Semi-Supervised Random Forest Methodology for Fault Diagnosis in Air-Handling Units

From Modelica Models to Fault Diagnosis in Air Handling Units

This paper presents a methodology for model-based fault detection and diagnosis underpinned by modelica models and using a qualitative approach to diagnosis, which has been applied to diagnosis of an air handling unit based on data recorded by a building management system. The main steps from model development to component diagnosis are discussed and illustrated using a heating coil component.

Semi-supervised Random Forest for Intrusion Detection Network

In order to protect valuable computer systems, network data needs to be analyzed and classified so that possible network intrusions can be detected. Machine learning techniques have been used to classify network data. For supervised machine learning methods, they can achieve high accuracy at classifying network data as normal or malicious, but they require the availability of fully labeled data...

Combining Engineering and Qualitative Models to Fault Diagnosis in Air Handling Units

This paper presents a methodology for model-based fault localization and identification that exploits both numerical (Modelica) models and a qualitative model-based approach to diagnosis. It has been applied to diagnosis of an air handling unit based on data recorded by a building management system. The main steps from model development to diagnosis based on the recorded data are discussed.

Model-Based Diagnostics for Air Handling Units

Introduction In most large air-conditioned buildings, air-handling units account for a significant portion of total building energy consumption and have a major impact on comfort conditions and maintenance costs. The devices within an air-handling unit either use energy directly in the case of fans, or indirectly, in the case of heat exchangers, which impose loads on the chiller and boiler plan...

Integrating Air Handling Units in Office Buildings for High Performance