Evaluate the impact of sensor accuracy on model performance in data-driven building fault detection and diagnostics using Monte Carlo simulation

The performance of data-driven fault detection and diagnostics(FDD)is heavily dependent on sensors.However,sensor inaccuracy and sensor faults are pervasive in building operation:inaccurate and missing sensor readings deteriorate FDD performance;sensor inaccuracy will also affect the selection of sensor for data-driven FDD in the model training process,which is another key factor of data-driven FDD performance.Sensor accuracy and sensor selection individually are well-studied research topics in this field,but the impact of sensor accuracy on sensor selection and its further impact on FDD performance has not been evaluated and quantified.In this paper,we developed a novel analysis methodology that comprehensively evaluates sensor fault on sensor selection and FDD accuracy.Monte Carlo simulation is applied to deal with multiple stochastic sensor inaccuracy and provide probabilistic analysis results of the impact of sensor inaccuracy on sensor selection and FDD accuracy.This methodology focuses on the net impact of fault states across a full sensor set.The developed methodology can be used for the early-stage sensor design and operation-stage sensor maintenance.A case study is conducted to demonstrate the analysis methodology using a commercial building model crated to Flexible Research Platform located at Oak Ridge National Laboratory,USA.

Semi-Supervised Machine Learning for Fault Detection and Diagnosis of a Rooftop Unit

Most heating,ventilation,and air-conditioning(HVAC)systems operate with one or more faults that result in increased energy consumption and that could lead to system failure over time.Today,most building owners are performing reactive maintenance only and may be less concerned or less able to assess the health of the system until catastrophic failure occurs.This is mainly because the building owners do not previously have good tools to detect and diagnose these faults,determine their impact,and act on findings.Commercially available fault detection and diagnostics(FDD)tools have been developed to address this issue and have the potential to reduce equipment downtime,energy costs,maintenance costs,and improve occupant comfort and system reliability.However,many of these tools require an in-depth knowledge of system behavior and thermodynamic principles to interpret the results.In this paper,supervised and semi-supervised machine learning(ML)approaches are applied to datasets collected from an operating system in the field to develop new FDD methods and to help building owners see the value proposition of performing proactive maintenance.The study data was collected from one packaged rooftop unit(RTU)HVAC system running under normal operating conditions at an industrial facility in Connecticut.This paper compares three different approaches for fault classification for a real-time operating RTU using semi-supervised learning,achieving accuracies as high as 95.7%using few-shot learning.