Machine Learning and Knowledge Discovery for Engineering Systems Health Management
Language: English
Published by Taylor & Francis Group, 2011
Series: Book 40 of 56 - Chapman & Hall/CRC Data Mining and Knowledge Discovery
- Hardcover
- New
Seller: Biblios, frankfurt am main, hessen, GermanyBiblios
AbeBooks seller since September 10, 2024
Condition: New
£ 226.85
Quantity: 3 available
Add to basketItem description from seller
Seller Inventory # 183141113
- Title
- Machine Learning and Knowledge Discovery for Engineering Systems Health Management
- Author
- Han Jiawei Srivastava Ashok N.
- Publisher
- Taylor & Francis Group
- Publication year
- 2011
- Condition
- New
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 1439841780
- ISBN 13
- 9781439841785
- Series
- Book 40 of 56: Chapman & Hall/CRC Data Mining and Knowledge Discovery
Machine Learning and Knowledge Discovery for Engineering Systems Health Management presents state-of-the-art tools and techniques for automatically detecting, diagnosing, and predicting the effects of adverse events in an engineered system. With contributions from many top authorities on the subject, this volume is the first to bring together the two areas of machine learning and systems health management.
Divided into three parts, the book explains how the fundamental algorithms and methods of both physics-based and data-driven approaches effectively address systems health management. The first part of the text describes data-driven methods for anomaly detection, diagnosis, and prognosis of massive data streams and associated performance metrics. It also illustrates the analysis of text reports using novel machine learning approaches that help detect and discriminate between failure modes. The second part focuses on physics-based methods for diagnostics and prognostics, exploring how these methods adapt to observed data. It covers physics-based, data-driven, and hybrid approaches to studying damage propagation and prognostics in composite materials and solid rocket motors. The third part discusses the use of machine learning and physics-based approaches in distributed data centers, aircraft engines, and embedded real-time software systems.
Reflecting the interdisciplinary nature of the field, this book shows how various machine learning and knowledge discovery techniques are used in the analysis of complex engineering systems. It emphasizes the importance of these techniques in managing the intricate interactions within and between the systems to maintain a high degree of reliability.
"Synopsis" may belong to another edition of this title.
About the Author
Ashok N. Srivastava is the Principal Scientist for Data Mining and Systems Health Management at NASA. Dr. Srivastava has received many awards, including the IEEE Computer Society Technical Achievement Award, the NASA Exceptional Achievement Medal, NASA Group Achievement Awards, the IBM Golden Circle Award, and a U.S. Department of Education Merit Fellowship. His current research focuses on the development of data mining algorithms for anomaly detection in massive data streams, kernel methods in machine learning, and text mining algorithms.
Jiawei Han is an Abel Bliss Professor of Computer Science at the University of Illinois. He is also the Director of the Information Network Academic Research Center, which is supported by the U.S. Army Research Lab. A fellow of ACM and IEEE, Dr. Han has received numerous honors, including IEEE W. Wallace McDowell Award, IEEE Computer Society Technical Achievement Award, ACM SIGKDD Innovation Award, IBM Faculty awards, and HP Innovation awards. His research interests include data mining, information network analysis, and database systems.
"About the title" may belong to another edition of this title.
Biblios
frankfurt am main, hessen, Germany
AbeBooks seller since September 10, 2024
Shipping rates from Germany to U.S.A.
| Item | 25 to 45 business days | 8 to 14 business days |
|---|---|---|
| First item | £ 8.55 | £ 16.07 |
Payment methods
Store description
Specialty
new books imported from india, uk, usaSeller's business information
Readingos GmbH
Kaiserstraße 47
Frankfurt am Main, Germany 60329
Terms of sale
We ship from Frankfurt
Right of withdrawal
If you are a consumer you can withdraw from the contract in accordance with the following. Consumer means any natural person who is acting for purposes which are outside his trade, business, craft or profession.
Information regarding the right of withdrawal
Statutory right to withdraw
You have the right to withdraw from this contract within 14 days without giving any reason.
The withdrawal period will expire after 14 days from the day on which you acquire, or a third party other than the carrier and indicated by you acquires, physical possession of the last good or the last lot or piece.
To exercise the right of withdrawal, electronically fill in and submit a clear statement on our website, under "My Purchases" in "My Account". We will communicate to you an acknowledgement of receipt of such a withdrawal on a durable medium (e.g. by e-mail) without delay.
To meet the withdrawal deadline, it is sufficient for you to send your communication concerning your exercise of the right of withdrawal before the withdrawal period has expired.
Effects of withdrawal
If you withdraw from this contract, we will reimburse to you all payments received from you, including the costs of delivery (except for the supplementary costs arising if you chose a type of delivery other than the least expensive type of standard delivery offered by us).
We may make a deduction from the reimbursement for loss in value of any goods supplied, if the loss is the result of unnecessary handling by you.
We will make the reimbursement without undue delay, and not later than 14 days after the day on which we are informed about your decision to withdraw from this contract.
We will make the reimbursement using the same means of payment as you used for the initial transaction, unless you have expressly agreed otherwise; in any event, you will not incur any fees as a result of such reimbursement.
We may withhold reimbursement until we have received the goods back, or you have supplied evidence of having sent back the goods, whichever is the earliest.
You shall send back the goods or hand them over to Biblios, frankfurt am main, Germany, without undue delay and in any event not later than 14 days from the day on which you communicate your withdrawal from this contract to us. The deadline is met if you send back the goods before the period of 14 days has expired. You will have to bear the direct cost of returning the goods. You are only liable for any diminished value of the goods resulting from the handling other than what is necessary to establish the nature, characteristics and functioning of the goods.
Exceptions to the right of withdrawal
The right of withdrawal does not apply to:
- The delivery of newspapers, journals or magazines with the exception of subscription contracts; and
- The supply of digital content which is not supplied on a tangible medium (e.g. on a CD or DVD) if you accepted when you placed your order that we could start to deliver it, and that you could not withdraw once delivery had started.