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AI-DRIVEN PREDICTIVE MAINTENANCE FOR INDUSTRIAL OPERATIONS
A Guide to Machine Learning, IIoT, Condition Monitoring, and Predictive Analytics to Reduce Downtime, Extend Asset Life, and Lower Costs
Engleski
90 b
Novo
Do 30 dana za povrat
Your Plant Is Bleeding Millions. The Warning Signs Were There for Weeks.
At 2:47 a.m., a bearing in a centrifugal pump at a Gulf Coast petrochemical facility reached the end of its service life. The failure had been developing for nine weeks. The vibration data showed it. The temperature trends confirmed it. Nobody was watching.
Seventy-one hours later, after an emergency shutdown, a contaminated process line, and a rush parts order, the total damage exceeded $1.08 million.
The bearing that started it all cost $340.
This is not a story about bad luck. It is a story about a maintenance strategy built for a world that no longer exists, and it happens thousands of times every year across manufacturing plants, refineries, utilities, and processing facilities worldwide.
The technology to prevent it exists right now. The barrier is no longer financial. The barrier is knowledge. This book closes that gap.
The Only Book That Takes You From Failure Physics to Full AI Deployment, Without Losing You Along the Way
AI-Driven Predictive Maintenance for Industrial Operations is the practitioner's implementation guide that the industrial maintenance world has been missing.
Not a vendor whitepaper. Not an academic ML textbook. A complete, end-to-end field guide written by an industrial AI architect who has personally led PdM programs from single-plant pilots to enterprise deployments spanning dozens of sites and thousands of monitored assets.
This Book Is for You If...
- You are a Maintenance Manager being asked to deliver AI results with limited budgets and legacy infrastructure
- You are a Reliability Engineer who understands FMEA and failure physics but has never trained a machine learning model
- You are a Data Scientist who knows Python but has never worked with IIoT sensor data or plant-floor operational realities
- You are a Plant or Controls Engineer responsible for IIoT infrastructure, SCADA integration, or edge architecture
- You are an Operations or Technology Leader building the business case or evaluating vendor platforms
From the P-F Curve and FMEA-to-RPN pipeline, through anomaly detection, RUL prediction, and SHAP explainability, to Kafka streaming architectures, MLOps drift detection, Hybrid Digital Twins, LLM-powered maintenance copilots, and a complete 127-asset capstone deployment with annotated Python code, this is the full stack, in one book.
The Results Are Documented. The ROI Is Real.
- 50-70% reduction in unplanned downtime
- 20-40% reduction in maintenance costs
- 40% extension in average asset lifespan
- Up to 30:1 ROI within 18 months
Every day your plant runs on a calendar-based schedule, it is rolling the dice on an 89% failure probability your PM program was never designed to catch.
This is the guide that changes that.
Scroll up and get your copy. Your next compressor is already telling you something. The question is whether you're listening.
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