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Artificial Intelligence For Predictive Maintenance(1st Edition)
Authors:
Jamie Flux
Cover Type:Hardcover
Condition:Used
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Book details
ISBN: B0DGV3PNS3, 979-8339028116
Book publisher: Independently published
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Book Summary: Key Features:- A Wide-ranging Exploration Of AI-driven Approaches Tailored Specifically For Predictive Maintenance.- Step-by-step Python Code Implementations For Each Technique Across All Chapters.- Insights On Integrating Both Physics-driven And Data-driven Methodologies For Robust Predictive Models.Explore Diverse Techniques And Methodologies, Including:- Master AI-driven Predictive Maintenance Algorithms To Anticipate Failures Before They Occur.- Implement Dynamic Bayesian Networks For Effective Modeling And Inference.- Utilize Markov Decision Processes To Optimize Maintenance Schedules Under Uncertainty.- Deploy Deep Reinforcement Learning To Determine Optimal Maintenance Actions.- Optimize Strategies Using Covariance Matrix Adaptation Evolution Strategy (CMA-ES).- Enhance Anomaly Detection With Advanced Ensemble Learning Techniques.- Apply Wavelet Transform For Sophisticated Signal Processing Insights.- Design Autoencoders For Effective Feature Extraction And Anomaly Detection.- Leverage Recurrent Neural Networks To Capture And Predict Temporal Equipment Patterns.- Enable Real-time Monitoring With Kalman Filters.- Optimize Model Training Through Stochastic Gradient Descent.- Integrate Bayesian Inference With Neural Networks Using Bayesian Neural Networks.- Develop Long Short-Term Memory (LSTM) Models For Sequential Predictions.- Capture System Interdependencies With Graph Neural Networks.- Utilize Regression Models For Precise Failure Time Prediction.- Classify Equipment States Using Support Vector Machines.- Model Nonlinear Maintenance Data Relationships Via Gaussian Process Regression.- Obtain Robust Predictions And Feature Insights With Random Forests.- Employ Monte Carlo Simulations For Comprehensive Risk Assessment.- Reduce Data Dimensionality Using Principal Component Analysis, Identifying Crucial Variables.- Investigate Root Causes With Fault Tree Analysis.- Optimize Through Genetic Algorithms For Efficient Resource Allocation.- Manage Uncertainty In Data Using Fuzzy Logic Systems.- Forecast Equipment Conditions With ARIMA Models.- Segment Maintenance Data Using Hierarchical Clustering For Deeper Insights.- Analyze Image Data Of Equipment With Convolutional Neural Networks.- Develop Adaptive Strategies Using Policy Gradient Methods In Reinforcement Learning.- Detect Anomalies With Spectral Clustering Techniques.- Visualize Complex Data With Dimensionality Reduction Using T-SNE.- Design Optimized Models Via Neural Architecture Search.- Leverage Pre-trained Models Through Transfer Learning For Maintenance Tasks.- Quantify Multi-level Uncertainty With Hierarchical Bayesian Models.- Apply Double Q-learning For Strengthened Maintenance Planning.- Enhance Prediction Accuracy Using Gradient Boosting Machines.- Estimate Failure Probabilities Effectively Using Markov Chains.- Track Maintenance-related Events With Conditional Random Fields.- Interpret Maintenance Imagery Via Semantic Segmentation Techniques.- Predict Failures With Minimal Data Using Zero-Shot Learning.- Detect Anomalous Patterns With Variational Autoencoders.- Build Predictive Models Using Hidden Markov Models.- Enhance Model Robustness With Adversarial Machine Learning.- Collaborate On Distributed Data Using Federated Learning.- Decode Temporal Sequences With Long-Short-Term Attention.- Extract Insights From Unlabeled Data Through Self-Supervised Learning.- Relate Complex Interactions With Factorization Machines.- Conduct Rapid Assessments With Extreme Learning Machines.- Focus On Important Sequence Signals Using Attention Mechanisms.- Fine-tune Models Using Bayesian Hyperparameter Optimization.- Merge RNNs And CNNs For Spatio-Temporal Data Predictions.
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