噪声与振动控制2019，Vol.39Issue(3)：1-6,6.DOI:10.3969/j.issn.1006-1355.2019.03.001
一种基于流形学习和KNN算法的柴油机工况识别方法
Operating Mode Identification Method for Diesel Engines based on Manifold Learning and KNN Algorithm
摘要
Abstract
Under different load conditions, the signals of vibration, temperature, speed, etc., of diesel engines are significantly different, and the fault signal characteristics of the engine unit are often submerged in signals that change drastically with the load change. Therefore, the diesel engine’s fault monitoring and diagnosing under variable load conditions is difficult and the drastically changed signals always troubles the actual fault diagnosis. This paper presents an operating mode identification method for diesel engines based on manifold learning and KNN algorithm, which lays a foundation for fault monitoring and early warning of diesel engines under variable load conditions. The method combines the multi-source signal features of the unit to construct the feature vector. The feature reduction and sensitive feature extraction of the feature vector is achieved through the manifold learning t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm. The K-Nearest Neighbor (KNN) classification algorithm is used to complete the automatic classification of diesel engine’s operating load status. The diesel engine signal under normal and fault conditions verifies the effectiveness and practicality of this method.关键词
振动与波/柴油机/变负荷/流形学习/KNN/敏感特征Key words
vibration and wave/ diesel engine/ variable load/ manifold learning/ KNN/ sensitive feature分类
能源科技引用本文复制引用
江志农,赵南洋,夏敏,赵飞松,高佳丽,张进杰..一种基于流形学习和KNN算法的柴油机工况识别方法[J].噪声与振动控制,2019,39(3):1-6,6.基金项目
国家自然科学基金资助项目(11572125,51575176) （11572125,51575176）
湖南省自然科学基金资助项目(2017JJ2084) （2017JJ2084）
