Advanced Concepts of Bearing Technology,: Rolling Bearing Analysis, Fifth Edition (Rolling Bearing Analysis, Fifth Edtion) 5th Edition

by Tedric A. Harris (Author),‎ Michael N. Kotzalas (Author)

4 customer reviews

ISBN-13: 978-0849371820

ISBN-10: 0849371821

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For the last four decades, Tedric Harris' Rolling Bearing Analysis has been the "bible" for engineers involved in rolling bearing technology. Why do so many students and practicing engineers rely on this book? The answer is simple: because of its complete coverage from low- to high-speed applications and full derivations of the underlying mathematics from a leader in the field. The fifth edition of this classic reference is divided conveniently into two volumes, each focused on a specialized area of bearing technology. This option allows you to select the coverage that is best suited to your needs.

The second of two books, Advanced Concepts of Bearing Technology steps up the level to more dynamic and complex loading, more extreme operating conditions, and higher-speed applications. The authors examine several topics that are unique to the book, including mathematical relationships for internal load distribution under conditions of high speed, combined radial, axial, and moment loading, as well as the effects of raceway and roller profiling. They also delve into the mathematical development of rolling element-raceway lubricant film thickness and contact friction, the stress-life method for calculating bearing fatigue endurance, and the effects of shaft and supporting structure flexure on bearing loading and deflection.

Advanced Concepts of Bearing Technology is the perfect aid for analyzing complex performance and fatigue-life phenomena in advanced applications.

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Essential Concepts of Bearing Technology, Fifth Edition (Rolling Bearing Analysis, Fifth Edtion) by Tedric A. Harris Hardcover $120.87
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Series: Rolling Bearing Analysis, Fifth Edtion
Hardcover: 368 pages
Publisher: CRC Press; 5th edition (October 9, 2006)
Language: English
ISBN-10: 0849371821
ISBN-13: 978-0849371820
Product Dimensions: 7 x 0.8 x 10 inches
Shipping Weight: 1.8 pounds (View shipping rates and policies)
Average Customer Review: 4 customer reviews
Amazon Best Sellers Rank: #2,000,988 in Books (See Top 100 in Books)
- #1034 in Books > Engineering & Transportation > Engineering > Industrial, Manufacturing & Operational Systems > Industrial Design
- #2567 in Books > Textbooks > Engineering > Mechanical Engineering
- #7074 in Books > Engineering & Transportation > Engineering > Mechanical

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Manlio D.

Multiscale anaysis of complex time series

May 25, 2012

Format: HardcoverVerified Purchase

After spending much time in looking for a good book about multiscale analysis of time series, I found this wonderful book by Jianbo Gao and his colleagues.
The book summarizes the current approaches to investigate complex time series analysis, with many applications to real world data. Many (old and new) theories and many (old and new) methods are presented, with particular attention to the most recent and powerful ones, already proposed in previous publications on scientific journals by Gao and colleagues.
The book is well written and can be used either as a reference book for teaching the corresponding topics or as a book of "scientific recipes" to investigate our strange complex world in the framework of time series analysis.
I strongly recommend this book for your personal library.

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verapan termvitkajohn

One Star

March 30, 2017

Format: HardcoverVerified Purchase

Too bad compared to previous edition

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Paul Rapp

Multiscale Analysis and Biomedical Research

January 2, 2012

Format: Hardcover

Scientists who have been following the papers of Jianbo Gao and his colleagues have welcomed this book. It brings together in a systematic presentation results that have been broadly distributed in the technical literature. Additionally, the book presents significant extensions to Gao's prior work.

Because Gao focuses on multiscale time series, these results will be particularly valuable in biomedical research. The 1960's and 1970's were decades of optimism in the mathematical biology community. It was thought that the successes of control theory in the analysis of technological systems, for example aircraft and chemical plants, would be equaled by successes in the quantitative analysis of physiological controls systems. Stated charitably, this optimistic expectation has been incompletely met. Many of us active in the field in the 1970's have been disappointed by the limited utilization of mathematical analysis in clinical practice forty years later. Several factors contributed to this disappointment. One was the inadequacy of the mathematical tools that could be brought to the process. Biological systems provide the definitive examples of highly integrated systems functioning at multiple time scales. Neurons function on a time scale of milliseconds. Circadian rhythms operate on at time scale of hours, and reproductive cycles occur on a time scale of weeks. But this is an integrated system. Each process interacts with faster and slower processes. The analysis of multiscale integration was an intractable challenge for classical control theory. Gao and his colleagues have attacked this problem head on with considerable success. Their stated objective: "The second vital strategy we envision is to develop measures that explicitly incorporate the concept of scale so that different behaviors of the data on varying scales can be simultaneously characterized by the same scale-dependent measure." This is precisely the kind of analyses we need in physiology.

To consider an important clinical example, heart rate variability analysis (the quantification of the variation in heart interbeat interval sequences) is a perfect example of the analytical challenge presented by physiological signals. The interval sequence is highly nonstationary and multiscale. It presents transitions between domains of oscillatory and power law behavior. Gao's scale dependent Lyapunov exponent is a powerful tool for analyzing this behavior. In an investigation of heart interbeat interval sequences obtained from healthy controls and from patients presenting congestive heart failure, calculation of the scale dependent Lyapunov exponents produced an impressive discrimination between the two populations.

The accessibility and clarity of presentation that characterized Gao's earlier papers is also present in this book.

Colleagues, given these mathematical advances, perhaps it's time to be optimistic again.

Paul Rapp, Silver Spring, Maryland

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Vincent A. Billock

Multiscale Analysis of Complex Time Series

February 8, 2008

Format: Hardcover

The need to find order in data is the scientist's corollary to the more catholic need to find order in the universe which scientists sample. In the 1970s Mandelbrot drew attention to a kind of geometric order, which he called fractal, that was more complex and often more useful than the Euclidean geometry that had dominated mathematics for more than two millennia. A side effect of this fractal geometry was a set of insights into analyzing complex time series. Part of Mandelbrot's charm is that his fractal mathematics are as powerful in dealing with stock prices and water accumulation behind dams as they were in describing the shape of a coastline or a mountain range. These insights dovetailed beautifully and synergistically with developments in nonlinear dynamics, leading to what is now known as complexity theory. Many good books on fractals and complexity theory have followed, but until recently it was difficult to find books that increased insight on understanding complex time series while never losing sight of the bigger picture: the complex universe that spawned the time series. Gao et al.'s new book "Multiscale Analysis of Complex Time Series" meets this standard.

One disclosure: Although I've never met Jianbo Gao in person, I once collaborated by email with Gao on a study of 1/f^b processes in visual perception. 1/f^b phenomena are becoming important in vision and are found in many different circumstances, ranging from natural images and visual textures (a focus of my own work) to time series analysis of multistable perception. I was therefore not surprised by the special focus in this book on 1/f^b processes. One thing that distinguishes this book from several others on complex time series is the care in which the authors weave the 1/f story from so many different theoretical and experimental strands. The only comparable treatment I can think of is Schroeder's "Fractals, Chaos and Power Laws", which is an easier and more delightful read, but much less useful for a working scientist trying to get his hands around real world data. If I'm enthusiastic about the book, it is perhaps for selfish reasons; reading it gave me a deeper understanding of some of the specialized problems I'm considering.

To develop understanding of multiscale 1/f-like time series, Gao et al. review fractals, chaos, probability theory, stochastic systems and the use of wavelets to probe at many different scales. They then delve more deeply into Levy motions, multifractals, multiplicative processes and bifurcation theory. The book is suitable as a textbook, with problems sets after each chapter, and datasets and software available on the Wiley website, but I suspect it will find more use as a monograph, for specialists pondering meaning in the multi-scaled datasets that their laboratories have just produced. The authors show sensitivity to a beginner's sensibilities, with "warm-up" exercises provided before plunging into more difficult material.

One topic I would like to have seen treated in depth in the book is stochastic resonance - a qualitative change in the behavior of a system when driven by noise. Stochastic resonance is a natural fit to this material: some kinds of SR are best driven by 1/f noise and some kinds of SR create 1/f behaviors. I suspect that SR's relative immaturity as a discipline makes it harder to treat authoritatively. This is a problem that time should solve, perhaps in time for a second edition.

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