Diagnostics Methodology for Turbocharger System Using Data Driven Models Daimler Trucks Innovation Center

Format:

Book

Conference/Event

Author/Creator:

Jagtap, Virendra Shashikant, author.

Contributor:

Ganguly, Gourav

Mitra, Partha

Patidar, Sachin

Conference Name:

SAENIS TTTMS Thermal Management Systems Conference (2025-11-06 : Guwahati, India)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2025

Summary:

In a conventional powertrain driven by Internal combustion (IC) engines, turbocharger (TC) is a key component for enhancing performance and efficiency. Predominantly turbochargers are used to serve multiple purposes of downsizing, increased power, better fuel efficiency, reduced emissions, and improved performance at high altitudes. TC is responsible for fulfilling the air mass requirement of the engine at different operating conditions. Failure of TC system leads to abnormal engine operation. If the TC hardware is beyond repair, the associated replacement cost is very high. Ultimately, a predictive diagnostics approach is required to identify the issue with TC so that the failure of TC could be avoided.The proposed methodology uses advanced artificial intelligence technique called recurrent neural network (RNN) and long short-term memory (LSTM) network for predicting faults in a typical TC system. In this study, actual values of TC speed and boost pressure are obtained from physical sensors present on the vehicle whereas estimated values of TC speed and boost pressure are obtained from data driven models. To enable predictive diagnostic of TC, a fault detection unit is incorporated which differentiates between the various fault conditions such as TC hardware fault or sensor fault.For initial validation, this methodology was applied to a healthy TC system to ensure that fault conditions were not getting active. For further validation, a faulty TC system was selected. Using the proposed approach, degradation in the boost pressure and TC speed for faulty TC was successfully identified. Various fault conditions and steps involved in the fault detection are clearly described

Notes:

Vendor supplied data

Publisher Number:

2025-28-0404

Access Restriction:

Restricted for use by site license