基于改进深度学习算法的船舶柴油机故障诊断技术

随着数据挖掘技术的发展,深度置信网络(DBN)这类深度学习算法被越来越多运用到工程领域。在故障诊断领域,结合DBN强大的自适应特征提取和非线性映射能力,可以摆脱以往对专家经验的依赖。基于此,本文为有效地监测柴油机气缸运行状态,提出一种基于改进深度学习算法的船舶柴油机故障诊断技术。先将原始信号的频域形式输入DBN当中,采用蚱蜢优化算法(GOA)搜索DBN的最优参数组合,并建立起最佳的柴油机气缸故障诊断模型。经测试验证,本文提出的诊断模型能够准确识别柴油机气缸运行状态并进行故障诊断,诊断率可以达到99.5%以上,具有较好的工程实用价值。

Fault diagnosis technology of marine diesel engine based on improved deep learning algorithm

With the development of data mining technology, deep learning algorithms such as deep belief network(DBN) are widely used in engineering. In the field of fault diagnosis, the dependence on expert experience could be avoidable with the strong adaptive feature extraction and nonlinear mapping ability of DBN. Based on this, in order to find out the abnormal operation of diesel engine cylinder efficiently and timely as well as diagnose its fault accurately, a marine diesel engine fault diagnosis technology based on improved deep learning algorithm is proposed in this paper: Inputting the original signal in frequency domain into DBN, searching the optimal parameter combination of DBN through Grasshopper optimization algorithm(GOA), and establishing the optimal cylinder fault diagnosis model of diesel engine. The test results indicate that the diagnosis model is able to identify the running state of diesel engine cylinder accurately, and is able to carry out fault diagnosis with the diagnosis rate up to more than 99.5%, which proves to be of good engineering practical value.