基于人工神经网络的柴油机失火故障诊断

车载诊断系统在诊断失火故障时,采用基于曲轴段角加速度和阈值规则相结合的方法,该方法在内燃机高速轻载运行时诊断单缸完全失火工况存在一定的局限性。通过对比分析失火和正常工况下曲轴瞬时转速的幅频和相频特征,提取不同谐次的幅值和相位信息,结合人工神经网络作为故障模式识别工具,得到了一种改善方法。通过台架实验,对此改善方法进行了单缸完全失火、两缸完全失火和单缸一定程度失火的故障诊断测试。结果表明,在实验条件下该方法可以有效识别不同的失火模式,并可在单缸失火模式下实现失火程度判别。同时,该方法通过少量工况数据训练神经网络,即可实现一定转速范围内的失火诊断,可行性强,可用于发动机失火故障在线诊断。

Misfire Detection of Diesel Engine Based on Artificial Neural Networks

Engine misfire can be detected through on-board diagnostics (OBD) by means of instantaneous crankshaft angular acceleration analyzing and threshold-based classification rules. This method has certain limitations in diagnosing single-cylinder complete misfire conditions when the internal combustion engine is running at high speed and light load. The amplitude-frequency and phase-frequency characteristics of the instantaneous crankshaft rotational speed under misfire and normal operating conditions are compared. By extracting the amplitude and phase information of different harmonics and combining the artificial neural network (ANN) as a fault pattern recognition tool, an approach is proposed. Through bench experiments, the improved method is tested for fault diagnosis of complete misfire of single cylinder, complete misfire of two cylinders and certain degree of misfire of single cylinder at different speed on a diesel engine test-rig. The results show that the method can effectively identify different misfire modes under experimental conditions, and identify the misfire severity and cylinder location in the single-cylinder misfire mode with high accuracy. At the same time, the method can realize the misfire diagnosis within a certain speed range by training the neural network through a small amount of working condition data. It has strong feasibility and is expected to be used for the on-line diagnosis of engine misfire faults.