关键词: 纺纱过程, 质量波动, 异常检测, 局部离群因子, 深度置信网络, 特征空间变换

Abstract: The spinning process parameters are numerous and interrelated,which blurs the important information between the process parameters and the yarn quality indicators.The quality fluctuation of the spinning process caused by strong coupling of the spinning process parameters is difficult to detect and control.Aiming at this problem,an abnormal detection method for quality fluctuation of multi-association parameters in spinning process based on feature space transformation was proposed.The relationship between the parameters of the spinning process was analyzed,and the partial least squares method was used to eliminate the correlation between the parameters,and a feature space with orthogonality was obtained.Then the new feature space data was used to calculate the Local Outlier Factor (LOF) statistics and determine whether the spinning process was stable,locate the abnormal fluctuation area.On this basis,the abnormal fluctuation data after correlation analysis was used as the input of the deep belief network model to identify the type of abnormal fluctuations in the spinning process.The algorithm analysis was verified,and the result showed that the proposed model could improve the abnormal detection accuracy of multi-related parameters in the spinning process to 98.2%.

Key words: spinning process, quality fluctuation, abnormal detection, local outlier factor, deep belief network, feature space transformation