The document outlines a predictive and prescriptive maintenance strategy for automotive technology, emphasizing the integration of AI and machine learning to optimize equipment performance and lifespan. It details the methodology for data acquisition and analysis, including the identification of common faults, to improve monitoring efficiency and reduce downtime. The proposed advancements include enhanced AI and IoT integration, sensor fusion, and wireless monitoring systems to improve vehicle safety and maintenance reporting.

2. Management Team
CEO: Mr. Gokulakrishnan Sriram
Extensive experience in automotive technology and business management.
CTO: Dr. Karthikeyan Rajagopal
Expert in AI and machine learning with a background in automotive diagnostics.
Project Manager: Mr.D.Prakash
Expert in Predective maintenance & Level 3 Certified Vibration Analyst

3. INTRODUCTION
Problem
 Manual Monitoring Inefficiencies: Manual
monitoring of automobile performance and
maintenance is time consuming and error-prone.
 Costly Downtime: Inadequate monitoring leads to higher
costs and potential downtime in critical situations and on-
road situations.

5. • Early Detection of Faults​
• Reduced Downtime​
• Improved Safety​
• Increased Equipment
Lifespan​
• Cost Effective​
​

7. METHODOLOGY
Machine
Specificatio
n
Mounting
Sensor on
Machine
(DAQ) Data
Acquisition
System
Computer
Collected Data
Analysis
 Time Series Analysis
 FFT
 Recurrence Plots
 Finding Extreme
Event
 Forecasting (ML)
Identification of
Defect
Classification of
Defect
Decision Making &
Predictive Report
Generation
AI based
Prescriptive
Report

8. Parts Prone to
faults

9. Predictive Maintenance
• Predictive maintenance is the strategy that
organizations use to estimate and plan their
operational equipment's maintenance
schedule.
• The strategy is designed to optimize
equipment performance and lifespan.
• Predictive maintenance solutions integrate
sensor data with business operational data
and apply analytics based on artificial
intelligence (AI)
• Predictive maintenance involves monitoring,
analysis, and action based upon gathered
insights.

10. Prescriptive Maintenance
• Prescriptive maintenance represents a cutting-
edge approach to asset management, utilizing
advanced analytics and machine learning to
predict maintenance needs and optimize
equipment performance by prescribing
suggestions
• Advanced asset condition monitoring solutions,
like vibration sensors, help enable pattern
detection and bring reliability teams closer to
prescriptive maintenance
• Leverage historical data and real-time data
• Optimize maintenance operations
• Minimize downtime and increase efficiency.

11. MOST COMMON FAULTS
Noisy Engine: Splattering,
knocking, or rattling sound,
it could be a signal for
trouble.
Bad Wheel Bearings: You
can identify a bad wheel
bearing by listening to the
road sound while turning
slowly
Electrical Faults: Affects
vehicle’s performance and
may be detected through
irregular vibrations.
Fuel Injector Issues:
Problems with the fuel
system can also cause the
vehicle to vibrate unusually.
Cooling System Problems:
Issues with the cooling
system can lead to
overheating, which might
cause the vehicle to
vibrate.
Malfunctioning Sensors: If
sensors malfunction, it can
lead to a variety of
problems, including unusual
vibrations.

12. DATA ACQUISITION
• Data collection: Vibration data is derived from sensors
placed on machinery or vehicle parts, measuring their
vibration patterns and translating them into electrical
signals.
• Signal conditioning: The electrical signals generated by
the sensors are amplified and filtered to remove any noise
or interference. This process is known as signal
conditioning.
• Data acquisition: The conditioned signals are then
digitized and recorded using a data acquisition system
that converts the analog signals into digital signals that
can be stored and processed by a computer.
• Data processing: The digitized signals are then processed
to create datasets in the format of x, y values such that
time vs amplitude and analyzing the vibration patterns
of the machinery and identifying any anomalies or faults.
• Data storage: The datasets are stored in a database or
file system for further analysis and processing. The
datasets can be used to train machine learning models or
to perform predictive maintenance on the machinery.

13. ANALYSIS
1. Time series analysis:
2. FFT (fast Fourier transform)
3. Extreme event
4. Recurrence
5. Forecasting(ML)

14. HOLISTIC FAULT IMPACT ANALYSIS
Unlike traditional AI fault prediction systems that limits the
prediction to fault occurrence, we aim to analyze the
phases as well as impact of a fault occurring.
This holistic approach allows for a more comprehensive
understanding of the fault and its potential implications,
leading to more effective preventive measures and
maintenance strategies.
This is a significant novelty as it moves beyond isolated
fault predictions to a more interconnected and systemic
view of fault impacts. This could potentially lead to
improvements in overall system resilience and reliability.

15. Prospects for Advancement
 Integration with AI and IoT: Collaborative Utilization of artificial intelligence (AI) and Internet of
Things (IoT) capabilities to create smart systems. These communicate real-time data condition to
the vehicle's dashboard providing detailed analytics and maintenance reminders.
 Sensor Fusion Technology: Combine various sensor technologies to create a more comprehensive and
accurate monitoring system. This fusion of sensors can provide a holistic view of system health.
 Self-Diagnosis and Reporting: Develop a self-diagnostic system that continuously evaluates and
automatically generates detailed reports for both the driver and service centers thus improving
safety.
 Wireless Monitoring: Possibility to introduce wireless monitoring systems that transmit data to the
cloud. This data can be accessed by the driver, service centers, or even manufacturers
 Customizable Alerts: Allow drivers to customize their alert preferences based on their driving
habits, preferences, or urgency levels, giving them more control and personalization.

19. Thank You