Detecting crossovers in polymer fiber using machine learning

仪器有限公司的核心技术平台是基于测量与传感器接触的材料的电性能。传感器产生一个紧密受限的电磁场，当材料进入这个电磁场时，传感器可以检测材料的性质。该传感器对材料的量、形状、密度、温度、电导率、杂质、水分含量和流速都很敏感。传感器实时采集数据，并根据大量测试确定的参数提供反馈。这种传感器被用于多个行业，如航空航天和工业制造。其中两个比较成功的应用是冰探测和聚合物纤维制造。Instrumar从其在全球的安装中接收tb级的数据。将这些未经处理的大量数据转化为有价值的信息是他们的目标之一。开发过程将使用机器学习方法来识别和确定最重要的贡献者，以可靠和准确地确定结果。我们想量化数据集中原始特征和计算生成特征的贡献（重要性）。此外，我们希望能够区分签名，短期和长期趋势，如机器谐波。一旦算法被成功编码，模型被生成，识别模型的性能是很重要的。使用机器学习和额外的数据意味着我们可以开发更好的算法和简化的模型。能够描述开发的算法，使Instrumar能够为聚合物纤维生产商和其他运营商提供更好的反馈。这有助于制造商达到可持续性和环境标准。纤维工业是地球上最严重的污染者之一。每提高一次效率都会对环境产生巨大影响。

Instrumar Limited's core technology platform is based on the measurement of electrical properties of materials that come in contact with its sensor. The sensor generates a tightly confined electromagnetic field that detects the properties as material enters this field. The sensor is sensitive to the amount, the shape, its density, temperature, conductivity, impurities, moisture content and flow speed of the material. The sensors sample data in real time and provide feedback based on parameters that are determined through extensive testing. This sensor is being used in multiple industries such as aerospace and industrial manufacturing. Two of their more successful applications are in the ice detection and polymer fiber manufacturing. Instrumar receives terabytes of data from its installations worldwide. It is one of their goals to turn this unprocessed high volume data into value-added information.The development process will be to use machine learning methods to identify and determine the top contributors to reliably and accurately determine the outcomes. We want to quantify the contribution (importance) of the original and computationally generated features in the dataset. Also, we want to be able to segregate between signatures, short term and long term trends such as machine harmonics. Once algorithms are successfully coded and models are generated it is important to identify the performance of the models. Using machine learning with extra data means we can develop better algorithms and simplified models. Being able to characterize developed algorithms allows Instrumar to provide better feedback to polymer fiber producers and other operators. This helps the manufacturers meet sustainability and environmental standards. The fiber industry is one of the planets worst polluters. Every efficiency gained has a huge environmental impact.