柴油机振动信号特征参数提取方法及缸内压力信号重构方法的研究
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摘要
近年来，柴油机故障诊断技术在国内外得到了较大的发展，这不仅降低了柴油机故障发生率，也减小了巨大的经济损失。本文在总结和汲取别人研究成果的基础上，结合实际课题要求，以振动信号和缸内压力信号作为故障诊断基础，引入多种信号分析方法，从柴油机表面振动信号以及缸内压力信号中获取故障特征信息，有效实现了柴油机故障诊断。
首先，从柴油机表面振动信号产生的激励源出发，论述了柴油机主要激励源、表面振动信号及它们之间的关系，分析了表面振动信号的传递途径，为柴油机故障诊断提供理论依据。
然后，在实验台架上模拟了气门间隙异常、气门漏气、喷油压力变化以及喷油提前角变化四种柴油机故障，测量了缸盖振动信号、缸内压力信号和瞬时转速信号。由于振动信号为非平稳时变信号，因此，对振动信号进行分析时，采用了以小波分析为主、时域分析和频域分析为辅的方式。首先，为提高信号的信噪比，采用了小波降噪技术对信号进行了处理，然后利用小波包分解技术，将信号分解为不同频带上的时域信号，获得缸盖振动信号的时频域特征参数，再结合时域分析法和频域分析法，得出不同柴油机状态下缸盖振动信号特征参数。将时频域特征参数和时域、频域特征参数相结合，得出不同故障状态下缸盖振动信号特征参数的变化规律，为实现故障监测提供实验依据。同时，柴油机缸内压力是缸内燃烧过程的结果，也是能量从热能向机械能转换的基础，通过对缸内压力曲线的分析可以对柴油机工作状态做出判断，因此，本文利用时域分析法得出了不同故障下缸内压力信号特征参数。通过缸内压力信号和缸盖振动信号特征参数的综合分析判断柴油机故障，提高柴油机故障诊断正确率。
最后，课题对缸内压力信号重构方法进行了研究。目前缸内压力信号多采用直接测量法获得，但直接测量法受到诸多条件的限制，不适于柴油机在线监测，而间接测量法既方便又经济，因此，利用间接测量法获得缸内压力信号有很大的研究价值。目前，间接测量法有瞬时转速法和振动法，课题根据缸盖振动信号、瞬时转速信号和缸内压力信号之间的非线性关系，采用了非线性拟合度较好的BP神经网络建立重构缸内压力信号的模型。通过对瞬时转速信号时域法及频域法和振动信号时域法及频域的对比，得出了四种方法各自的优缺点，并提出了利用振动信号和瞬时转速信号相结合重构缸内压力信号的方法，该方法弥补了前四种方法的不足。最后通过比较最大压力、平均指示压力、最大压力升高率等参数来评价重构信号的优劣，结果表明将振动信号和转速信号相结合重构缸内压力信号效果较好。
In recent years, the technique of diesel engine fault diagnosis, which improves the performance of diesel engines and reduces the economic loss, is quickly developed in domestic and oversea. In this paper, in order to solve practice problems, we adopt several methods to analyze the vibration signal and pressure signal on the basis of former achievements. Using these methods we obtained signal characteristic information for faults and realized diesel engine fault diagnosis.
Firstly, from the starting point of vibration signals formed, the characteristics of actuation sources and their responses and the relations between them are discussed in detail. Meanwhile radiate routes of vibration signals are analyzed. The analysis is the theoretical basis for diesel engine diagnosis.
Secondly, this paper implements the valve clearance, the valve leakage, the injection pressure and the injection advanced angle faults on the test bench. And the vibration signal, the cylinder pressure signal and the crack angle speed were tested and saved. According to the non-stationary feature of the vibration signal of the engine, the signal was mainly analyzed by the wavelet technique. First the signal noise was reduced using the wavelet method so that the signal-to-noise performance was improved. Then the research using wavelet packet decomposes the vibration signal and obtains the time-frequency domain characteristic parameters. Also this paper uses the time or frequency domain methods to obtain the characteristic parameters on the time or frequency domain. By the analysis of the signals, the tendency of the characteristic parameters was generalized. At the mean time, the cylinder pressure signal which is the result of the combustion and is the inversion basis from the heat energy to the mechanical energy was analyzed. In order to identify engine faults more correctly, this paper combines both the vibration and the cylinder pressure characteristic parameters.
At last, this paper studies the methods that how to reconstruction the cylinder pressure signal. At present, the cylinder pressure signal was almost obtained by direct measurement. As direct measurements of the cylinder pressure were restricted by many conditions and not suitable for measurements in vehicles on the road, indirect methods have great potential value for it is economy and convenience. There are two methods for reconstruction, one is using the vibration signal, the other is using the speed signal. Accord to the non-linear feature between the speed signal, vibration signal and the in-cylinder pressure signal, a non-linear model based on BP networks is proposed for the reconstruction. By comparing these methods, the features of the methods were obtained. And the method that combining the speed signal and the vibration signal was proposed. The method makes up for the disadvantages of the former methods. At last, the reconstruction signal was valuated by the parameters. The result shows that the method performs well for reconstruction the cylinder pressure signal.
首先，从柴油机表面振动信号产生的激励源出发，论述了柴油机主要激励源、表面振动信号及它们之间的关系，分析了表面振动信号的传递途径，为柴油机故障诊断提供理论依据。
然后，在实验台架上模拟了气门间隙异常、气门漏气、喷油压力变化以及喷油提前角变化四种柴油机故障，测量了缸盖振动信号、缸内压力信号和瞬时转速信号。由于振动信号为非平稳时变信号，因此，对振动信号进行分析时，采用了以小波分析为主、时域分析和频域分析为辅的方式。首先，为提高信号的信噪比，采用了小波降噪技术对信号进行了处理，然后利用小波包分解技术，将信号分解为不同频带上的时域信号，获得缸盖振动信号的时频域特征参数，再结合时域分析法和频域分析法，得出不同柴油机状态下缸盖振动信号特征参数。将时频域特征参数和时域、频域特征参数相结合，得出不同故障状态下缸盖振动信号特征参数的变化规律，为实现故障监测提供实验依据。同时，柴油机缸内压力是缸内燃烧过程的结果，也是能量从热能向机械能转换的基础，通过对缸内压力曲线的分析可以对柴油机工作状态做出判断，因此，本文利用时域分析法得出了不同故障下缸内压力信号特征参数。通过缸内压力信号和缸盖振动信号特征参数的综合分析判断柴油机故障，提高柴油机故障诊断正确率。
最后，课题对缸内压力信号重构方法进行了研究。目前缸内压力信号多采用直接测量法获得，但直接测量法受到诸多条件的限制，不适于柴油机在线监测，而间接测量法既方便又经济，因此，利用间接测量法获得缸内压力信号有很大的研究价值。目前，间接测量法有瞬时转速法和振动法，课题根据缸盖振动信号、瞬时转速信号和缸内压力信号之间的非线性关系，采用了非线性拟合度较好的BP神经网络建立重构缸内压力信号的模型。通过对瞬时转速信号时域法及频域法和振动信号时域法及频域的对比，得出了四种方法各自的优缺点，并提出了利用振动信号和瞬时转速信号相结合重构缸内压力信号的方法，该方法弥补了前四种方法的不足。最后通过比较最大压力、平均指示压力、最大压力升高率等参数来评价重构信号的优劣，结果表明将振动信号和转速信号相结合重构缸内压力信号效果较好。
In recent years, the technique of diesel engine fault diagnosis, which improves the performance of diesel engines and reduces the economic loss, is quickly developed in domestic and oversea. In this paper, in order to solve practice problems, we adopt several methods to analyze the vibration signal and pressure signal on the basis of former achievements. Using these methods we obtained signal characteristic information for faults and realized diesel engine fault diagnosis.
Firstly, from the starting point of vibration signals formed, the characteristics of actuation sources and their responses and the relations between them are discussed in detail. Meanwhile radiate routes of vibration signals are analyzed. The analysis is the theoretical basis for diesel engine diagnosis.
Secondly, this paper implements the valve clearance, the valve leakage, the injection pressure and the injection advanced angle faults on the test bench. And the vibration signal, the cylinder pressure signal and the crack angle speed were tested and saved. According to the non-stationary feature of the vibration signal of the engine, the signal was mainly analyzed by the wavelet technique. First the signal noise was reduced using the wavelet method so that the signal-to-noise performance was improved. Then the research using wavelet packet decomposes the vibration signal and obtains the time-frequency domain characteristic parameters. Also this paper uses the time or frequency domain methods to obtain the characteristic parameters on the time or frequency domain. By the analysis of the signals, the tendency of the characteristic parameters was generalized. At the mean time, the cylinder pressure signal which is the result of the combustion and is the inversion basis from the heat energy to the mechanical energy was analyzed. In order to identify engine faults more correctly, this paper combines both the vibration and the cylinder pressure characteristic parameters.
At last, this paper studies the methods that how to reconstruction the cylinder pressure signal. At present, the cylinder pressure signal was almost obtained by direct measurement. As direct measurements of the cylinder pressure were restricted by many conditions and not suitable for measurements in vehicles on the road, indirect methods have great potential value for it is economy and convenience. There are two methods for reconstruction, one is using the vibration signal, the other is using the speed signal. Accord to the non-linear feature between the speed signal, vibration signal and the in-cylinder pressure signal, a non-linear model based on BP networks is proposed for the reconstruction. By comparing these methods, the features of the methods were obtained. And the method that combining the speed signal and the vibration signal was proposed. The method makes up for the disadvantages of the former methods. At last, the reconstruction signal was valuated by the parameters. The result shows that the method performs well for reconstruction the cylinder pressure signal.
引文
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