Abstract

This paper introduces and applies the Scalable Data-based Diagnostic Concept. At its core, the concept consists of (Kernel) Principal Component Analysis (PCA) and Autoencoder (AE), which are used to perform accurate fault diagnosis in technical systems, e.g. in automotive or railroad sectors, including various sub-methods for fault detection, identification and isolation. The analysis of real automotive fault cases is done, where a new smoothed comparative detection chart is presented. The findings prove the necessity of choosing the right method, regarding efficiency and the inherent data structure, which is one of the main objectives of the comprehensive scalable diagnostic concept.

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