Machine Fault Diagnosis of Fused Filament Fabrication Process with Physics-Constrained Dictionary Learning
Machine Fault Diagnosis of Fused Filament Fabrication Process with Physics-Constrained Dictionary Learning
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DOI：
10.1016/j.promfg.2021.06.071
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Yanglong Lu;Yan Wang
中科院分区：
文献类型：
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作者：
Yanglong Lu;Yan Wang
传感器已广泛应用于现代制造系统中，以监测过程和机器健康状况，从而控制产品质量。大量传感器数据的处理对诊断效率提出了新的挑战。在本文中，提出了一种新的物理约束的字典学习方法，同时提高数据收集与压缩感知（CS）的效率，并进行诊断与传感器数据的分类。进行两阶段优化。在第一阶段，测量矩阵进行优化，以确定收集的数据点的时间戳与固定的基础矩阵。这是解决的基础上的约束FrameSense算法。在第二阶段，基和分类矩阵的优化与固定的测量矩阵的基础上的K-SVD算法。重复上述两个优化步骤，直到最佳测量、分类和基矩阵收敛而无需进一步改进。可以基于针对特定数据的学习的分类矩阵来更准确地对恢复的信号进行分类。所提出的机器故障诊断方法证明与声发射信号收集在熔丝制造过程中。
Sensors have been widely applied in modern manufacturing systems to monitor the processes and machine health conditions in order to control product quality. Processing a large amount of sensor data becomes a new challenge on the efficiency of diagnosis. In this paper, a novel physics-constrained dictionary learning approach is proposed to simultaneously improve the efficiency of data collection with compressed sensing (CS) and perform diagnosis with the classification of sensor data. Two-stage optimization is performed. At the first stage, measurement matrix is optimized to determine the time stamps of collected data points with a fixed basis matrix. This is solved based on a constrained FrameSense algorithm. At the second stage, the basis and classification matrices are optimized with the fixed measurement matrix based on the K-SVD algorithm. The above two optimization steps are repeated until the optimal measurement, classification, and basis matrices converge without further improvement. The recovered signals can be classified more accurately based on the learned classification matrix for specific data. The proposed approach for machine fault diagnosis is demonstrated with acoustic emission signals collected in the fused filament fabrication process.