Condition Monitoring With Computational Intelligence Methods For Mechanical And Electrical Systems: A Hands-On Guide To Real-World Diagnostics And Practical Applications
✔️ English edition, 2012; single copy hands-on diagnostic guide for systems
Condition Monitoring Using Computational Intelligence Methods introduces how smart data analysis powers real-world diagnostics in mechanical and electrical equipment. It covers signal-processing and pre-processing techniques, wavelets and principal component analysis (PCA), and explains feature extraction across frequency-, time-frequency-, and time-domain analyses. Data from these methods feed into condition classification using fuzzy systems, rough and neuro-rough sets, neural and Bayesian networks, hidden Markov models, Gaussian mixture models, and support vector machines.
✅ Author: Tshilidzi Marwala
✅ Edition: 2012
✅ Language: English
✅ Focus: condition monitoring via computational intelligence; signal processing, wavelets, PCA
✅ Techniques: feature extraction in frequency-, time-frequency-, and time-domain analysis
✅ Classifiers: fuzzy systems; rough and neuro-rough sets; neural & Bayesian networks; HMM; GMM; SVM
✅ Benefits: reduces downtime and maintenance costs, extends equipment life
✅ Audience: engineers, technicians, students in mechanical and electrical systems
💡 What is condition monitoring with computational intelligence used for in industrial systems? This handy approach helps prevent faults and extend equipment life.
- Improves uptime and reliability
- Enables predictive maintenance planning to reduce downtime
- Supports data-driven fault diagnosis and prognosis