Abstract Fault diagnosis is crucial for the printing quality assurance of a 3D printer. This paper presents a pre-classified reservoir computing (PCRC) method to diagnose the health condition of a 3D printer using the data collected by a low-cost attitude sensor. As the data from the low-cost attitude sensor often contain a large amount of interference information, it is difficult to accurately diagnose the printer condition. As such, a pre-classification strategy is proposed to reduce the intra-class distance by aggregating information labels of the same condition. Echo state network is then employed as a reservoir computing (RC) tool for applying data-driven based artificial intelligence to extract faulty features and to classify condition patterns simultaneously. The proposed PCRC method is evaluated using experimental data collected from the 3D printer. An optimal PCRC model is developed by tuning model parameters using experimental data. The advantages of the PCRC are demonstrated by comparing with other methods such as RC, random forest, support vector machine and spare auto-encoder. Due to the combined and compounded effect of the pre-classification strategy and RC modelling, the proposed method leads to the highest accuracy in fault diagnosis of the 3D printer with limited low-cost sensor data and relatively small datasets, without relying on physical domain knowledge.

中文翻译：

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用于3D打印机故障诊断的预分类储层计算

摘要 故障诊断对于3D打印机的打印质量保证至关重要。本文提出了一种使用低成本姿态传感器收集的数据诊断 3D 打印机健康状况的预分类储层计算 (PCRC) 方法。由于来自低成本姿态传感器的数据往往包含大量干扰信息，因此很难准确诊断打印机状况。因此，提出了一种预分类策略，通过聚合相同条件的信息标签来减少类内距离。然后将回波状态网络用作储层计算 (RC) 工具，以应用基于数据驱动的人工智能来提取故障特征并同时对条件模式进行分类。所提出的 PCRC 方法是使用从 3D 打印机收集的实验数据进行评估的。通过使用实验数据调整模型参数来开发最佳 PCRC 模型。PCRC 的优势通过与其他方法如 RC、随机森林、支持向量机和备用自动编码器的比较来证明。由于预分类策略和 RC 建模的组合和复合效应，所提出的方法在不依赖物理领域知识的情况下，以有限的低成本传感器数据和相对较小的数据集对 3D 打印机进行故障诊断的准确性最高. 支持向量机和备用自动编码器。由于预分类策略和 RC 建模的组合和复合效应，所提出的方法在不依赖物理领域知识的情况下，以有限的低成本传感器数据和相对较小的数据集对 3D 打印机的故障诊断具有最高的准确性. 支持向量机和备用自动编码器。由于预分类策略和 RC 建模的组合和复合效应，所提出的方法在不依赖物理领域知识的情况下，以有限的低成本传感器数据和相对较小的数据集对 3D 打印机的故障诊断具有最高的准确性.