An Approach to Reducing Input Parameter Volume for Fault Classifiers

Keyphrases

• Classification Accuracy 100%
• Fault Classifier 100%
• Nave Bayes Classifier 100%
• Classification Model 50%
• Condition Monitoring 50%
• Diagnostic Features 50%
• Highly Sensitive 50%
• Heterogeneous Groups 50%
• Success Rate 50%
• Confirmatory Factor Analysis 50%
• Generic Parameters 50%
• Rotating Machinery 50%
• Modeling Algorithm 50%
• Process Conditions 50%
• Information Redundancy 50%
• Computational Capability 50%
• Power over 50%
• Nave Bayes 50%
• Processing Requirements 50%
• Clustering Approach 50%
• Computable 50%
• Bayes Classification 50%
• Computational Burden 50%
• Minimal Information 50%
• Overabundance 50%
• Information Input 50%
• Harmonic Characteristics 50%
• Information Sustainability 50%
• Variable Clustering 50%
• Prognostic Model 50%
• Variable Reduction 50%

Engineering

• Input Parameter 100%
• Harmonics 100%
• Bayes Classifier 66%
• Condition Monitoring 33%
• Rotating Machinery 33%
• Process Condition 33%
• Input Information 33%
• Parameter Model 33%
• Heterogeneous Group 33%
• Success Rate 33%

Chemical Engineering

• Condition Monitoring 100%