2. • INTRODUCTION:
• 1. Importance of Modern Equipment: In today's world, businesses heavily depend on advanced technology
to operate smoothly.
• 2. Cost of Failure: If something goes wrong with this technology, it can be very expensive for the
organization.
• 3. Preventive and Reactive Maintenance: Some people rely on preventive maintenance (regular checks and
fixes) or reactive maintenance (fixing issues as they arise), but these methods can be risky and costly.
• 4. Introduction of Predictive Maintenance: Smart leaders are opting for predictive maintenance, which uses
Artificial Intelligence to predict when equipment might fail, allowing for proactive fixes before issues occur.
3. • Real-Time Streaming Technology: This technology is becoming more popular because it allows data to be
continuously sent from devices, sensors, and apps.
• Driving Growth in Predictive Maintenance: The rise of real-time streaming is a key factor behind the growth
of the Predictive Maintenance market.
• Analyzing Real-Time Data: The data streamed in real-time is analyzed using computational tools.
• Streaming Analytics: This is a crucial part of Predictive Maintenance. It involves delivering real-time data to
systems that automatically monitor equipment health.
• Automated Monitoring: The aim is to monitor assets automatically, detecting issues early to prevent
breakdowns.
• Timely Maintenance Alerts: Staff are alerted when maintenance is needed, based on the real-time data
analysis
4. • CAGR- Compound annual growth rate
• Why Maintance?
• Operational Continuity: Ensure equipment/systems remain operational.
• Optimized Performance: Maximize efficiency of production equipment.
• Prevent Breakdowns: Avoid equipment failures or breakdowns.
• Minimize Production Loss: Reduce downtime and production losses.
• Enhance Reliability: Increase reliability of operating systems.
• Safety Assurance: Maintain a safe working environment.
• Prevent Leakages/Losses: Avoid leaks and minimize losses.
5.
6. 1)Break Down Maintenance:
• Reactive Approach: Wait for equipment failure before repairing.
• Minimal Impact: Used when failure doesn't significantly affect operations.
• Cost-Driven: Mainly incurs repair costs.
• Limited Losses: Failure doesn't lead to substantial production loss.
2)Preventive Maintenance:
• Proactive Approach: Regular maintenance to prevent failure.
• Daily Tasks: Cleaning, inspection, oiling, and tightening.
• Preserves Health: Maintains equipment in healthy condition.
• Extended Service Life: Prolongs equipment lifespan.
• Includes Periodic and Predictive Maintenance: Regular checks and predictive analytics to detect issues early.
7. • Advantages of Break Down Maintenance:
1.Cost-Efficient: No proactive maintenance costs until failure occurs.
2.Simple Implementation: No need for complex schedules or planning.
3.Minimal Disruption: Maintenance only when necessary, reducing downtime.
4.Focused Resources: Resources are used only when needed, reducing waste.
• Disadvantages of Break Down Maintenance:
1.Unpredictable Downtime: Equipment failures can occur at any time, causing unexpected
downtime.
2.Higher Risk of Damage: Waiting for failure can lead to more extensive damage or costly
repairs.
3.Reduced Equipment Lifespan: Lack of proactive maintenance can shorten equipment
lifespan.
8. • Condition Monitoring:
1. Determining Machinery Condition: Assessing equipment health while it's running.
2. Key Elements for Success:
1. Knowing what sounds or signs to look for.
2. Understanding how to interpret these signs.
3. Knowing when to act on this information.
3. Preventative Action:
1. Enables fixing problem parts before they fail.
4. Benefits:
1. Reduces risk of major breakdowns.
2. Allows for advance ordering of parts.
3. Helps schedule manpower effectively.
4. Facilitates planning of other repairs during downtime.
9. • Corrective Maintenance:
• Improving Equipment: Enhances equipment and components.
• Reliability Boost: Enables reliable execution of preventive maintenance.
• Addressing Design Weakness: Fixes flaws in equipment design.
• Redesigning for Reliability: Improves equipment reliability.
• Enhancing Maintainability: Makes maintenance tasks easier and more efficiency
10. • Predictive Maintenance: Advantages:
1.Cost Savings: Minimizes unexpected downtime and costly repairs.
2.Efficiency: Enables maintenance to be performed only when needed,
optimizing resources.
3.Enhanced Safety: Helps prevent accidents by addressing issues before
they become critical.
4.Extended Equipment Life: Increases the lifespan of equipment by
addressing issues proactively.
• Disadvantages:
1.Initial Investment: Requires investment in monitoring equipment and
technology.
2.Complexity: Implementing and managing predictive maintenance
systems can be complex.
3.Skill Requirement: Requires skilled personnel to analyze data and
interpret results accurately.
4.False Alarms: Risk of false alarms leading to unnecessary maintenance
actions.
11. • Corrective Maintenance: Advantages:
1. Immediate Action: Addresses issues as they occur, minimizing downtime.
2. Cost-Effective: Only incurs maintenance costs when necessary.
3. Simplicity: No need for complex planning or scheduling.
4. Resource Efficiency: Resources are used only when needed, reducing waste.
• Disadvantages:
1. Unplanned Downtime: Can lead to unexpected downtime and production losses.
2. Increased Risk of Damage: Delaying maintenance may lead to more extensive damage.
3. Shortened Equipment Lifespan: Lack of proactive maintenance can shorten equipment lifespan.
4. Safety Concerns: Increased risk of accidents due to unexpected failures.
12. • Condition Monitoring: Advantages:
1. Early Detection: Helps identify issues before they cause equipment failure.
2. Improved Reliability: Enhances equipment reliability by addressing issues proactively.
3. Cost Savings: Minimizes repair costs by fixing problems early.
4. Efficiency: Allows for better planning of maintenance activities, reducing downtime.
• Disadvantages:
1. Skill Requirement: Requires trained personnel to interpret data accurately.
2. Equipment Investment: Initial investment in monitoring equipment and technology is needed.
3. False Alarms: Risk of false alarms leading to unnecessary maintenance actions.
4. Complexity: Implementing and managing condition monitoring systems can be complex.
13. • Aircraft Maintenance:
• Overhaul, repair, inspection, or modification of aircraft and its
components.
• Detailed inspections are commonly referred to as "checks.“
• Types of Checks:
1.A Check: Lighter maintenance check.
2.B Check: Also a lighter check.
3.C Check: More extensive, occurs every 20-24 months or
specific flight hours.
4.D Check: Most comprehensive and demanding, occurs every 5
years.
14. • C Check:
• Extensive inspection, puts aircraft out of service for 1-2 weeks.
• Requires a maintenance base hangar and up to 6000 man-hours.
• D Check:
• Comprehensive, occurs every 5 years.
• Takes the entire airplane apart for inspection, can last up to 2 months.
• Requires a suitable maintenance base, up to 50,000 man-hours, and is the most expensive.
• Nondestructive Testing (NDT) in Aircraft Maintenance:
• Economical inspection method.
• Ensures high quality and reliability.
• NDT Methods:
• Liquid penetrant
• Magnetic particle
• Eddy current
• Ultrasonic
• Radiography (x-ray/gamma ray)
• Visual Optical
• Sonic Resonance
• Infrared Thermography.
15. • Predictive Maintenance:
• Meaning: Anticipating and responding to machinery issues in advance.
• Observation and Response: Regular monitoring of machinery conditions to take timely action.
• Tools Used: Human senses and sensitive instruments like audio gauge, vibration analyser, etc.
• Need for Predictive Maintenance:
• Increased Automation: With automation rising, there's a greater need to anticipate maintenance.
• Business Loss Prevention: Avoiding production delays and ensuring quality products.
• Just-in-Time Manufacturing: Timely maintenance ensures smooth operations.
• Organized Environment: Planning maintenance in an organized manner.
16. Predictive Maintenance Services:
• Driven by Predictive Analytics: Detecting anomalies and failures to prevent critical downtime.
• Resource Optimization: Using resources efficiently, increasing equipment lifecycles, and
improving quality.
Predictive Maintenance for Analytics:
• Data Collection: Gathering various equipment condition data.
• Data Mining and Machine Learning: Extracting insights and analytics from datasets.
Predictive Maintenance Tools and Software:
• Monitoring Techniques: Using both conventional and advanced techniques.
• Prevention Techniques: Planning maintenance in advance based on monitoring results.
• Global Adoption: More common in developed countries like the USA, less so in Asia-Pacific
and the Middle East.
• IoT Integration: IoT sensors capture data, Machine Learning analyzes it for maintenance
needs.
17. Predictive Maintenance with Machine Learning:
• Meaning: Anticipating issues in advance using machine learning.
• Anomaly Detection: Machine learning models monitor IoT sensor data to learn normal behavior and detect
anomalies.
• Automatic Identification: Identifies anomalies, finds correlations, and makes recommendations.
• Dynamic Adjustments: Machine learning adapts to new data in real-time and alerts staff of serious issues.
• No Manual Configuration: Doesn't require manual setup or threshold settings like other maintenance
methods.
Factors Addressed Before Implementing Predictive Maintenance:
• Error History: Includes normal operational and failure patterns in training data.
• Repair/Maintenance History: Contains information on repairs and parts replacements.
• Machine Operating Conditions: Data from sensors capturing equipment performance over time.
• Static Feature Data: Technical details like equipment model and service start date.
18. • IoT-based Predictive Maintenance:
• Competing with Time-based Approach: Emphasizes detecting random failures instead of age-related issues.
• Data Collection Process: Involves sensors capturing parameters and transitioning data through gateways.
• Data Processing Steps: Raw data moves to a Data Lake, then to a Data Warehouse for cleaning and
structuring.
• Machine Learning Model: Analyzes data, detects abnormal patterns, and predicts future failures.
• IBM Predictive Maintenance Service:
• IBM Service Overview: Supervises, analyzes, and reports on device parameters, providing maintenance
recommendations.
• Applications: Used for predicting asset downtime, optimizing maintenance cycles, and investigating root
causes of failures
19. Benefits of Predictive Maintenance:
• Cost Reduction: Maintenance costs decrease by approximately 50%.
• Decreased Failures: Unexpected failures decrease by 55%.
• Faster Repair Time: Overhaul and repair time is 60% lower.
• Inventory Reduction: Spare parts inventory is cut by 30%.
• Increased Mean Time Between Failures: Machinery MTBF increases by 30%.
• Improved Uptime: Uptime is increased by 30%.
Importance of Predictive Maintenance:
• Cost Optimization: Ensures maintenance is neither too early nor too late, saving money.
• Availability of Sensor Technology: Installation of sensors on machinery is more affordable,
providing real-time data.
• Increased OEE: Predictive analytics software helps schedule maintenance, increasing equipment
availability and performance.
20. Common Predictive Maintenance Uses:
• Manufacturing and IoT: IoT technology monitors manufacturing processes, detecting and eliminating
deteriorating parts.
• Automotive: Connected cars use sensor data to warn drivers of issues before breakdowns occur.
• Utility Suppliers: Use smart meter data for early detection of supply and demand issues, preventing
outages.
• Insurance: Utilizes Predictive Analytics for more accurate predictions on weather-related disasters.
SCADA System:
• Definition: Supervisory Control and Data Acquisition (SCADA) is a computer control system used to
monitor and control plant processes.
• Maintenance Scope: Ranges from basic tasks like OS updates to complex configurations.
• Complexity: Basic maintenance can become complex without proper business rules.
• Functionality: Uses data communications, graphical interface, and management for system monitoring
and control.
Importance of SCADA System Maintenance:
• Cost of Downtime: Missed alarms and downtime can lead to expensive incidents.
• Regulatory Compliance: Regulators can impose large fines for incidents.
• Design for Maintainability: Systems designed for cost-effective maintenance are more likely to operate
21. OEE and TPM Losses:
• Goal: TPM and OEE programs aim to reduce the Six Big Losses, the main causes of
equipment-based productivity loss.
• Six Big Losses:
• Downtime Loss
• Speed Loss
• Quality Loss
• Performance Loss
• Startup/Setup Loss
• Yield Loss
Capture the Six Big Losses for Enhanced OEE Analysis:
• Helps gain additional insights into OEE factors of Availability, Performance, and Quality.
• Identifies areas for improvement and optimization in manufacturing processes.
22. Equipment Failure:
• Definition: Any significant period where equipment scheduled for production is not running due
to a failure.
• Types: Tooling failure, breakdowns, unplanned maintenance.
• Availability Loss: Equipment failure reduces the availability of equipment for production.
Setup and Adjustments:
• Definition: Periods where equipment is stopped for setup, changeovers, adjustments, etc.
• Availability Loss: Setup and adjustments reduce the availability of equipment for production.
Idling and Minor Stops:
• Definition: Short stops due to misfeeds, jams, incorrect settings, etc.
• Availability Loss: Idling and minor stops reduce equipment availability for
production.
• Chronic Issues: Often chronic problems that operators may overlook.
• Reduced Speed:
• Definition: Equipment operates at slower than normal speeds due to various
reasons.
• Performance Loss: Reduced speed affects the performance of equipment, leading to
reduced productivity.
23. • Process Defects:
• Definition: Production of defective parts during stable production.
• Quality Loss: Process defects result in lower quality output and increased waste.
• Reduced Yield:
• Definition: Lower than expected output due to various reasons like changeovers, incorrect
settings, etc.
• Quality Loss: Reduced yield leads to a decrease in the quantity of usable parts produced.
• Using the Six Big Losses:
• Availability Loss Reduction: Minimize equipment failures and setup times to reduce
unplanned downtime.
• Performance Loss Reduction: Address idling, minor stops, and reduced speed to prevent
productivity losses.
• Quality Loss Reduction: Minimize process defects and reduced yield to improve product
quality and reduce waste.
24. What is a Digital Twin?
• Definition: A digital twin is a digital representation of a physical object, process, or service.
• Examples: It can replicate physical objects like jet engines, wind farms, buildings, or even entire cities.
• Purpose: Used to collect data and predict the performance of physical assets or processes.
How do Digital Twins Work?
• Creation: Digital twins are created as virtual counterparts of physical assets using sensors.
• Data Collection: Engineers gather data from various sources such as physical, manufacturing, and
operational data.
• Synthesis: Data is synthesized to create a digital twin, even before the physical asset is built.
Applications of Digital Twins:
• Manufacturing: Optimizing production processes and equipment performance.
• Automobile: Monitoring vehicle performance and predicting maintenance needs.
• Retail: Enhancing customer experiences through personalized recommendations.
• Healthcare: Improving patient care and treatment outcomes.
• Smart Cities: Managing urban infrastructure and resources efficiently.
• Industrial IoT: Enhancing operational efficiency and predictive maintenance.
25. • Types of Digital Twins:
• Asset Twins: Represent individual components or assets, providing insights into their performance and
interactions.
• System or Unit Twins: Show how different assets work together to form a functioning system, offering
visibility into asset interactions.
• Process Twins: Reveal how systems collaborate to create an entire production facility, helping optimize
overall effectiveness and efficiency.
• Robotics And Automation In Manufacturing
• Industry Trends: By 2021, 20% of top manufacturers are expected to utilize embedded intelligence, IIoT,
blockchain, and cognitive intelligence to automate processes, reducing execution time by up to 25%. In
2018, there were 74 robot units per 10,000 employees globally, with over 230,000 real industrial robots
in the United States.
26. • Types of Automation
• Fixed Or Hard Automation: Dedicated robots perform repetitive operations with fixed sequences, enhancing
production rates but lacking flexibility. Commonly found in processes like distillation and paint shops.
• Programmable Automation: Suited for batch production with medium to high volume, yet doesn't allow easy
reconfiguration. Common in industries like paper mills and steel rolling mills.
• Flexible Or Soft Automation: Offers flexibility in product design and operations, allowing rapid changes
through human-operated commands. Used in automatic guided vehicles, automobiles, and CNC machines.
• Production Efficiencies And Cost Savings
• Increased efficiency and faster throughput: Robots can operate quicker than humans, reducing cycle time and
enabling 24/7 operations.
• Flexibility and scalability: Robots can adapt to changing tasks and priorities, offering scalability in operations.
• Improved accuracy: Robots follow instructions precisely, minimizing errors in production.
27. • Ease of integration with existing machinery: Advances in technology have made robot assembly and
maintenance faster and less expensive.
• Real-time data gathering: Robots provide valuable data for process improvement and maintenance through
continuous monitoring and analysis
• Onsite Safety
• Fewer accidents and injuries: Robotics developers ensure safe operation through safe zones and
fencing, reducing worker injuries.
• Faster reactions: Robots react quickly to hazardous situations, mitigating risks.
• No safety training: Robots handle dangerous tasks without the need for extensive safety training,
reducing the risk of on-the-job injuries.
28. • Advantages For Industrial Automation
• Reduced labor cost: Automation reduces the need for manual labor, cutting costs.
• Mitigate labor shortages: Automation fills the gaps left by labor shortages, ensuring continuous operation.
• Improve worker safety: Automation takes on dangerous tasks, reducing the risk of worker injuries.
• Reduce manufacturing lead time: Automation speeds up production processes, reducing lead times.
• Accomplish processes that cannot be done manually: Automation enables tasks that are too complex or hazardous for
humans.
• Avoid the high cost of not automating: Not automating processes can lead to inefficiencies and higher operational costs in
the long run.
• Disadvantages Of Industrial Automation
• Higher start-up and operation costs: Initial investment and ongoing operational costs of automation systems can be
significant.
• Higher cost of maintenance: Maintenance of automation systems can be costly, especially for complex machinery.
• Obsolescence/depreciation cost: Automation systems may become obsolete over time, requiring frequent upgrades or
replacements, leading to depreciation costs.
29. • What Is Automated Manufacturing?
• Efficiency Boost: Automated manufacturing uses technology to streamline production processes,
leading to higher outputs at lower costs.
• Role of AI: Artificial intelligence (AI) enhances automation by empowering robots to perform tasks
more effectively and take on additional responsibilities.
• Real-Time Inspection: AI enables robots to inspect parts during production, detecting issues in real-
time to enhance product quality and yield.
• Safety Improvements: AI-powered robotic arms enhance safety by executing complex tasks in
hazardous environments, reducing the risk of injuries to human workers.
• Retraining Ease: AI facilitates the retraining and repurposing of robots for different tasks,
contributing to flexibility in manufacturing processes.
How to Achieve Warehouse Automation and Automated Manufacturing
• IT/OT Convergence: Achieving warehouse automation and automated manufacturing
involves integrating information technology (IT) with operational technology (OT)
systems to optimize industrial operations.
• Benefits of Robotics in Warehouses and Manufacturing
• Efficiency and Productivity: Robotics enhance efficiency and productivity in
warehouses and manufacturing facilities.
• Product Quality Improvement: Automated processes lead to improved product
quality through precision and consistency.
• Worker Safety: Robotics contribute to safer workplaces by handling hazardous tasks
and reducing the risk of injuries to human workers.
• Cost Savings: Automation leads to cost savings by minimizing labor expenses and
reducing waste.
• Faster Cycle Times: Automated processes result in faster cycle times, speeding up
production and delivery schedules.
30. Robotic Warehouse and Manufacturing Automation Technology
• Autonomous Mobile Robots (AMRs): Mobile robots capable of navigating
autonomously within warehouse or manufacturing environments.
• Industrial Robotic Arms: Robotic arms designed for various tasks such as assembly,
welding, and material handling.
• Cobots: Collaborative robots designed to work alongside humans, enhancing
productivity and safety.
• Pick and Place: Robotic systems specialized in picking items from one location and
placing them in another.
• Palletizing: Robotics systems designed to stack and organize pallets of goods.
• Material Handling: Robots equipped to handle and transport materials within a
facility.
31. • What is a PLC?
• Definition: PLC stands for “Programmable Logic Controller”, a specialized computer designed
for operation in harsh industrial environments.
• Similarities to Personal Computers: PLCs share similarities with personal computers,
including a power supply, CPU, inputs and outputs (I/O), memory, and operating software.
• Role of PLCs in Automation
• Integral Part of Automation: PLCs play a vital role in automation, often forming part of a
larger SCADA system.
• Programmability: PLCs can be programmed to meet the operational requirements of various
industrial processes.
• Flexibility: They allow for reprogramming, making them adaptable to changes in production
requirements.
• PLC Basics
• Invention: PLCs were invented by Dick Morley in 1964.
• Functions: PLCs perform functions such as timing, counting, calculating, comparing, and
processing various analog signals.
• Advantages: The main advantage of PLCs over hard-wired control systems is their flexibility,
allowing for easy changes and modifications at low cost.
32. • How Does a PLC Work?
• Scan Process: PLCs operate through a scan process involving
cycling, monitoring inputs, executing user programs, internal diagnosis,
and updating outputs.
• Five Main Parts: Typical PLCs consist of a rack or chassis, power
supply module, CPU, input & output module, and communication
interface module.
• PLC Programming
• Textual Language: PLC programming can be done using textual
languages such as instruction list and structured text.
• Graphical Form: Graphical programming languages like ladder
diagrams (LD), function block diagrams (FBD), and sequential function
charts (SFC) are also used.
33. • Applications of PLCs
• Process Automation: PLCs are used in various industries including glass, paper, cement manufacturing, and
thermal power plants for process automation.
• Boilers: They are crucial in boiler automation within thermal power plants.
• Flexible in Size and Output Types: PLCs come in various sizes (mini, micro, nano) and types (relay output,
transistor output, triac output) to suit different industrial needs.
34. • Predicting Remaining Useful Life of a Machine:
• Definition: The remaining useful life (RUL) refers to the duration a machine is expected to operate
before requiring repair or replacement.
• Importance: Estimating RUL aids in scheduling maintenance, optimizing efficiency, and preventing
unplanned downtime.
• Modeling Solutions:
• Regression: Predicting RUL or Time to Failure (TTF).
• Binary classification: Predicting if an asset will fail within a certain timeframe.
• Multi-class classification: Predicting failure within different time windows.
• Procedures for Prediction:
• Data Preparation:
• Import the dataset.
• Visualize the dataset.
• Analyze correlations between features.
• Data Labeling:
• Label training data by setting window length.
• Normalize data.
• Label test data based on RUL.
35. • Data Sequencing:
• Sequence the data.
• Model Preparation:
• Prepare the model architecture.
• Model Training and Validation:
• Fit the model.
• Validate the model.
• Prediction:
• Predict RUL on test data.
• Mask test data for sensor values recorded during off conditions.
36. • Automation in Manufacturing:
• Definition: Automation in manufacturing involves performing processes with minimal human
intervention.
• Examples: Moulding machines running automatically, producing various products at mass
scale.
• Sensors in Automation:
• Purpose: Sensors collect data from the environment and send it to microprocessors for
decision-making in automation processes.
• Types of Sensors:
• Displacement, position, and proximity sensors.
• Velocity and motion sensors.
• Transducers: Sensors that convert physical variables into electrical signals for microprocessor
interpretation.
• Examples: Potentiometers, strain-gauge elements, capacitive elements, optical encoders, etc.
37. • Human-Machine Interaction (HMI):
• Definition: HMI refers to the interface that allows humans to interact with machines, systems, or devices. It aims to
make the interaction intuitive and user-friendly.
• Physical Aspects of HMI:
• Touch display on a machine.
• Push buttons.
• Mobile devices.
• Computers with keypads.
• Working of Human-Machine Interaction:
• Direct Control:
• Users interact with devices directly, such as touching a smartphone screen or issuing verbal
commands.
• Automated Response:
• Systems automatically identify user needs, like traffic lights changing color when a vehicle
passes over an inductive loop on the road's surface.
• Digital Assistants:
• Chatbots respond automatically to user requests and continue learning.
38. • Examples of Human-Machine Interaction:
• Interacting with a mobile app.
• Browsing a website on a desktop computer.
• Using Internet of Things (IoT) devices.
• Application Areas:
• Electronic commerce.
• Team collaboration.
• Culture and globalization.
• User learning and training.
• System development.
• Healthcare.
• Difference between HMI and SCADA:
• HMI is local to the machine, usually placed on the control panel nearby, while SCADA is a remote
monitoring system set up in a control room, away from the machine itself.
