Anomaly detection and advanced sensor analytics for leading HVAC manufacturer
About the customer and the challenge
The series of projects have been conducted with one of the leading HVAC (Heating, ventilation, and air conditioning) manufacturers globally.
The vast majority of machines produced nowadays, including also air conditioning or heating systems, have integrated modern IoT (Internet-of-Things) sensors, generating massive amounts of sensor readings, describing the current state of the machines.
To take advantage of the data it’s crucial to analyze and find meaningful information in them, to both cut the costs connected with maintenance work and optimize the device usage, as well as build new innovative products and scenarios offering added value to the users of the devices, often without the need to buy additional physical devices.
Together with our customer, we have conducted multiple advanced analytics and AI projects focusing on different use cases, such as:
- Determination of the most endurable and reliable sensors from different alternatives during field trials
- Building effective data architecture and data processing pipelines for 100+ devices with 50+ sensors each, running on AWS cloud (based on technologies such as Apache Airflow, Athena, MS Azure IoT Hub, AWS Glue)
- Classification of the operational modes of the devices using multivariate analysis
- Anomaly detection to detect malfunctioning of the single sensors or whole device, based on the abnormalities from the normal value patterns
- Estimation of the impact of the operating modes of the machine on its environment, including automatic air quality optimization based on indoor and outdoor conditions.
All projects focus on applying effective data engineering techniques in the cloud, as well as applying machine learning methods on time series data, including both the classical machine learning methods as well as building deep neural networks.
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