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Data-Driven Remaining Useful Life Prognosis Techniques: Stochastic Models, Methods and Applications (Springer Series in Reliability Engineering) by Si, Xiao-Sheng; Hu, Chang-Hua; Zhang, Zheng-Xin | Hardcover | Reliability Engineering
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Specifications
| author | Si, Xiao-Sheng; Hu, Chang-Hua; Zhang, Zheng-Xin |
| publisher | Springer-Verlag Berlin and Heidelberg GmbH & Co. K |
| isbn13 | 9783662540282 |
| isbn10 | 3662540282 |
| binding | hardcover |
| language | english |
| edition | 1st ed. 2017 |
| pages | 430 |
Product Description
Here on GlowMirror, you'll find Data-Driven Remaining Useful Life Prognosis Techniques: Stochastic Models, Methods and Applications (Springer Series in Reliability Engineering) by Si, Xiao-Sheng; Hu, Chang-Hua; Zhang, Zheng-Xin | Hardcover | Reliability Engineering filed under Business & Economics -- everything you need to know is covered below.
About this book
Data-Driven Remaining Useful Life Prognosis Techniques: Stochastic Models, Methods and Applications (Springer Series in Reliability Engineering) by Si, Xiao-Sheng; Hu, Chang-Hua; Zhang, Zheng-Xin published by Springer-Verlag Berlin and Heidelberg GmbH & Co. K.
Related topics
Brownian motion, Degradation process, Expectation maximization, Linear stochastic degrading systems, Markov process, Nonlinear stochastic degrading systems, Prognostics and health management, Remaining useful life prognosis.
Book Insights
What You'll Learn
- ·Strategic frameworks and business concepts in Data-Driven Remaining Useful Life Prognosis Techniques: Stochastic Models, Methods and Applications (Springer Series in Reliability Engineering) by Si, Xiao-Sheng; Hu, Chang-Hua; Zhang, Zheng-Xin | Hardcover | Reliability Engineering
- ·Case studies from successful companies and leaders
- ·Practical tools for decision-making and management
Who Should Read This
Business professionals, entrepreneurs, managers, and MBA students.
Key Highlights
- ·Brand new physical book delivered across India
- ·15-day hassle-free return policy
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Data-Driven Remaining Useful Life Prognosis Techniques: Stochastic Models, Methods and Applications (Springer Series in Reliability Engineering) by Si, Xiao-Sheng; Hu, Chang-Hua; Zhang, Zheng-Xin | Hardcover | Reliability Engineering is available at ₹15,651 on GlowMirror.
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Data-Driven Remaining Useful Life Prognosis Techniques: Stochastic Models, Methods and Applications (Springer Series in Reliability Engineering) by Si, Xiao-Sheng; Hu, Chang-Hua; Zhang, Zheng-Xin | Hardcover | Reliability Engineering is available in english on GlowMirror.
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Data-Driven Remaining Useful Life Prognosis Techniques: Stochastic Models, Methods and Applications (Springer Series in Reliability Engineering) by Si, Xiao-Sheng; Hu, Chang-Hua; Zhang, Zheng-Xin | Hardcover | Reliability Engineering is authored by Si, Xiao-Sheng; Hu, Chang-Hua; Zhang, Zheng-Xin.
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Data-Driven Remaining Useful Life Prognosis Techniques: Stochastic Models, Methods and Applications (Springer Series in Reliability Engineering) by Si, Xiao-Sheng; Hu, Chang-Hua; Zhang, Zheng-Xin | Hardcover | Reliability Engineering is published by Springer-Verlag Berlin and Heidelberg GmbH & Co. K.
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Data-Driven Remaining Useful Life Prognosis Techniques: Stochastic Models, Methods and Applications (Springer Series in Reliability Engineering) by Si, Xiao-Sheng; Hu, Chang-Hua; Zhang, Zheng-Xin | Hardcover | Reliability Engineering has 430 pages.
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At an average reading speed of 250 words per minute, reading Data-Driven Remaining Useful Life Prognosis Techniques: Stochastic Models, Methods and Applications (Springer Series in Reliability Engineering) by Si, Xiao-Sheng; Hu, Chang-Hua; Zhang, Zheng-Xin | Hardcover | Reliability Engineering takes approximately 11 hours.
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About this book
Data-Driven Remaining Useful Life Prognosis Techniques: Stochastic Models, Methods and Applications (Springer Series in Reliability Engineering) by Si, Xiao-Sheng; Hu, Chang-Hua; Zhang, Zheng-Xin published by Springer-Verlag Berlin and Heidelberg GmbH & Co. K.
Related topics
Brownia...
Is Data-Driven Remaining Useful Life Prognosis Techniques: Stochastic Models, Methods and Applications (Springer Series in Reliability Engineering) by Si, Xiao-Sheng; Hu, Chang-Hua; Zhang, Zheng-Xin | Hardcover | Reliability Engineering suitable for beginners?
Whether Data-Driven Remaining Useful Life Prognosis Techniques: Stochastic Models, Methods and Applications (Springer Series in Reliability Engineering) by Si, Xiao-Sheng; Hu, Chang-Hua; Zhang, Zheng-Xin | Hardcover | Reliability Engineering is suitable for beginners depends on your background in Business & Economics. Review the product description and specifications above for details on the target audience and difficulty level.
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Data-Driven Remaining Useful Life Prognosis Techniques: Stochastic Models, Methods and Applications (Springer Series in Reliability Engineering) by Si, Xiao-Sheng; Hu, Chang-Hua; Zhang, Zheng-Xin | Hardcover | Reliability Engineering is from the Springer-Verlag Berlin and Heidelberg GmbH & Co. K edition/year. Check the specifications section for full edition details.
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To order Data-Driven Remaining Useful Life Prognosis Techniques: Stochastic Models, Methods and Applications (Springer Series in Reliability Engineering) by Si, Xiao-Sheng; Hu, Chang-Hua; Zhang, Zheng-Xin | Hardcover | Reliability Engineering on GlowMirror, click the "Add to Cart" button on this page, proceed to checkout, and choose your preferred payment method — online payment or Cash on Delivery. Delivery takes 2–7 business days across India.
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Data-Driven Remaining Useful Life Prognosis Techniques: Stochastic Models, Methods and Applications (Springer Series in Reliability Engineering) by Si, Xiao-Sheng; Hu, Chang-Hua; Zhang, Zheng-Xin | Hardcover | Reliability Engineering listed on GlowMirror is a brand new book in good condition.
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Yes, GlowMirror delivers Data-Driven Remaining Useful Life Prognosis Techniques: Stochastic Models, Methods and Applications (Springer Series in Reliability Engineering) by Si, Xiao-Sheng; Hu, Chang-Hua; Zhang, Zheng-Xin | Hardcover | Reliability Engineering across India. Use the pincode checker on this page to verify delivery availability to your location.
