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  • Duration: 5 Days
  • Mode: Face-to-Face

Overview

     The Industrial Predictive Maintenance – Awareness Level program provides a comprehensive introduction to predictive maintenance concepts used in modern manufacturing industries. Participants will understand the common failure mechanisms of industrial motors, pumps, gearboxes, conveyors and HVAC systems through real industrial case studies and failure analysis. The course explains why industries are shifting from reactive and preventive maintenance to condition-based and predictive maintenance. It introduces commonly used sensing technologies, industrial communication systems, software platforms, data acquisition methods, international maintenance standards, safety requirements, and implementation strategies. The program also evaluates the technical and economic feasibility of deploying predictive maintenance in conventional industrial environments.

Highlights

  • To understand the principles, importance and industrial benefits of predictive maintenance.
  • To understand the technologies, standards, hardware, software and protection systems used for predictive maintenance.
  • To identify common failure modes in rotating and process equipment using engineering reasoning.
  • To evaluate the feasibility and limitations of implementing predictive maintenance in existing industrial plants.

Eligibility

  • Maintenance Engineers

Course Content

Introduction to Maintenance Engineering

  • Evolution of Industrial Maintenance
  • Breakdown, Preventive and Corrective Maintenance      
  • Reliability Centered Maintenance (RCM)      
  • Condition-Based Maintenance (CBM)     
  • Predictive Maintenance (PdM)      
  • Prescriptive Maintenance      
  • Digital Maintenance in Industry 4.0

Need for Predictive Maintenance

  • Cost of equipment failures
  • Production losses
  • Unplanned downtime
  • Maintenance cost comparison
  • Asset reliability improvement
  • ROI of predictive maintenance

Industrial Equipment Failure Analysis

  • Introduction to rotating equipment and process assets
  • Industrial Motors – Coil degradation, Winding resistance variation, Bearing wear, Rotor imbalance and shaft misalignment
  • Industrial Pumps – Oil seal leakage, Bearing failure, Pressure loss, Flow reduction, Brush wear (where applicable), Cavitation and Abnormal vibration
  • Gearboxes – Gear tooth wear, Tooth crack, Misalignment, Lubrication failure, Oil chamber leakage and Bearing degradation
  • Industrial Conveyors – Chain elongation, Chain tension loss, Roller wear, Sprocket damage, Lubrication failure and Belt misalignment
  • HVAC Systems – Refrigerant leakage, Air flow restriction, Temperature imbalance, Pressure variation, Fan bearing failure and Compressor degradation

Technologies Used in Predictive Maintenance

  • Overview of available monitoring technologies
  • Vibration Analysis
  • Thermal Imaging
  • Acoustic Monitoring
  • Oil Analysis
  • Current Signature Analysis (MCSA)
  • Ultrasonic Inspection
  • Motor Current Monitoring
  • Pressure Monitoring
  • Flow Monitoring
  • Temperature Monitoring
  • Power Quality Monitoring

Industrial Sensors

  • Accelerometers
  • MEMS vibration sensors
  • RTDs
  • Thermocouples
  • Pressure transmitters
  • Flow sensors
  • Current transformers
  • Hall-effect sensors
  • Gas sensors
  • Humidity sensors

Software Ecosystem

  • Introduction to software used in predictive maintenance
  • PLC & SCADA overview
  • Historian databases
  • Edge computing
  • Data acquisition software
  • Dashboard software
  • Industrial IoT platforms
  • AI and Machine Learning overview
  • Cloud monitoring platforms

Hardware Requirements

  • Industrial sensors
  • Data acquisition units
  • PLC
  • Industrial controllers
  • Edge gateways
  • Embedded controllers
  • Communication modules
  • Industrial PCs
  • Cloud gateways

Industrial Communication Systems

  • Analog signals (4–20 mA, 0–10 V)
  • Digital I/O
  • Modbus RTU/TCP
  • CAN/CANopen
  • PROFIBUS
  • PROFINET
  • EtherNet/IP
  • OPC UA

Standards, Policies and Safety Requirements

  • ISO 55000 (Asset Management)
  • ISO 17359 (Condition Monitoring)
  • ISO 13374 (Condition Monitoring Architecture)
  • ISO 10816 / ISO 20816 (Machine Vibration)
  • ISO 14224 (Reliability Data Collection)
  • IEC 60034 (Rotating Machines)
  • IEC 61508 (Functional Safety)
  • IEC 62443 (Industrial Cybersecurity)
  • OSHA safety requirements
  • Lock-Out Tag-Out (LOTO)
  • Electrical isolation procedures
  • Hazard identification

Protection Systems

  • Motor protection relays
  • Thermal overload protection
  • Vibration protection
  • Pressure protection
  • Flow interlocks
  • Temperature protection
  • Emergency shutdown systems
  • Alarm management

Limitations of Predictive Maintenance

  • Sensor limitations
  • Data quality issues
  • Installation constraints
  • Environmental effects
  • Cost considerations
  • Legacy equipment challenges
  • False alarms
  • Human factors
  • Cybersecurity risks

Building Blocks of Predictive Maintenance

  • Asset identification
  • Criticality analysis
  • Failure Mode Analysis (FMEA)
  • Sensor selection
  • Data acquisition
  • Communication infrastructure
  • Data storage
  • Data visualization
  • Condition monitoring
  • Fault diagnosis
  • Prognostics
  • Maintenance decision making

Feasibility Analysis

  • Selecting candidate equipment
  • Cost-benefit analysis
  • Return on Investment (ROI)
  • Retrofit feasibility
  • Existing infrastructure assessment
  • Challenges in conventional industries
  • Management commitment

Industrial Case Studies & Scenario-Based Analysis

  • Case Study 1: Motor winding degradation in a manufacturing plant
  • Case Study 2: Gearbox failure due to lubrication issues
  • Case Study 3: Conveyor chain failure in an automotive assembly line
  • Case Study 4: Pump cavitation in a chemical process plant
  • Case Study 5: HVAC compressor degradation in a pharmaceutical cleanroom

Outcomes

Upon completion of the program, the participants will be able to,
  • Explain predictive maintenance concepts and their industrial applications.
  • Identify suitable monitoring techniques for different industrial assets.
  • Interpret common industrial failure symptoms and their possible root causes.
  • Recommend an appropriate predictive maintenance strategy for conventional industrial equipment.
  • Duration: 2 Days (16 Hours)
  • Mode: Face-to-Face
  • The programs will be certified by TVSTS.

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