Issue link: https://resources.mouser.com/i/1437726
18 REIMAGINING WHAT'S NEXT Analytics Zoo Solution Analytics Zoo is an analytics + artificial intelligence platform (based on Apache Spark TM , BigDL, etc.) open- sourced by Intel, which makes it easy to build end-to- end deep-learning applications for big data that can run directly on standard Apache Hadoop/Spark clusters based on Intel ® Xeon ® processors (no graphics processing units needed). We have built the end-to-end LSTM-based anomaly detection pipeline on Apache Spark and Analytics-Zoo, which applies unsupervised learning on a large set of time series data. A sequence of vibrational signals (signals that last 50 seconds) leading to the current time are used as input to the LSTM model, which then tries to predict the next data point. When the next data point is distant from the model's predictions, we consider it an anomaly. The entire end-to-end pipeline is illustrated in Figure 1. 1. It first reads raw data in Apache Spark as resilient distributed datasets (RDD), then extracts the features, and finally outputs features into dataframe. In the raw datasets, each data set describes a test- to-failure experiment and consists of individual files that are 1-second vibration signal snapshots recorded at 20kHz (Figure 2). To train and test our models, we extracted statistics of each second as features, including root mean square (RMS), kurtosis, peak, and energy values of eight bands obtained by wavelet packet for three layers. 2. It further processes the features in RDD, including wavelet domain denoising, normalizing values using a standard scaler, unrolling the feature sequence with a length of 50 (so that the model can learn the pattern from previous 50 seconds to predict next point), and transforming data into RDD of Samples at the end. 3. It then uses the Keras-style API in Analytics Zoo to build a time series anomaly detection model (which consists of three LSTM layers followed by a dense layer, as shown below) and trains the model (which learns from 50 previous values to predict the next one). val model = Sequential[Float]() model.add(LSTM[Float](8, returnSequences = true, inputShape = inputShape)) model.add(Dropout[Float](0.2)) model.add(LSTM[Float](32, returnSequences = true)) model.add(Dropout[Float](0.2)) model.add(LSTM[Float](15, returnSequences = false)) model.add(Dropout[Float](0.2)) model.add(Dense[Float](outputDim = 1)) Learn More ADVANTECH VEGA-320 M.2 EDGE AI MODULE Learn More INTEL® PROGRAMMABLE ACCELERATION CARD WITH ARRIA® 10 GX Figure 1: Anomaly detection pipeline of vibration time serials based on Analytics Zoo and Apache Spark.