Misfire Detection Using Crank Speed and Long Short-Term Memory Recurrent Neural Network

Xinwei Wang, Pan Zhang, Wenzhi Gao, Yong Li, Yanjun Wang and Haoqian Pang
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Xinwei Wang: State Key Laboratory of Engine Reliability, Weifang 261061, China
Pan Zhang: State Key Laboratory of Engines, Tianjin University, Tianjin 300350, China
Wenzhi Gao: State Key Laboratory of Engines, Tianjin University, Tianjin 300350, China
Yong Li: State Key Laboratory of Engines, Tianjin University, Tianjin 300350, China
Yanjun Wang: State Key Laboratory of Engines, Tianjin University, Tianjin 300350, China
Haoqian Pang: State Key Laboratory of Engines, Tianjin University, Tianjin 300350, China

Energies, 2022, vol. 15, issue 1, 1-24

Abstract: In this work, a new approach was developed for the detection of engine misfire based on the long short-term memory recurrent neural network (LSTM RNN) using crank speed signal. The datasets are acquired from a six-cylinder-inline, turbo-charged diesel engine. Previous works investigated misfire detection in a limited range of engine running speed, running load or misfire types. In this work, the misfire patterns consist of normal condition, six types of one-cylinder misfire faults and fifteen types of two-cylinder misfire faults. All the misfire patterns are tested under wide range of running conditions of the tested engine. The traditional misfire detection method is tested on the datasets first, and the result show its limitation on high-speed low-load conditions. The LSTM RNN is a type of artificial neural network which has the ability of considering both the current input in-formation and the previous input information; hence it is helpful in extracting features of crank speed in which the misfire-induced speed fluctuation will last one or a few cycles. In order to select the engine operating conditions for network training properly, five data division strategies are attempted. For the sake of acquiring high performance of designed network, four types of network structure are tested. The results show that, utilizing the datasets in this work, the LSTM RNN based algorithm can overcome the limitation at high-speed low-load conditions of traditional misfire detection method. Moreover, the network which takes fixed segment of raw speed signal as input and takes misfire or fault-free labels as output achieves the best performance with the misfire diagnosis accuracy not less than 99.90%.

Keywords: engine misfire; pattern recognition; fault detection; LSTM; time-frequency analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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