In today's rapidly evolving technological landscape, the seamless operation of various systems relies heavily on sensor data. However, as the volume and complexity of data grow, so does the risk of anomalies that can disrupt critical processes. This article explores the importance of anomaly detection in sensor data, highlighting its pivotal role in safeguarding efficiency, reliability, and safety across diverse industries.
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The Critical Importance and Growing Applications of Anomaly Detection
Anomaly detection is identifying patterns in data that deviate from expected behavior, often called faults or outliers. It has gained attention due to its relevance in real-world applications like fraud detection, intrusion detection, and system health monitoring.
Anomalies can have positive or negative implications, making it crucial for decision-makers to detect and take appropriate actions.
Various techniques have been developed, leveraging statistical distributions, distances, densities, clustering, and classification to pinpoint anomalies. The goal is to identify data patterns that deviate significantly from expected behavior, providing valuable insights into system functioning and detecting rare or challenging-to-predict abnormalities.
Timely anomaly detection prevents costly consequences, enhances cybersecurity, and optimizes resource usage. It enables proactive responses to fraud, system failures, health issues, and changing conditions, ensuring efficient and reliable operations.
Industrial maintenance is a prime example where curative fixes following obvious failures result in significant costs, including downtime, safety risks, and expenses. Implementing predictive maintenance through sensor monitoring enables the proactive resolution of unseen issues before they escalate.
Anomaly Detection with Sensor Data
Anomaly Detection in Massive Sensor Data Streams: A Petroleum Industry Case Study
The petroleum industry faces significant challenges in detecting anomalies in sensor data streams from offshore oil platforms. With billions of measurements generated daily, manual monitoring is infeasible, and the lack of labeled data prevents the use of supervised learning techniques.
A two-stage approach was proposed by a study published in Sensors to address this. It combines yet another segmentation algorithm (YASA), an efficient time series segmentation algorithm, with a one-class support vector machine (SVM) classifier.
YASA breaks sensor streams into homogeneous blocks, feeds into the SVM to model normal behavior and identifies deviations as potential anomalies.
Empirical testing on real turbomachine data showed that this approach outperformed existing alternatives and has been successfully deployed by a major petroleum industry conglomerate in Brazil.
Anomaly Detection in Power Grid Monitoring Sensor System
Monitoring modern power grids involves handling vast amounts of real-time sensor data from hundreds of thousands of interconnected sensors that record critical parameters like voltage and current multiple times per second.
Detecting anomalies in such massive real-time sensor data streams is critical for power grid operators to achieve timely situational awareness and quickly mitigate emerging failures or instability threats before propagating across the highly interconnected network. However, the scale and complexity of power grid data make manually detecting anomalies nearly impossible.
Researchers at the MIT-IBM Watson AI Lab developed an artificial intelligence approach to autonomously pinpoint anomalies in real-time from power grid sensor streams.
The model uses a special deep-learning approach called a normalizing flow and a Bayesian network to learn the complex relationships between different sensors and estimate anomalies accurately.
By estimating probability densities, the model can flag low-probability events as likely anomalies without needing labeled anomaly data for training.
This AI approach outperformed traditional techniques in identifying anomalies in grid monitoring datasets and can be applied to other sensor-based monitoring systems, such as traffic networks, without the need for domain expertise or labor-intensive rule-based systems.
Hemlock Semiconductor Operations
Hemlock semiconductor operations (HSC), the leading U.S. polysilicon manufacturer, employed TIBCO's process control, predictive maintenance, and anomaly detection solutions to enhance semiconductor manufacturing.
HSC uses near real-time alerting with predefined thresholds and machine learning insights to promptly identify and address process anomalies. TIBCO Spotfire automation services software generates alerts for parameters outside acceptable ranges (anomalies), enabling quick action to prevent defects.
According to HSO's program manager, the increased insights have enabled them to "optimally tune each process." As a result, HSC has achieved significant cost savings of $300,000 per month and gained better visibility and optimization of its manufacturing processes.
Anomaly Detection in Wearable Health Monitoring Devices
Wearable devices have revolutionized healthcare monitoring by continuously evaluating physiological parameters like heart rate, steps, and body temperature via multiple miniaturized sensors.
The massive amounts of multivariate time series data generated via miniaturized sensors present opportunities for personalized digital health and predictive analytics. However, detecting medically relevant anomalies in this high-velocity data requires accurate automated techniques.
Given the clinical significance of anomalies for diagnosis and treatment, researchers have proposed various methods for identifying anomalies indicative of emerging medical issues.
For example, combining sensor data with frequent medical measurements has enabled the prediction of Lyme disease onset and differentiation between insulin sensitivity levels. Machine learning approaches have also shown promise in detecting COVID-19 infections based on abnormal changes in resting heart rate and steps count.
As a result, detecting anomalies can drive timely interventions, improving health outcomes and reducing costs.
Uhlmann Pac Systeme's Pharma Packing Machine Case Study
Uhlmann Pac-Systeme specializes in manufacturing blister packaging machines for drugs, which use servomotors and sensors to monitor various parameters like power consumption, mechanical position, and motor load. Detecting anomalies in this data is vital for proactive maintenance and minimizing unplanned downtime.
A study published in Sensors used distance profiling on real-life sensor data from a Uhlmann Pac Systeme's pharmaceutical packaging machine. The goal was to detect anomalies in a machine component. The proposed approach successfully identified deviations by comparing the sensor data to a baseline profile using Mueen's algorithm for similarity search (MASS) algorithm, potentially indicating bearing wear.
The approach detected significant pattern deviations 13 hours before the breakdown, enabling timely technician intervention and preventing catastrophic failure. While a simple method, distance profiling provided actionable insights from sensor streams.
Anomaly detection in sensor data is crucial for optimizing operations, mitigating risks, and driving efficiency across industries. Real-time analysis of high-velocity information using advanced analytics provides a competitive advantage and empowers proactive decision-making.
As technology advances, harnessing the full potential of sensor data through robust anomaly detection will remain a fundamental pillar for enhancing efficiency and ensuring reliability in today's industrial landscape.
References and Further Reading
Dai, E., & Chen, J. (2022). Graph-augmented normalizing flows for anomaly detection of multiple time series. arXiv preprint arXiv:2202.07857. doi.org/10.48550/arXiv.2202.07857
Erhan, L., et al. (2021). Smart anomaly detection in sensor systems: A multi-perspective review. Information Fusion, 67, pp.64-79. doi.org/10.1016/j.inffus.2020.10.001
Kammerer, K., et al. (2019). Anomaly detections for manufacturing systems based on sensor data—insights into two challenging real-world production settings. Sensors, 19(24), p.5370. doi.org/10.3390/s19245370
Lakey, E. (2022). How Anomaly Detection Can Save Your Manufacturing Company Time and Money. [Online]. TIBCO. Available at: https://www.tibco.com/blog/2022/02/28/how-anomaly-detection-can-save-your-manufacturing-company-time-and-money/
Martí, L., et al. (2015). Anomaly detection based on sensor data in petroleum industry applications. Sensors, 15(2), pp.2774-2797. doi.org/10.3390/s150202774
Rabatel, J., et al. (2011). Anomaly detection in monitoring sensor data for preventive maintenance. Expert Systems with Applications, 38(6), pp.7003-7015. doi.org/10.1016/j.eswa.2010.12.014
Sunny, J. S., et al. (2022). Anomaly detection framework for wearables data: a perspective review on data concepts, data analysis algorithms and prospects. Sensors, 22(3), p.756. doi.org/10.3390/s22030756
Zewe, A. (2022). Using artificial intelligence to find anomalies hiding in massive datasets. [Online]. Available at: https://news.mit.edu/2022/artificial-intelligence-anomalies-data-0225