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中文摘要：
为解决在少量油液样本条件下发动机磨损故障诊断难的问题,提出一种改进半监督模糊C-均值聚类算法（Improved semi-supervised fuzzy c-means clustering algorithm,ISS-FCM）。定义一种优化的目标函数,将无标签样本与训练样本间的平均距离度量考虑在内并赋予其一定权值,以引导聚类过程。为避免随机初始化划分矩阵使聚类结果陷入局部极值,利用训练样本对划分矩阵进行初始化。由于原始油液数据的聚类趋势不明显,不能有效描述发动机的磨损状态,利用自回归（Autoregression,AR）模型从油液光谱数据中提取出残差方差特征。结合某型履带车辆发动机台架试验,利用所提ISS-FCM算法对油液原子发射光谱测量数据进行分析,成功诊断出该发动机的拉缸和烧瓦故障。试验结果证明该方法在发动机磨损故障诊断领域的有效性。
英文摘要：
A improved semi-supervised fuzzy c-means clustering algorithm（ISS-FCM） is proposed to diagnose engine wear faults with small oil samples.An optimized objective function,which is defined through introducing average distance measure between unlabeled samples and training samples with weighting values,is used to conduct the clustering process.To avoid local extrema originating from initialing partition matrix randomly,the training samples are utilized in partition matrix initialing work.By reason that engine wear condition can not be effectively characterized by original oil data with unobvious cluster trendency,Autoregression（AR） model is used to abstract the residual variance features from oil data.The atomic emission spectrometric oil data of engine bench test are analyzed with the proposed method.The cylinder scoring and bushing ablating faults are diagnosed successfully.Experimental results demonstrate the validity of the presented method in the field of engine wear fault diagnosis.
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