Diagnostics and Prognostics: SVMBased Diagnostics and Prognostics for Rotating Systems,Used

Diagnostics and Prognostics: SVMBased Diagnostics and Prognostics for Rotating Systems,Used

In Stock
SKU: DADAX3847307436
Brand: LAP Lambert Academic Publishing
Condition: New
Regular price$115.19
Quantity
Add to wishlist
Add to compare

Sold by Ergodebooks, an authorized reseller.

Returns accepted within 30 days | support@ergodebooks.com

Verified
Shipping Information
  • Free Standard Shipping — United States only
  • Processing Time: 1–3 business days
  • Estimated Delivery: 3–5 business days after dispatch
  • Double-boxed, fully insured & discreetly packaged
  • Tracking number sent via email once dispatched
  • Orders over $250 require signature upon delivery. Taxes calculated at checkout.
Returns & Refund

Returns accepted within 30 days of delivery.

Damaged or Defective Item

Free return shipping + replacement or full refund

Wrong Item Received

Free return shipping + replacement or full refund

Change of Mind

Return shipping at customer's expense · 25% restocking fee applies

All returns require a Return Authorization (RA) number before sending.

To initiate a return, contact us:

support@ergodebooks.com +1 (281) 738-1050
View Full Return & Refund Policy
Payment Option
Payment Methods

Help

If you have any questions, you are always welcome to contact us. We'll get back to you as soon as possible, withing 24 hours on weekdays.

Customer service

All questions about your order, return and delivery must be sent to our customer service team by e-mail at yourstore@yourdomain.com

Sale & Press

If you are interested in selling our products, need more information about our brand or wish to make a collaboration, please contact us at press@yourdomain.com

In recent decades, conditionbased maintenance (CBM) is acknowledged a costeffective and widely used maintenance program for engineering systems. Diagnostics and prognostics are critical components of CBM responsible for offering information about present and future system conditions. These two components are respectively an integrated process covering several aspects that are essential for successful implementations of diagnostics and prognostics. For diagnostics, data to be used should be clean and useful. Data cleaning can provide clean data by removing outliers caused by noise, while feature selection can select useful characteristic features for fault classification. For prognostics, noise may appear in condition indicator values. Using such noisy values may result in unreliable predictions for prognostics. A method is thus demanded to provide predictions without noise effects. Support vector machine (SVM), a machine learning method, is recognized having good generalization ability and an effective tool for classification and prediction needed by diagnostics and prognostics. This book explores the potentials of SVM for addressing above problems in diagnostics and prognostics.

⚠️ WARNING (California Proposition 65):

This product may contain chemicals known to the State of California to cause cancer, birth defects, or other reproductive harm.

For more information, please visit www.P65Warnings.ca.gov.

Recently Viewed