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Anomaly Detection Using Machine Learning In Industrial IoT

The document discusses the use of Industrial Internet of Things (IIoT) and machine learning for anomaly detection in hydroelectric power generators by analyzing various operational parameters. It details the methodology of building an artificial neural network model to detect anomalies by comparing reconstruction errors from normal and new operational sequences. The model can operate in both batch and real-time modes to assess and identify anomalous behaviors.

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Industrial IoT•
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Main Keywords Industrial IoT • Internet of things (IoT) is the network of physical devices embedded with sensors and connectivity which enables these objects to connect and exchange data. • Industrial IoT is IoT in the context of industrial processes such as manufacturing. Machine Learning • A program or system that builds (trains) a predictive model from input data. • The system uses the learned model to make useful predictions from new (never-before-seen) data drawn from the same distribution as the one used to train the model. • Supervised Learning and Unsupervised Learning Anomaly Detection • Anomalies are unusual patterns that do not conform to the expected behaviour of a system • Anomaly Detection is a technique used to identify anomalies
Use Case - Introduction • Detect anomalous start-up sequence of a hydro electric power generator. • Types of Anomalies • Value • Behavioural • Parameters • Power generated • Generator RPM • Generator Cooling Water Temperature • Ambient Temperature • Various bearing Temperatures
Normal vs. Anomaly
Solution Overview • Artificial Neural Network model is trained on normal generator startup sequence (best fit) • This model is then applied to new generator sequence to detect anomalous behavior • Anomaly is detected by reviewing the reconstruction error (predicted vs actual value) and applying thresholds derived during model training NEURAL NET (MLP) PREDICT INPUT DATA STREAM (Generator Startup Sequence) TRAINING DATA SET (Good Startup Sequence) ANOMALY Minimize Loss Function ERROR > α α = Error Threshold
Model Building - Methodology Input Raw Data (7 Parameters) + Engineered Features (Features built on 8 parameters) Output Prediction of Outcome parameter Calculate Root Mean Square Error (RMSE) for the model Compare the RMSE(model) with RMSE(new sequence) to detect anomalies
Model Performance - Actual vs. Predicted Training Normal Sequences RMSE = 0.01317 Testing Anomalous Sequences RMSE = 0.302729
Anomaly Detection - Modes Batch Mode • Complete start-up sequence is passed as input. • Compare RMSE(model) to RMSE(sequence) Scenario RMSE Normal 0.012556 Anomalous 0.302729 RMSE of Model = 0.01317
Anomaly Detection - Modes Real Time Mode • Pass streaming data from the sensor as input. • Anomaly if RMSE is increasing over time.
Summary • To identify anomalies, identify the normal. • Create features that describes anomalies. • Simulate the normal behaviour with a machine learning model. • Compare the predictions with actual. • Deploy in batch mode or real time mode.
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