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Real-Time Manufacturing Drilling Operations Analysis by Utilization of Data-Fusion

机译：利用数据融合进行实时制造钻井作业分析

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In mining and construction operations, the protection, safety and machinery's lifetime hold a crucial concern that can impose unwelcoming costs on the projects. The motivation behind this work is to deliver a model capable of addressing these apprehensions besides managing the potential risks and costs of the types of machinery. The presented model in this article aims to increase the quality and reliability of the products and their operations by utilizing sensor information for real-time prediction and categorization of drilling operations. This model works based on the time analyses on the sensory fused data. We applied the model on the three-axis acceleration and angular velocity signals (generated from a simulated system) to extract features and categorize three different rock drilling operations. For each operation, we measured the Median Absolute Deviation (MAD) and dynamic range parameters of the acceleration signals. In addition, we succeeded to calculate the Root Mean Square (RMS) parameter as a feature from angular velocity signals. The obtained results in this study approve the real-time prediction and categorization potential of the introduced approach for the different rock drilling operations. However, the limitation of this work can be the source of the data which is originating from the simulated normal operations. As an extending future work in future publications, we will include the faulty operation data, the real data from measurements and present data analysis of abnormal operations.
机译：在采矿和建筑运营中，保护，安全和机械的使用寿命至关重要，这可能会给项目带来不受欢迎的成本。这项工作的动机是提供一种模型，该模型除了管理机械类型的潜在风险和成本外，还能够解决这些担忧。本文中提出的模型旨在通过利用传感器信息进行钻井作业的实时预测和分类来提高产品及其作业的质量和可靠性。该模型基于对感官融合数据的时间分析而工作。我们将模型应用于三轴加速度和角速度信号（从模拟系统生成），以提取特征并对三种不同的凿岩作业进行分类。对于每个操作，我们测量了加速度信号的中位数绝对偏差（MAD）和动态范围参数。此外，我们成功地根据角速度信号计算了均方根（RMS）参数。在这项研究中获得的结果批准了针对不同凿岩作业的引入方法的实时预测和分类潜力。但是，这项工作的局限性可能是源自模拟正常操作的数据源。作为将来出版物中扩展的未来工作，我们将包括错误的操作数据，测量的实际数据以及对异常操作的当前数据分析。

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