Data-driven Anomaly Detection of Engine Knock based on Automotive ECU

  • Leonardo Tomasi Francis Universidade Federal de Santa Catarina
  • Victor Elízio Pierozan Universidade Federal de Santa Catarina
  • Giovani Gracioli Universidade Federal de Santa Catarina
  • Gustavo Medeiros de Araujo Universidade Federal de Santa Catarina

Resumo


In the automotive industry, the study of internal combustion engines (ICE) has massively been studied to identify the occurrence of some failures, such as engine knock [1], [2]. The occurrence of this phenomenon on the engine directly affects the engine maintenance cost and longer engine life. The use of machine learning for failure detection is highlighted [3]-[6]. An investigation was carried out by performing experiments with a Renault Sandero car, collecting some sets of variables for batch analysis. In this paper, we use artificial intelligence techniques with a data-driven approach, more specifically, machine learning, to detect the phenomenon of engine knock. The investigation was conducted with a feature extraction classifier, AutoEnconder Dense and Convolutional, SVM, and Isolated Forest. Finally, the best result achieved was 81% considering a feature extraction classifier on the collection of variables defined.

Palavras-chave: Engine Failure Detection, Machine Learning, Classification Problem, Engine Knock, Fault Detection
Publicado
21/11/2022
FRANCIS, Leonardo Tomasi; PIEROZAN, Victor Elízio; GRACIOLI, Giovani; DE ARAUJO, Gustavo Medeiros. Data-driven Anomaly Detection of Engine Knock based on Automotive ECU. In: SIMPÓSIO BRASILEIRO DE ENGENHARIA DE SISTEMAS COMPUTACIONAIS (SBESC), 12. , 2022, Fortaleza/CE. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2022 . p. 85-92. ISSN 2237-5430.