摘要
在详细分析现有MSPCA模型不足的基础上,借助在线多尺度滤波(OLMS),提出了一种多变量统计过程的在线监测方法,并将其应用于传感器故障诊断。该方法中,首先在固定窗长的数据窗口内用边缘校正滤波器对信号进行小波分解,然后用小波阈值滤波对分解的小波系数进行消噪,并借助该固定窗长的移动窗口将小波变换和自适应PCA结合起来对数据进行在线多尺度建模,从而避免了直接对信号进行消噪所造成的时间浪费,提高了故障诊断率。最后以6135D型柴油机在严重漏气下的8个振动信号的故障诊断为例进行故障分析,结果表明了所提方法的可行性和实用性。
By analyzing shortages of current MSPCA model, an on-line multi-variable statistical process monitoring method is proposed, which uses some concepts from online multi-scale filtering and can be applied to sensor fault diagnosis. In the method, wavelet decomposition is employed to the signals using edge correction filter in a fixed-length data window, and then wavelet denoising is conducted with wavelet threshold filtering. Next, an on-line multi-scale model is constructed for data combining wavelet transformation and adaptive PCA in the previous data window. This model avoids time waste in direct signal denoising and reduces time cost in multi-scale data with conventional PCA, which eventually increases accuracy in fault diagnosis. Experiments on eight vibration signals of 6135D diesel engine under severe leak condition prove the practicability and feasibility of the proposed method.
出处
《重庆大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2010年第6期128-133,共6页
Journal of Chongqing University
基金
国家自然科学基金资助项目(60974090)
教育部博士点基金资助项目(200806110021)
