Artificial Intelligence Tools

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A01=Diego Galar Pascual
advanced AI condition monitoring applications
Age Group_Uncategorized
Age Group_Uncategorized
algorithms
anomaly
Anomaly Detection
Anomaly Detection Techniques
Author_Diego Galar Pascual
automatic-update
Bayesian Networks
Category1=Non-Fiction
Category=KJMV5
Category=TBD
Category=TJFM
Cell Cx
Challenges of Condition Monitoring using AI techniques
clustering
Collective Anomalies
condition
Condition monitoring: Available techniques
Context Aware Applications
Context Aware Systems
Contextual Anomalies
COP=United States
CRF Model
Data Set
Delivery_Delivery within 10-20 working days
detection
eq_bestseller
eq_business-finance-law
eq_isMigrated=0
eq_isMigrated=2
eq_nobargain
eq_non-fiction
fault diagnosis systems
functions
Fuzzy Controller
Fuzzy Observers
Fuzzy Sets
Hinge Loss Function
Input and output data
Knowledge Acquisition
Language_English
LOF
machine learning models
Massive field data collection: Issues and challenges
membership
Membership Functions
monitoring
Nearest Neighbor Based Techniques
Oil Analysis
outlier
Outlier Detection
PA=Available
Point Anomalies
predictive maintenance methods
Price_€100 and above
PS=Active
Radial Basis Function
Radial Basis Function Network
Rare Class
Semisupervised Learning
sensor data analysis
softlaunch
statistical signal processing
SVM Classification
techniques
uncertainty quantification

Product details

  • ISBN 9781466584051
  • Weight: 954g
  • Dimensions: 156 x 234mm
  • Publication Date: 22 Apr 2015
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
  • Language: English
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Artificial Intelligence Tools: Decision Support Systems in Condition Monitoring and Diagnosis discusses various white- and black-box approaches to fault diagnosis in condition monitoring (CM). This indispensable resource:

  • Addresses nearest-neighbor-based, clustering-based, statistical, and information theory-based techniques
  • Considers the merits of each technique as well as the issues associated with real-life application
  • Covers classification methods, from neural networks to Bayesian and support vector machines
  • Proposes fuzzy logic to explain the uncertainties associated with diagnostic processes
  • Provides data sets, sample signals, and MATLAB® code for algorithm testing

Artificial Intelligence Tools: Decision Support Systems in Condition Monitoring and Diagnosis delivers a thorough evaluation of the latest AI tools for CM, describing the most common fault diagnosis techniques used and the data acquired when these techniques are applied.

Diego Galar Pascual holds an M.Sc and Ph.D from Saragossa University, Zaragoza, Spain. He has been a professor at several universities, including Saragossa University and the European University of Madrid, Spain. At Saragossa University, he also served as director of academic innovation, director of international relations, pro-vice-chancellor, and senior researcher in the Aragon Institute of Engineering Research (i3A). In addition, he has been the technological director and CBM manager of international firms such as Volvo, Saab, Boliden, Scania, Tetrapak, Heinz, and Atlas Copco. Currently, he is the professor of condition monitoring in the Division of Operation and Maintenance of the Luleå University of Technology (LTU), Sweden, where he also is involved with the LTU-SKF University Technology Center. Widely published, Dr. Galar Pascual serves as a visiting professor at the University of Valencia (Spain), Polytechnic of Braganza (Portugal), Valley University (Mexico), Sunderland University (UK), University of Maryland (College Park, USA), and Northern Illinois University (DeKalb, USA).

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