Case Study
How UST's cloud-based vehicle health monitoring system revolutionized predictive maintenance for a global automotive manufacturer
OUR CLIENT
This company is a leading global automobile manufacturer renowned for producing diverse vehicles, including cars, trucks, buses, and military vehicles. It operates in 26 countries across four continents and is strongly committed to innovation, quality, and reliability. The company is dedicated to sustainability and is at the forefront of developing electric and hybrid vehicles to minimize environmental impact. With a vast network of over 250 dealerships, it provides comprehensive after-sales services to its customers. The company’s extensive portfolio caters to various market segments, solidifying its position as a critical player in the automotive industry. Through multiple production facilities and research centers worldwide, it focuses on advanced engineering and design to meet the evolving needs of its global customer base.
THE CHALLENGE
Reactive maintenance approach—Lack of real-time data and predictive capabilities drive up costs and increase downtime across the fleet
The client struggled to predict the remaining useful life (RUL) of critical components, such as brake pads and clutch plates. Its reliance on fixed maintenance schedules resulted in inefficiencies, leading to premature replacements or unexpected failures and increasing maintenance costs and downtime. Evolving consumer expectations around vehicle safety and operational efficiency pushed the client to seek a solution that would monitor and predict component wear tailored to individual driving profiles. It needed a cloud-based vehicle health monitoring application capable of processing live telemetry data to deliver real-time insights and optimize maintenance schedules. It could not deliver tailored, data-driven maintenance solutions without predictive maintenance capabilities.
THE TRANSFORMATION
Cloud-based vehicle health monitoring system enables predictive maintenance and optimizes fleet performance
UST implemented a cloud-based vehicle health monitoring system that addressed the client’s RUL challenges. By extracting controller area network (CAN) data and integrating it with a cloud service, UST facilitated real-time telemetry data transmission and processing, providing crucial insights into component health. This scalable and adaptable approach not only improved vehicle safety and reliability but also reduced maintenance costs and downtime by enabling timely interventions based on accurate, data-driven predictions.
At the heart of the solution was the time and mileage-based prediction with vehicle learning (TMPVL) algorithm, a machine-learning model designed to predict the RUL of vehicle components based on driving profiles and conditions. This predictive maintenance capability reduced unexpected failures and optimized maintenance schedules.
Furthermore, the integration of firmware over the air (FOTA) and configuration over the air (COTA) capabilities enabled seamless updates and configurations across the fleet. This remote orchestration minimized the need for manual interventions and downtime, ensuring that all vehicles consistently operated with the latest software and configurations, enhancing overall system reliability.
Advanced visualization tools provided drivers and fleet managers with immediate access to actionable insights via a smart tablet interface and web dashboard. This enabled real-time vehicle data monitoring, facilitating better decision-making and data-driven maintenance strategies.
With this system, the client can accurately predict component wear, improving vehicle safety, performance, and reliability while lowering maintenance costs and downtime. The solution's scalable nature allows for adaptability across the entire fleet, driving greater operational efficiency and customer satisfaction.
THE IMPACT
Predictive maintenance solution reduces downtime, lowers maintenance costs, and enhances vehicle safety with real-time insights for diverse vehicle systems
The implementation of UST's cloud-based vehicle health monitoring system delivered these significant operational improvements:
- Comprehensive fleet management and control—The client can now remotely monitor and manage their entire fleet through a single dashboard, providing seamless insights into the health and performance of critical components, such as brake pads and clutch plates.
- Increased operational efficiency—Integrating telemetry data and machine learning enabled proactive predictive maintenance, reducing downtime, minimizing manual interventions, and refining maintenance schedules for improved efficiency.
- Enhanced safety and reliability—By accurately predicting the RUL of vehicle components, the client can take prompt actions to prevent unexpected failures, ensuring consistent vehicle safety and reliability.
- Seamless remote updates and configurations—With FOTA and COTA capabilities, the client can quickly deploy software and configuration updates across their fleet, ensuring all vehicles are running the latest versions without needing physical interventions.
- Actionable insights for continuous improvement—The system’s advanced analytics dashboard delivers valuable insights into vehicle performance, enabling data-driven decision-making and helping original equipment manufacturers (OEMs) understand usage patterns for ongoing enhancements.
- Cost-effectiveness—By optimizing maintenance schedules and reducing downtime through predictive maintenance, the client has significantly lowered operational costs associated with unexpected failures and manual interventions.
Discover how UST’s predictive maintenance solutions can transform your fleet operations, enhance efficiency, and improve vehicle safety and performance. Unlock real-time insights, streamline maintenance processes, and achieve higher customer satisfaction through proactive, data-driven solutions.