Scholarlink

<p>The instantaneous speed of diesel engines contains an abundance of information, regarding fuel supply stability and individual cylinder performance. Real-time acquisition of accurate instantaneous speed is crucial for monitoring cylinder-to-cylinder uniformity, diagnosing faults, and enabling precise speed control in marine diesel engines. However, measurement noise distorts the signal, which makes it significantly difficult to monitor the effective information in the actual operation. To address this challenge, this paper proposes a novel real-time filtering method using an extended Kalman filter (EKF). According to the characteristics of crankshaft instantaneous speed of diesel engine, a dedicated state-space model is derived. The EKF utilizes the model to perform real-time feedback and rolling optimization effectively suppressing noise. The performance of the method proposed is validated using both simulated signals and experimental data from a four-cylinder marine diesel engine. Simulation and experimental results demonstrate that the coefficient of determination <i>R</i><sup>2</sup> between the estimated and actual speed reaches 99.83%, while the signal-to-noise ratio (SNR) improves by above 10% on average across different operating conditions. This enhancement enables reliable real-time engine state monitoring and control.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Real-time Estimation of Instantaneous Speed in Diesel Engines Based on the Extended Kalman Filter

  • 期刊论文

  • 发表日期：

    2026年1月16日

查看全文
  • Zhongxin Shi,
  • Hongzi Fei,
  • Liuping Wang,
  • Bingxin Liu,
  • Yilin Liu

摘要

The instantaneous speed of diesel engines contains an abundance of information, regarding fuel supply stability and individual cylinder performance. Real-time acquisition of accurate instantaneous speed is crucial for monitoring cylinder-to-cylinder uniformity, diagnosing faults, and enabling precise speed control in marine diesel engines. However, measurement noise distorts the signal, which makes it significantly difficult to monitor the effective information in the actual operation. To address this challenge, this paper proposes a novel real-time filtering method using an extended Kalman filter (EKF). According to the characteristics of crankshaft instantaneous speed of diesel engine, a dedicated state-space model is derived. The EKF utilizes the model to perform real-time feedback and rolling optimization effectively suppressing noise. The performance of the method proposed is validated using both simulated signals and experimental data from a four-cylinder marine diesel engine. Simulation and experimental results demonstrate that the coefficient of determination R2 between the estimated and actual speed reaches 99.83%, while the signal-to-noise ratio (SNR) improves by above 10% on average across different operating conditions. This enhancement enables reliable real-time engine state monitoring and control.