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Multivariate statistical analysis strategy for multiple misfire detection in internal combustion engines

机译：内燃机多重失火检测的多元统计分析策略

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摘要

This paper proposes a multivariate statistical analysis approach to processing the instantaneous engine speed signal for the purpose of locating multiple misfire events in internal combustion engines. The state of each cylinder is described with a characteristic vector extracted from the instantaneous engine speed signal following a three-step procedure. These characteristic vectors are considered as the values of various procedure parameters of an engine cycle. Therefore, determination of occurrence of misfire events and identification of misfiring cylinders can be accomplished by a principal component analysis (PCA) based pattern recognition methodology. The proposed algorithm can be implemented easily in practice because the threshold can be defined adaptively without the information of operating conditions. Besides, the effect of torsional vibration on the engine speed waveform is interpreted as the presence of super powerful cylinder, which is also isolated by the algorithm. The misfiring cylinder and the super powerful cylinder are often adjacent in the firing sequence, thus missing detections and false alarms can be avoided effectively by checking the relationship between the cylinders.
机译：本文提出了一种多元统计分析方法来处理瞬时发动机转速信号，以定位内燃机中的多个失火事件。按照三步过程，从瞬时发动机转速信号中提取特征向量来描述每个气缸的状态。这些特征向量被认为是发动机循环的各种过程参数的值。因此，可以通过基于主成分分析（PCA）的模式识别方法来确定失火事件的发生和识别失火气缸。由于阈值可以在没有操作条件信息的情况下自适应地定义，因此在实践中可以轻松实现该算法。此外，扭转振动对发动机转速波形的影响被解释为存在超强气缸，该气缸也被算法隔离。点火失败的气缸和超大功率气缸通常在点火顺序中相邻，因此可以通过检查气缸之间的关系来有效地避免漏检和误报警。

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