Issue link: https://resources.mouser.com/i/1437726
17 INTEL 2021 Background In the manufacturing industry, particularly in the steel industry, there are two ways to avoid producing unqualified products caused by device failure. One is to maintain equipment regularly; the other is to replace the equipment component before they fail. Both approaches could be unnecessarily expensive. However, it is possible to collect a massive amount of vibration data of different devices, and automatically detect anomalies of the device statuses using these data. Efficient time-series data retrieval and automatic failure detection of the devices at scale is the key to avoiding unnecessary cost. LSTM-Based Time Series Anomaly Detection Using Analytics Zoo for Apache Spark TM and BigDL at Baosight Case study Jinquan Dai and Guoqiong Song This article shares the experience and lessons learned from Baosight and the Intel team in building an unsupervised time series anomaly detection project, using long short-term memory (LSTM) models on Analytics Zoo. Recurrent neural networks (RNNs), especially LSTMs, are widely used in signal-processing time-series analysis. As connectionist models, recurrent neural networks (RNNs) capture the dynamics of sequences via cycles in the network of nodes. In this project, we adopt the approaches of LSTMs to simulate statistics of vibration signals; in the following section, we use Cincinnati University's Center for Intelligent Maintenance Systems (IMS) lifecycle data (download) to showcase the analytics pipeline.