Industry Applications of IoT: Machine Diagnosis & Prognosis and Indoor Air Quality Monitoring

Industry Applications of IoT: Machine Diagnosis & Prognosis and Indoor Air Quality Monitoring

1. Industry Applications of IoT: Machine Diagnosis & Prognosis and Indoor Air Quality Monitoring Supervisor Dr. Hitesh Mohapatra presented by - Alisha Samantaray (2229009) Anirudh kumar aryan (2229011) Anubhav Kakati (2229015)

2. Content Introduction to IoT in industrial settings 01 Indoor Air Quality Monitoring 02

3. Introduction to IoT in industrial settings IoT in Industry The Industrial Internet of Things (IIoT) refers to the use of Internet of Things (IoT) technologies in industrial sectors and applications. It involves the integration of sensors, instruments, and other devices with network connectivity in industrial environments to collect, exchange, and analyze data. The Industrial Internet of Things (IIoT) offers several key benefits, particularly in terms of efficiency, safety, and cost reduction:

4. The Industrial Internet of Things (IIoT) offers several key benefits, particularly in terms of : Efficiency Safety Cost Reduction

5. Machine Diagnosis & Prognosis - Introduction Machine diagnosis refers to the process of identifying and determining the nature and cause of issues or faults within machinery or equipment. This involves monitoring the machine's performance, analyzing data collected from sensors, and using diagnostic tools to detect anomalies, malfunctions, or potential failures. Machine prognosis involves predicting the future condition and remaining useful life (RUL) of machinery or equipment based on current data and historical trends. It uses advanced algorithms, machine learning, and statistical models to estimate when a machine is likely to fail or require maintenance.

6. Key aspects of machine diagnosis include: Condition Monitoring: Continuously observing the operational parameters and health indicators of a machine. Fault Detection: Identifying deviations from normal operating conditions that may indicate a problem. Root Cause Analysis : Determining the underlying reasons for the detected faults.

7. Key aspects of machine prognosis include: Remaining Useful Life (RUL) Estimation: Predicting the time left before a machine or its components will no longer perform effectively. Failure Prediction: Forecasting when and how a machine is likely to fail. Maintenance Planning: Scheduling maintenance activities based on predicted machine health to minimize disruption and cost.

8. Importance in industrial maintenance Increased Reliability: Preventive Actions: Diagnosis helps identify issues early, allowing for preventive measures before a minor problem becomes a major failure. Predictive Maintenance: Prognosis predicts future failures, enabling timely interventions that maintain the reliability of equipment. Reduced Downtime: Minimized Unplanned Downtime: By diagnosing problems before they lead to breakdowns, and predicting failures, industries can significantly reduce unplanned downtime. Optimized Maintenance Schedules: Prognosis allows maintenance to be scheduled during planned downtimes or off-peak hours, minimizing the impact on production.

9. Cost Savings: Lower Maintenance Costs: Early diagnosis and timely prognosis reduce the need for emergency repairs and the associated costs. Extended Equipment Lifespan: Proper maintenance based on accurate diagnosis and prognosis can extend the useful life of machinery, reducing capital expenditure on replacements. Improved Efficiency: Optimal Performance: Regular maintenance based on accurate diagnostics and prognostics ensures that machinery operates at peak efficiency, leading to higher productivity. Resource Management: Efficient maintenance scheduling and execution help manage resources better, reducing waste and maximizing utilization.

10. Sensors and data collection Real-time monitoring Types of data collected (vibration, temperature, etc.) IoT for Machine Diagnosis

11. Machine Diagnosis Techniques Signal processing Pattern recognition Machine learning algorithms TYPES

12. Machine Prognosis Machine prognosis involves predicting the future condition and remaining useful life (RUL) of machinery or equipment based on current operational data and historical performance trends. This process uses advanced algorithms, statistical models, and machine learning techniques to forecast potential failures or maintenance needs.

13. Predictive Maintenance: Timely Interventions: By predicting when equipment is likely to fail, maintenance can be scheduled proactively, preventing unexpected downtime and extending the life of machinery. Optimized Resource Allocation: Maintenance resources can be allocated more efficiently, focusing on machines that are most likely to need attention. Cost Efficiency: Reduced Emergency Repairs: Predictive maintenance reduces the need for costly emergency repairs and last-minute parts procurement. Lower Total Cost of Ownership: Regularly scheduled maintenance based on accurate prognosis can extend the lifespan of equipment, reducing the need for premature replacements. Importance of Machine Prognosis

14. Enhanced Reliability and Availability: Minimized Downtime: Accurate predictions of potential failures allow for maintenance activities to be planned during non-peak times, minimizing the impact on production schedules. Consistent Performance: Regular maintenance ensures machinery operates at optimal performance levels, enhancing overall production reliability. Improved Safety: Hazard Prevention: Predicting and addressing potential failures before they occur reduces the risk of accidents and improves workplace safety. Compliance: Ensures that machinery operates within safety and regulatory standards, preventing compliance-related issues.

15. Benefits of IoT-based Machine Diagnosis & Prognosis Reduced downtime Cost savings Improved safety Optimized maintenance schedules

16. Indoor Air Quality (IAQ) Monitoring - Introduction Indoor Air Quality (IAQ) Monitoring refers to the process of assessing the cleanliness and safety of the air within and around buildings and structures, particularly as it relates to the health and comfort of building occupants. Given that people spend a significant amount of their time indoors—whether at home, in offices, or in commercial spaces—the quality of indoor air has a profound impact on health and well-being.

17. Poor IAQ can lead to respiratory problems, headaches, fatigue, and other health issues. In industrial settings, where workers may be exposed to hazardous chemicals, particulate matter, and other pollutants, ensuring good IAQ is essential to prevent occupational illnesses. Importance of IAQ in industrial settings Indoor Air Quality (IAQ) significantly affects worker health and productivity in industrial settings. Ensuring good IAQ can lead to numerous benefits, while poor IAQ can result in serious health issues and decreased efficiency. Impact on worker health and productivity

18. IoT for IAQ Monitoring Carbon Dioxide (CO2) Sensors Particulate Matter (PM) Sensors Volatile Organic Compounds (VOCs) Sensors Carbon Monoxide (CO) Sensors Ozone (O3) Sensors

19. Data collection and analysis Data collection and analysis in IAQ monitoring involve deploying sensors to measure pollutants like CO2, particulate matter, and VOCs in real-time. The collected data is aggregated into a central database, visualized through dashboards, and analyzed to identify trends, detect anomalies, and forecast future conditions. Predictive analytics and actionable insights help optimize ventilation, ensure regulatory compliance, and enhance overall air quality management.

20. IAQ Monitoring System Architecture An IAQ (Indoor Air Quality) monitoring system architecture typically consists of a network of sensors integrated into a larger system designed to continuously assess and manage indoor air quality. Sensor network Data transmission in IAQ (Indoor Air Quality) monitoring systems involves sending sensor data from measurement points to a central system or cloud platform for analysis and management. Data transmission Cloud-based data storage and analysis involves using cloud computing resources to collect, store, and analyze data from Indoor Air Quality (IAQ) monitoring systems. Cloud-based data storage and analysis

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