Predictive maintenance using IoT – MCQs – EE

1. What is the main goal of predictive maintenance in IoT-enabled systems?
(A) To predict equipment failures before they occur
(B) To repair equipment after breakdown
(C) To reduce equipment lifetime
(D) To increase manual inspection

2. Predictive maintenance relies heavily on which technology?
(A) Internet of Things (IoT)
(B) Manual inspection
(C) Paper-based records
(D) Random sampling

3. Which type of data is most important for predictive maintenance?
(A) Real-time sensor data
(B) Text messages
(C) Historical logs only
(D) Manual reports

4. IoT devices used in predictive maintenance are mainly:
(A) Sensors and actuators
(B) Circuit breakers
(C) Fuses and relays
(D) Power inverters

5. Predictive maintenance helps in:
(A) Reducing downtime and maintenance cost
(B) Increasing maintenance frequency
(C) Eliminating automation
(D) Ignoring equipment health

6. Which of the following is a key parameter monitored for motor predictive maintenance?
(A) Vibration level
(B) Wall color
(C) Employee count
(D) Air humidity only

7. IoT sensors in predictive maintenance collect data such as:
(A) Temperature, vibration, current, and pressure
(B) Employee attendance
(C) Marketing trends
(D) Internet usage statistics

8. Predictive maintenance is different from preventive maintenance because it:
(A) Predicts failures based on data analytics
(B) Follows fixed time schedules
(C) Requires manual inspection
(D) Is performed after equipment failure

9. Which technology is often combined with IoT for predictive maintenance?
(A) Artificial Intelligence (AI) and Machine Learning (ML)
(B) Manual measurement
(C) Static control systems
(D) Relay logic

10. The main advantage of IoT-based predictive maintenance is:
(A) Early detection of anomalies
(B) Increased repair time
(C) Higher manual workload
(D) Reduced data visibility

11. Which communication protocol is commonly used for IoT devices in predictive maintenance?
(A) MQTT
(B) HTTP only
(C) SMTP
(D) FTP

12. Data collected from IoT sensors is typically analyzed using:
(A) Cloud or edge analytics platforms
(B) Manual logs
(C) Local spreadsheets
(D) Telephone reports

13. The process of detecting small deviations in machine behavior before failure is called:
(A) Condition monitoring
(B) Preventive action
(C) Manual observation
(D) System shutdown

14. Vibration sensors are mainly used for detecting:
(A) Mechanical imbalance or misalignment
(B) Temperature changes only
(C) Voltage fluctuations
(D) Data overload

15. Temperature sensors help detect:
(A) Overheating or lubrication failure
(B) Air pressure
(C) Data loss
(D) Lighting issues

16. What is the role of cloud computing in predictive maintenance?
(A) To store and analyze large amounts of sensor data
(B) To generate mechanical movement
(C) To replace sensors
(D) To control power supply

17. Predictive maintenance contributes to:
(A) Increased asset reliability
(B) Higher energy losses
(C) Reduced automation
(D) Frequent system shutdowns

18. Edge computing in predictive maintenance helps by:
(A) Processing data locally for faster insights
(B) Sending all data to cloud without filtering
(C) Ignoring sensor readings
(D) Storing data permanently offline

19. Which machine learning technique is commonly used in predictive maintenance?
(A) Regression and anomaly detection
(B) Sorting algorithms
(C) Binary tree search
(D) Random guessing

20. IoT-enabled predictive maintenance reduces:
(A) Unplanned downtime
(B) System automation
(C) Data accuracy
(D) Equipment performance

21. The first step in implementing predictive maintenance is:
(A) Installing sensors and collecting data
(B) Replacing all equipment
(C) Scheduling manual checks
(D) Running power tests only

22. Which of the following is an example of a predictive maintenance application?
(A) Wind turbine vibration monitoring
(B) Manual oil change tracking
(C) Periodic cleaning schedules
(D) Employee attendance system

23. Data anomalies detected by IoT sensors indicate:
(A) Early signs of component failure
(B) Normal operation
(C) Regular behavior
(D) No risk to the system

24. Predictive maintenance analytics is based on which type of modeling?
(A) Predictive and prescriptive modeling
(B) Manual data logging
(C) Random sampling
(D) Static modeling only

25. The combination of IoT, AI, and big data enables:
(A) Intelligent decision-making for maintenance planning
(B) Manual intervention
(C) Delay in response
(D) System shutdown

26. Predictive maintenance systems send alerts through:
(A) IoT dashboards, alarms, or notifications
(B) Printed reports only
(C) Local control room logs
(D) Manual inspection

27. The key challenge in IoT predictive maintenance is:
(A) Managing and analyzing large volumes of data
(B) Lack of communication networks
(C) Frequent manual testing
(D) Low cost of hardware

28. Predictive maintenance can help industries:
(A) Extend equipment life and improve safety
(B) Increase breakdown frequency
(C) Ignore maintenance schedules
(D) Decrease data accuracy

29. IoT-enabled predictive maintenance supports which type of maintenance strategy?
(A) Condition-based maintenance
(B) Breakdown maintenance
(C) Reactive maintenance
(D) Manual inspection

30. The ultimate goal of predictive maintenance using IoT is to:
(A) Achieve zero unplanned downtime and optimal performance
(B) Increase manual work
(C) Reduce data availability
(D) Ignore sensor feedback