The industrial internet of things allows manufacturers to acquire large amounts of data. This opportunity, assuming the right methods are available, allows manufacturers to find anomalies that arise during manufacturing system operation. Data acquired from a manufacturing system are usually in the forms of time series. This paper proposes a new method that can detect anomalies in time series data. This model is based on recurrent neural networks, and it can be trained using data acquired during routine system operation. This is very beneficial because often, there are few data labeled as anomalies, since anomalies are hopefully rare events in a well-managed manufacturing system. The model takes time series data as an input and reconstructs the input data. Time series data with an anomaly would causes patterns in the reconstruction errors that are inconsistent with error patterns of anomaly-free data. The performance of the proposed method is assessed using data from a diesel engine assembly process. Three common types of anomalies are detected from the time series data. It is shown that the method not only can detect anomalies, but it can also provide insights into the timestep at which the anomaly occurred. This feature helps a manufacturer pinpoint the source of the problem.

工业物联网使制造商可以获取大量数据。假设可以使用正确的方法，那么这个机会可以使制造商发现在制造系统运行期间出现的异常情况。从制造系统获取的数据通常采用时间序列的形式。本文提出了一种可以检测时间序列数据异常的新方法。该模型基于递归神经网络，可以使用在常规系统运行期间获取的数据进行训练。这是非常有益的，因为通常很少有被标记为异常的数据，因为在管理良好的制造系统中，异常可能是罕见的事件。该模型将时间序列数据作为输入，并重建输入数据。具有异常的时间序列数据会导致重建错误的模式与无异常数据的错误模式不一致。使用来自柴油发动机组装过程的数据评估了所提出方法的性能。从时间序列数据中检测出三种常见的异常类型。结果表明，该方法不仅可以检测异常，而且还可以洞悉异常发生的时间步长。此功能可帮助制造商查明问题的根源。但是它也可以提供异常发生时间的见解。此功能可帮助制造商查明问题的根源。但是它也可以提供异常发生时间的见解。此功能可帮助制造商查明问题的根源。