Xen.AI IoT (IoT Sensor Data Analytics)
Xen.AI IoT is an Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning based customizable solution for Internet of Things (IoT) based Sensor Data Analytics.
While new sensor, mobile and wireless technologies are driving the evolution of the internet of things (IoT), the true business value of the IoT lies in big data analytics. Analytics on IoT data involves datasets generated by sensors, which are now both cheap and sophisticated enough to support a seemingly endless variety of use cases.The potential of sensors lies in their ability to gather data about the physical environment, which can then be analyzed or combined with other forms of data to detect patterns.
With an increase in the population and complexity of city infrastructures, cities seek methods to handle large-scale urbanization problems. IoT plays a vital role in collecting data from the city environment. IoT enables cities to use live status reports and smart monitoring systems to react more intelligently to emerging outages and natural calamities. By adopting IoT technologies in city, the majority of the city's assets can be connected to one another, making them more readily observable, and consequently, more easy to monitor and manage. The purpose of building smart cities is to improve services like traffic management, water management, and energy consumption, as well as improving the quality of life for the citizens.
The largest use case for industrial AI is “Predictive Maintenance”. Predictive Maintenance makes use of advanced analytics (e.g., Machine Learning) to determine the condition of a single asset or an entire set of assets (e.g., a factory). The goal: Predict when maintenance should be performed. Predictive maintenance usually combines various sensor readings, sometimes external data sources, and performs predictive analytics on thousands of logged events. Predicting the remaining useful life of an asset using supervised ML is the most common technique in Predictive maintenance.
While there are various ways to do AI-based quality inspection, automated optical inspection is by far the largest sub-category. Automated optical inspection is a technique where a camera autonomously scans the device under test for catastrophic failure (e.g. missing component) and/or quality defects (e.g. fillet size or shape or component skew). Computer vision is the foundation of optical inspection. Once the images are recognized, semi-supervised ML is the most effective technique to classify images into failure classes. The main benefit of this use case is cost reduction, and the main potential beneficiaries are large manufacturing facilities, where a small reduction in scrap or test time can yield very large savings.
One implementation of automated manufacturing process optimization is through Autonomous machines or robots. The idea behind those autonomous assets is that they replicate monotonous human tasks in the manufacturing process, thus saving costs. Before being put into production, the autonomous machines/robots perform the same task over and over again, learning each time until they achieve sufficient accuracy. The reinforcement learning technique is often used to train robots and autonomous machinery. Under this technique, a robot can relatively quickly teach itself to do a task under the supervision of a human. The “brains” of such a robot/machine are usually neural networks.