Model-based fault detection and diagnosis for diesel engines

Schwarte, Anselm; Kimmich, Frank; Isermann, Rolf

doi:10.1007/bf03227560

Cited by 10 publications

(1 citation statement)

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“…A large number of diagnostic solutions exist for specific engine types, such as diesel engines (Kimmich et al, 2005;Schwarte et al, 2002) and spark-ignited automotive engines (Gagliardi et al, 2018;Svärd et al, 2013), or for subcomponents, such as the common rail system (Clever and Isermann, 2010). As these methods were expressly developed for certain engine types and, thus, for certain fault types, they are not suitable for general application on engine test beds.…”

Section: Introductionmentioning

confidence: 99%

A modular adaptive residual generator for a diagnostic system that detects sensor faults on engine test beds

Wohlthan¹,

Pirker²,

Wimmer

2022

J. Sens. Sens. Syst.

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Abstract. It is a great challenge to apply a diagnostic system for sensor fault detection to engine test beds. The main problem is that such test beds involve frequent configuration changes or a change in the entire test engine. Therefore, the diagnostic system must be highly adaptable to different types of test engines. This paper presents a diagnostic method consisting of the following steps: residual generation, fault detection and fault isolation. As adaptability can be achieved with residual generation, the focus is on this step. The modular toolbox-based approach combines physics-based and data-driven modeling concepts and, thus, enables highly flexible application to various types of engine test beds. Adaptability and fault detection quality are validated using measurement data from a single-cylinder research engine and a multicylinder diesel engine.

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