Real-time abnormality detection and classification in diesel engine operations with convolutional neural network

作者：

Highlights：

• Fluctuations in the rotational speed of prime mover are translated in to a CAD signal to observe the behavior of the engine IC process.

• Combined detection of cylinder misfires and engine load conditions is an important tool in advanced engine control systems.

• A CNN-based classifier extracted the feature from CAD signals and detected multiple irregularities in each IC cycle of the engine operation in real-time.

• Low computation complexity and a lower prediction time by proposed shallow CNNbased classifier.

摘要

•Fluctuations in the rotational speed of prime mover are translated in to a CAD signal to observe the behavior of the engine IC process.•Combined detection of cylinder misfires and engine load conditions is an important tool in advanced engine control systems.•A CNN-based classifier extracted the feature from CAD signals and detected multiple irregularities in each IC cycle of the engine operation in real-time.•Low computation complexity and a lower prediction time by proposed shallow CNNbased classifier.

论文关键词：Abnormality detection,Convolutional neural network (CNN),Engine load,Misfire,Multi-class classification,Signal processing

论文评审过程：Received 16 April 2021, Revised 23 August 2021, Accepted 12 November 2021, Available online 15 December 2021, Version of Record 3 January 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.116233