Leveraging Telematics Big Data Analytics for Real-Time Monitoring of Catalytic Converter Failures and Root Cause Identification for Emission Compliance Maruti Suzuki India, Limited

Format:

Book

Conference/Event

Author/Creator:

Dev, Triyambak, author.

Contributor:

AGARWAL, Shashank

Bose, Sushant

Chandra, Animesh

Garg, Amit

Kalkur, Varun

Modak, Saikat

Paul, Varsha

Prasad, Kakaraparti Agam

Sundararaman, Venkataraman

Conference Name:

Symposium on International Automotive Technology (2026) (2026-01-28 : Pune, India)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2026

Summary:

The automotive industry is continuously evolving at high pace to meet rising customer expectations, reliability, reduced maintenance, and most relevant, compliance with stringent emission norms. Traditionally, the analysis of vehicle emissions relies heavily on periodic inspections and manual checks. These conventional methods are often time-consuming, prone to human error, and lack the ability to provide real-time insights. Also, identifying failures due to non-manufacturing issues require meticulous physical inspections and historical data reviews, which are not always accurate or timely. Telematics or Connected cars technology being one of the major technological innovations in recent times revolutionizes these processes by enabling real-time data exchange between vehicles and external systems. The current study presents an innovative approach to utilizing telematics data for real-time monitoring of vehicle emissions and pinpointing Catalytic converter failures by analyzing vehicle probe data retrieved from telematics system aimed to identify fuel adulteration events or CNG kit retrofitments that can compromise vehicle performance and longevity. The methodology involves continuous data transmission from telematics devices to the cloud, where the system monitors vehicle emissions in real-time and alerts customers of potential failures. Further to identify the cause of failure, the telematics raw data is processed and aggregated for analysis using statistical models to detect potential fuel tank cleaning due to incorrect or adulterated fuel filling done in the past. This process is validated through a two-level model, ensuring accuracy in detecting fuel adulteration instances. The key advantage of this approach lies in its server-based high-speed processing, which eliminates the resource burden involved during physical inspection and testing of failed parts and enhances detection capabilities compared to existing solutions. This innovative method not only improves vehicle maintenance and customer satisfaction but also ensures compliance with emission norms, thereby contributing to a cleaner and more sustainable environment

Notes:

Vendor supplied data

Publisher Number:

2026-26-0591

Access Restriction:

Restricted for use by site license