Engine downsizing and boosting have been recognized as effective strategies for improving engine efficiency. However, operating the engines at high load promotes abnormal combustion events, such as pre-ignition and potential superknock. Currently the most effective method for detecting pre-ignition is by using in-cylinder pressure sensors that have high precision and sensitivity, but also high cost. Due to rapid advances in automotive technology such as autonomous driving, computer-aided designs and future connectivity, we propose to use a complimentary data-driven strategy for diagnosing abnormal combustion events. To this end, a data-driven diagnostics approach for pre-ignition detection with deep neural networks is proposed. The success of convolutional neural networks (CNNs) in object detection and recurrent neural networks (RNNs) in sequence forecasting inspired us to develop these models for pre-ignition detection. For a cost-effective strategy, we use data from less expensive sensors, such as lambda and low-resolution exhaust back pressure (EBP), instead of high resolution in-cylinder pressure measurements. The first deep learning model is combined with a commonly used dimensionality reduction tool–Principal Component Analysis (PCA). The second model eliminates this step and directly processes time-series data. Results indicate that the first model with reduced input dimensions, and correspondingly smaller size of the network, shows better performance in detecting pre-ignition cycles with an F1 score of 79%. Overall, the proposed deep learning approach is a promising alternative for abnormal combustion diagnostics using data from low resolution sensors.

发动机小型化和增压是提高发动机效率的有效策略。然而，在高负载下运转发动机会促进异常燃烧事件，例如预点火和潜在的超爆震。当前，检测提前点火的最有效方法是使用缸内压力传感器，该传感器具有高精度和灵敏度，但成本也很高。由于自动驾驶，计算机辅助设计和未来连接性等汽车技术的飞速发展，我们建议使用互补的数据驱动策略来诊断异常燃烧事件。为此，提出了一种数据驱动的诊断方法，用于使用深度神经网络进行点火前检测。卷积神经网络（CNN）在对象检测中的成功以及递归神经网络（RNN）在序列预测中的成功启发了我们开发用于点火前检测的这些模型。为了获得具有成本效益的策略，我们使用来自价格较低廉的传感器（例如lambda和低分辨率排气背压（EBP））的数据，而不是高分辨率缸内压力测量。第一个深度学习模型与常用的降维工具-主成分分析（PCA）相结合。第二个模型消除了此步骤，直接处理时间序列数据。结果表明，具有减小的输入尺寸和相应较小的网络尺寸的第一个模型在检测点火前循环方面表现出更好的性能，F1得分为79％。全面的，