Industrial processes are monitored by a large number of various sensors that produce time-series data. Deep Learning offers a possibility to create anomaly detection methods that can aid in preventing malfunctions and increasing efficiency. But creating such a solution can be a complicated task, with factors such as inference speed, amount of available data, number of sensors, and many more, influencing the feasibility of such implementation. We introduce the DeTAVIZ interface, which is a web browser based visualization tool for quick exploration and assessment of feasibility of DL based anomaly detection in a given problem. Provided with a pool of pretrained models and simulation results, DeTAVIZ allows the user to easily and quickly iterate through multiple post processing options and compare different models, and allows for manual optimisation towards a chosen metric.
翻译:深入学习为创建异常现象检测方法提供了可能性,有助于防止故障和提高效率。但是创建这样的解决方案可能是一项复杂的任务,其因素包括推断速度、可用数据数量、传感器数量等,以及影响实施可行性的更多因素。我们引入了DETAVIZ接口,这是一个基于网络浏览器的可视化工具,用于快速探索和评估基于DL的异常现象在特定问题中检测的可行性。DTAVIZ提供了一批预先培训的模型和模拟结果,它允许用户通过多个后处理选项和比较不同的模型而容易和迅速地循环,并允许人工优化到选定的指标。