IoT Control Tower

This blog post discusses the challenges of IoT data analysis for system health monitoring and provides a reference pipeline for anomaly detection using machine learning techniques. The pipeline includes training regression models, computing anomaly scores, and making binary decisions to detect anomalies in IoT data.

Grid Dynamics has developed a starter kit for building an IoT platform from scratch in Google Cloud Platform (GCP), specifically tailored for smart manufacturing enterprises. The kit includes modular components for data collection, deployment to the edge, IoT device management, and more, reducing the time-to-market for developing an IoT platform.

The global market for the Internet of Things (IoT) is expected to reach $413.7 billion by 2031, with key industries driving this growth including manufacturing, supply chain and logistics, energy, and smart cities. Building an IoT platform can be challenging due to the complexity of integrating data collection, IoT device management, and machine learning platforms, but AWS offers a solution with their IoT Platform Starter Kit.

Learn more about the IoT Platform Starter Kit for Microsoft Azure, which provides best-in-class cloud-native services for IoT and a reference implementation to accelerate the delivery of applied IoT projects.

This article discusses the importance of anomaly detection in technical systems and outlines a solution design methodology based on the types and availability of labeled data. It highlights the pitfalls of using unsupervised methods and recommends the use of one-class learning approaches, even in situations where two-class labeling is available or no labeled data is present.

This article discusses a methodology for designing machine learning-based health monitoring systems for complex industrial systems. It emphasizes the use of hierarchical decomposition and one-class learning to address challenges such as high dimensionality, high data rates, and qualitative and quantitative inhomogeneity of sensor readings.

AWS has outlined how equipment operators can build a predictive maintenance solution using AutoML and no-code tools provided by the company. The solution uses machine learning techniques to estimate the remaining useful life (RUL) of machines or equipment, allowing operators to optimise maintenance schedules and balance resource usage and failure risks. The solution can be implemented using AWS Canvas or AWS AutoPilot, depending on the specific requirements of the application.