulation of the energy industry, placing emphasis on the importance of reliable operation of plant, throughout the whole life cycle, regardless of first cost. The availability of advanced electronics and software in powerful instrumentation, computers, and digital signal processors (DSP) has simplified our ability to instrument and analyse machinery. As a result condition monitoring is now being applied to a wider range of systems from fault-tolerant drives of a few hundred watts in the aerospace industry, to machinery of a few hundred megawatts in major capital plant. In this new book the original authors have been joined by Ran, an expert in power electronics and control, and Sedding, an expert in the monitoring of electrical insulation systems. Together the authors have revised and expanded the earlier book, merging their own experience with that of machine analysts to bring it up to date.