Predictive Maintenance with LOTA Certified Course

Course Description:

The Predictive Maintenance with Iota Certified Course his is an on‑demand, self-paced certification course available through the IOTA Learning Management System (LMS). It comprises concise lecture modules, real-world case examples, and quizzes, with a focus on using quantitative tools and structured rules to assess risk and guide maintenance decisions

Key Features of Course Divine:

• Collaboration with E‑Cell IIT Tirupati
• 1:1 Online Mentorship Platform
• Credit-Based Certification
• Live Classes Led by Industry Experts
• Live, Real-World Projects
• 100% Placement Support
• Potential Interview Training
• Resume-Building Activities

Career Opportunities After Predictive Maintenance with Iota Certified Course:

• Predictive Maintenance Engineer
• Eliot / IoT Engineer
• Machine Learning Engineer
• Cloud IoT Engineer / Architect
• Maintenance / Reliability Analyst
• Eliot Consultant / Solutions Architect

Essential Skills you will Develop Predictive Maintenance with Iota Certified Course:

• IoT Systems & Sensor Integration
• Data Collection & Time-Series Management
• Machine Learning & Predictive Modeling
• Condition‑Based & Rule‑Based Diagnostics
• CMMS & Digital Maintenance Tools

Tools Covered:

• Ultrasound inspection tools
• Vibration
•  samplers
• Data Acquisition

Syllabus:

Module 1: Fundamentals of Maintenance Strategies  Reactive, preventive, condition‑based, predictive strategies Role of Pd.M. in Industry 4.0 and IOTA’s ledger for secure data flows.

Module 2: IoT & Sensor Technologies Sensor types (vibration, ultrasonic, infrared, oil oil analysis) Signal acquisition, IoT gateways, basic MQTT/Copal protocols Integrating IOTA Tangle for tamper-proof asset health data logging.

Module 3: Data Processing & Analytics Data cleaning, feature extraction (time-domain, frequency-domain) Time-series analysis frameworks for anomaly detection Trend and outlier identification using prevalence of Vibe/thermal monitoring.

Module 4: Machine Learning & Prognostics Supervised/unsupervised algorithms: regression, classification, clustering RUL (Remaining Useful Life) models and validation techniques Practical lab: deploying ML models for failure prediction.

Module 5: Pd.M. Diagnostic Tools Deep dives into vibration, ultrasound, thermography, oil analysis  Hands-on use cases: setting thresholds and identifying failure signatures.

Module 6: AI, Edge, and Blockchain Integration AI/ML on edge devices, stream processing IOTA Tangle for decentralized, secure data streaming Smart contracts oracles: triggering maintenance workflows based on sensor ledger events

Module 7: Decision Optimization & Program Management Cost‑benefit and risk-based optimization frameworks KPI identification and monitoring for Pd.M. success Change management and stakeholder communication.

Module 8: System Deployment & Scaling Integrating Pd.M. systems with CMMS and ERP Data governance, security, regulatory issues Hands-on: full-stack deployment of IoT–IOTA–AI powered Pd.M. solution.

Module 9: Industry Case Studies & Emerging Trends Case studies across sectors: energy, manufacturing, transportation Evolution toward AI, digital twins, Eliot, edge analytics Emerging topics: blockchain audits, digital twins, AR/VR diagnostics

Module 10: Capstone Project Students design and execute a Pd.M. strategy: data collection, model training, ledger integration, dashboard Present results, evaluate ROI, KPIs, and deployment roadmap.

Industry Projects:

• IOTA + NEDO (Japan)
• Knox + Deutsche Bah (Germany)
• Alcoa + Sensei (Iceland)
• Duke Energy Renewable + See (USA)
• Core View – Pump Manufacturer

Who is this program for?

• Maintenance & Reliability Engineers
• Iota Specialists & Device Engineers
• Data Engineers, Analysts & Data Scientists

How To Apply:

Mobile: 9100348679

Email: coursedivine@gmail.com