Results for “Fault Detection and Diagnostics”

DOE OSTI2020

Curation of Ground-Truth Validated Benchmarking Datasets for Fault Detection & Diagnostics Tools

Fault detection and diagnostics (FDD) analytical tools for heating, ventilation and air conditioning (HVAC) systems represent one of the most active areas of smart building technology development. A diversity of techniques is used for FDD analytics, spanning physical models, black box, and rule-based approaches, and researchers continuously strive to develop improved algorithms. With FDD algorithm numbers now in the hundreds, there is a need for performance evaluation of these algorithms in order to assess improvements, improve costeffectiveness, and to prioritize investment in the further development of these technologies. A persistent challenge of FDD advance has been the lack of common datasets to benchmark the performance accuracy of FDD algorithms. This paper summarizes the successful curation of HVAC operational data, paired with validated ground-truth information regarding the presence and absence of faults. The current dataset, consisting of both simulation and experimental data, will evolve to include a larger set of HVAC systems with the objective of creating the largest publicly available dataset to be used by FDD developers, users, and researchers to compare and contrast performance accuracy across FDD algorithms, helping to drive improvements that will spur greater market adoption of FDD tools. Furthermore, in order to avoid previously observed issues with contributed datasets and ensure high quality and consistency of future submissions, the development of data validation and ground-truth assessment protocol is detailed in this study.

Casillas, Armando↗

DOE OSTI2020

Automatic Fault Detection & Diagnostics: Residential Market Analysis

This report provides an overview of the market potential for automated fault detection and diagnostics (AFDD) in the residential sector, focusing on embedded AFDD in central air conditioners (CAC) and air-source heat pumps (ASHP) as well as the use of smart diagnostic tools to ensure quality installation of CAC/ASHP equipment. The contents include background on AFDD, stakeholder engagement efforts, a technology assessment of AFDD and smart diagnostic tools, residential CAC and ASHP market trends, a characterization of the CAC and ASHP installed base, a synopsis of utility provider programs, and market barriers.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

Dataset of low global warming potential refrigerant refrigeration system for fault detection and diagnostics

Abstract HVAC and refrigeration system fault detection and diagnostics (FDD) has attracted extensive studies for decades; however, FDD of supermarket refrigeration systems has not gained significant attention. Supermarkets consume around 50 kWh/ft 2 of electricity annually. The biggest consumer of energy in a supermarket is its refrigeration system, which accounts for 40%–60% of its total electricity usage and is equivalent to about 2%–3% of the total energy consumed by commercial buildings in the United States. Also, the supermarket refrigeration system is one of the biggest consumers of refrigerants. Reducing refrigerant usage or using environmentally friendly alternatives can result in significant climate benefits. A challenge is the lack of publicly available data sets to benchmark the system performance and record the faulted performance. This paper identifies common faults of supermarket refrigeration systems and conducts an experimental study to collect the faulted performance data and analyze these faults. This work provides a foundation for future research on the development of FDD methods and field automated FDD implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

A systematic feature extraction and selection framework for data-driven whole-building automated fault detection and diagnostics in commercial buildings

In data-driven automated fault detection and diagnostics (AFDD) modeling for building energy systems, feature engineering is a critical process of extracting information from high-dimensional and noisy sensor measurement and turning it into informative and representative inputs or features for data-driven modeling. However, few studies specifically discuss the feature engineering, especially the interactions between feature extraction and feature selection in whole-building AFDD. We developed a systematic feature extraction and selection framework for whole-building AFDD. In this framework, features are aggressively extracted from raw sensor data using statistical feature extraction techniques with various window sizes and statistics. With many features extracted, a hybrid feature selection algorithm that combines the filter and wrapper method then selects the best feature set. The framework considers diversity in the duration of fault behavior among fault types in whole-building AFDD, thus achieving high model generalization. We implemented our developed framework in a virtual testbed calibrated with measured data from Oak Ridge National Laboratory's Flexible Research Platform designed to mimic the operation of a typical small commercial building. The AFDD model is trained by the simulation data generated from the virtual testbed. The results show that (1) the developed framework improves the generalization of the AFDD model by 10.7% compared with literature-reported feature extraction and selection methods and (2) features with diverse window sizes and statistics are selected, providing insight into physical systems beyond the current understanding of buildings and faults and improving the detection and diagnostics of multiple fault types.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Sensor cost-effectiveness analysis for data-driven fault detection and diagnostics in commercial buildings

Data-driven building fault detection and diagnostics (FDD) is heavily dependent on sensors. However, common sensors from Building Automation Systems are not optimized to maximize accuracy in FDD. Installing additional sensors that provide more detailed building system information is key to maximizing the performance of FDD solutions. Here in this paper, we present a sensor cost analysis workflow to quantify the economic implications of installing new sensors for FDD using the concept of sensor threshold marginal cost (STMC). STMC does not represent actual sensor cost. Rather, it represents a target cost based on the economic benefit that would be realized through improved FDD performance and one or more specified economic criteria. We calculate STMCs for multiple possible fault types and use fault prevalence information to aggregate STMCs into a single dollar value to determine the cost-effectiveness of a potential sensor investment. We conducted a case study using Oak Ridge National Laboratory's Flexible Research Platform (FRP) test facility as a reference. The case study demonstrates the feasibility of the analysis and highlights the key cost considerations in sensor selection for FDD. The results also indicate that identifying and installing the few key sensor(s) is critical to cost-effectively improve FDD performance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

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. Furthermore, 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.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

LBNL Fault Detection and Diagnostics Datasets

These datasets can be used to evaluate and benchmark the performance accuracy of Fault Detection and Diagnostics (FDD) algorithms or tools. It contains operational data from simulation, laboratory experiments, and field measurements from real buildings for seven HVAC systems/equipment (rooftop unit, single-duct air handler unit, dual-duct air handler unit, variable air volume box, fan coil unit, chiller plant, and boiler plant). Each dataset includes a .pdf file to document key information necessary to understand the content and scope, multiple csv files containing all the time-series data for faults at different severity levels and one fault-free case, and a ttl file to visualize the data according to BRICK schema. The dataset was created by LBNL, PNNL, NREL, ORNL and Drexel University.

AC↗

DOE OSTI2021

Sensor impact evaluation and verification for fault detection and diagnostics in building energy systems: A review

Sensors are the key information source for fault detection and diagnostics (FDD) in buildings. However, sensors are often not properly designed, installed, calibrated, located, and maintained, which negatively impacts FDD performance. Several sensor-related FDD topics have been widely studied, covering a wide range of fault types and applications. However, it is difficult to get a clear picture of the technical development of sensor-related topics in FDD. A systematic review of sensor topics is needed to summarize the existing research in a logical way, draw conclusions on the current development, and predict the future development of sensors in building FDD. To address this gap, we conducted a comprehensive literature review of more than 100 FDD-sensor-related papers. In this article, we subdivide the FDD tasks into building-level, system-level, and component-level FDD, and review sensor-related topics in each category. Our major conclusions are: (a) current data-driven FDD research focuses more on FDD algorithms than sensors, (b) sensor “hardware” research topics are less studied than sensor “software” topics, (c) very few papers focus on sensor engineering as an integral aspect of FDD development, and (d) some important sensor topics, such as sensor cost-effectiveness and sensor schema/layout/location, are not well studied. Finally, we discuss the need for a systematic framework of FDD sensors and models to integrate sensor design/selection, sensor data analysis/mining, feature selection, physics-based or data-driven algorithm development, sensor fault detection, sensor calibration, and sensor maintenance. Finally, expert interviews are conducted to validate the above findings and conclusions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Bringing Fault Detection and Diagnostics (FDD) Tools into the Mainstream: Retro Commissioning and Continuous Commissioning of HVAC and Refrigeration Systems

HVAC systems in the commercial building sector consume about 3.4 quads of energy annually. Of all the HVAC systems, packaged rooftop air–conditioning units (RTUs) provide cooling and heating for over 60 percent of the commercial building floorspace (about 90 billion ft2) in the U.S. and they are a significant source of energy consumption and peak demand. All HVAC systems suffer from faults that impact thermal comfort and increase energy consumption. There are several commercially available Automated Fault Detection and Diagnostic (AFDD) tools on the market that can detect and diagnose faults, and if those faults are corrected, can save significant national energy. However, there are multiple market barriers for these tools including lack of independent verification of their performance in the field in terms of their technical capabilities, ease of use and installation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Compressor is A Sensor: A Universal Refrigerant Charge Fault Detection and Diagnostics Method Based on Pump Down Operation

The primary goal of this report is to develop a universal charge fault detection method that requires only a few experimental data with high prediction accuracy. Currently, pump-down operations is typical practices by HVAC technicians when they need to open the refrigerant circuit to make a repair. In addition, compressors have a built-in low-pressure cut-off protection function, and the compressor performance maps are commonly available from manufacturers. The new charge fault detection and diagnostics method innovatively utilizes the typical pump down operation, the compressor’s low-pressure cut-off protection, and the compressor performance map. It does not require any geometry information of heat exchangers, refrigerant lines, or charge buffers.

42 ENGINEERING↗

DOE OSTI2022

Market Barriers and Drivers for the Next Generation Fault Detection and Diagnostic Tools

Commercial buildings in the U.S. consume as much as 30% excess energy compared to buildings that operate fault free and efficiently. Fault detection and diagnostic (FDD) platforms help to continually identify operational inefficiencies and maintain low-carbon performance. However, the recommendations generated by FDD tools need to be implemented by technicians, resulting in delays or lost savings opportunities. Recent research advances showed fault AUTOcorrection integrating with commercial FDD offerings filled this gap. Seven innovative AUTOcorrection algorithms were integrated into two FDD platforms and deployed across four buildings. The enhanced tools successfully correct faults focusing on incorrectly programmed schedules, override not released, control hunting, rogue zone, and suboptimal setpoints. Although its technical efficacy has been proven in the field, fault AUTO-correction is still early in the deployment cycle and opportunities and barriers need to be understood to reach its full potential in market transformation. This paper broadly introduces the new technology that automatically corrects HVAC faults. The authors describe in detail technology potential, market barriers, and enablers for scalability based on field testing results and interviews with the FDD providers and facility managers. The interviewees agreed that AUTO-correction can reduce the extent to which savings are dependent upon human intervention, scale building operators’ ability to act on FDD findings (especially for facilities with small operation teams), and achieve significant savings. To enable scalable deployment, future efforts are needed to overcome the barriers such as cybersecurity and accountability concerns from building operators and standardization of control parameters used in building automation systems.

Pritoni, Marco↗

DOE OSTI2020

Analysis of Fault Data Collected from Automated Fault Detection and Diagnostic Products for Packaged Rooftop Units

Rooftop units (RTUs) and other packaged systems are very common in commercial buildings in the U.S., and they often have minimal controls and poor performance. Automated fault detection and diagnostics (AFDD) is a powerful tool that can continuously monitor operating equipment, detect abnormal performance, diagnose problems, and report findings to building operators. AFDD technologies for RTUs have been under development for many years and have recently begun to enter the market in a significant way. There are several AFDD systems available for RTUs that feature a wide range of designs, capabilities, and reporting. Unfortunately, there is little consistency among the AFDD applications and little understanding of the performance and value of these systems. This study presents analysis of AFDD data provided by four companies from over 28,000 RTUs, five building types, and multiple climate zones. The objectives of this investigation were to gain a better understanding of how RTU AFDD systems operate, the types and frequencies of faults identified, and how building operators interact with these systems. The monitoring of a variety of RTUs provides insights into the AFDD monitoring inputs, faults, and diagnostics from which these tools are capable of informing building owners about the status of their HVAC systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Analysis of Fault Data Collected from Automated Fault Detection and Diagnostic Products for Packaged Rooftop Units

Rooftop units (RTUs) and other packaged systems are very common in commercial buildings in the U.S., and they often have minimal controls and poor performance. Automated fault detection and diagnostics (AFDD) is a powerful tool that can continuously monitor operating equipment, detect abnormal performance, diagnose problems, and report findings to building operators. AFDD technologies for RTUs have been under development for many years and have recently begun to enter the market in a significant way. There are several AFDD systems available for RTUs that feature a wide range of designs, capabilities, and reporting. Unfortunately, there is little consistency among the AFDD applications and little understanding of the performance and value of these systems. This study presents analysis of AFDD data provided by four companies from over 28,000 RTUs, five building types, and multiple climate zones. The objectives of this investigation were to gain a better understanding of how RTU AFDD systems operate, the types and frequencies of faults identified, and how building operators interact with these systems. The monitoring of a variety of RTUs provides insights into the AFDD monitoring inputs, faults, and diagnostics from which these tools are capable of informing building owners about the status of their HVAC systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Implementation and test of an automated control hunting fault correction algorithm in a fault detection and diagnostics tool

Control hunting due to improper proportional–integral–derivative (PID) parameters in the building automation system (BAS) is one of the most common faults identified in commercial buildings. It can cause suboptimal performance and early failure of heating, ventilation, and air conditioning (HVAC) equipment. Commercial fault detection and diagnostics (FDD) software represents one of the fastest growing market segments in smart building technologies in the United States. Implementation of PID retuning procedures as an auto-correction algorithm and integration into FDD software has the potential to mitigate control hunting across a heterogeneous portfolio of buildings with different BAS in a scalable way. This paper presents the development, implementation, and field testing of an automated control hunting fault correction algorithm based on lambda tuning open-loop rules. The algorithm was developed in a commercial FDD software and successfully tested among nine variable air volume boxes in an office building in the United States. The paper shows the feasibility of using FDD tools to automatically correct control hunting faults, discusses scalability considerations, and proposes a path forward for the HVAC industry and academia to further improve this technology.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

A Universal Refrigerant Charge Fault Detection and Diagnostics Method Based on Pump Down Operation

The performance of the heat pump system varies greatly depending on the refrigerant charge amount. Improving the refrigerant charge fault detection and diagnostics (FDD) method of vapor compression systems have the potential for increasing energy efficiency and reducing service cost. Previous studies to predict refrigerant charge amount are mostly empirical methods which require significant amount of experimental data for high accuracy. The primary goal of this research is to develop a universal charge fault detection method which requires only a few experimental data with high prediction accuracy.Currently, pump down operations are typical practices by HVAC technicians when they need to open the refrigerant circuit to make a repairment. In addition, compressors have a built-in low-pressure cut-off protection function, and the compressor performance maps are commonly available from manufacturers. The proposed method innovatively utilizes the typical pump down operation, the compressor low-pressure cut-off protection, and the compressor performance map. It does not require any geometry information of heat exchangers, refrigerant lines, or charge buffers.The new charge prediction method is firstly formulated through theoretical analysis, then verified and calibrated by a quasi-steady-state simulation of the pump down process for a residential heat pump system. The quasi steady-state simulation uses an HVAC system simulation framework driven by DOE/ORNL Heat Pump Design Model (HPDM). Preliminary experiment validations with heat pump refrigerant leakage tests demonstrate the deviation of the proposed charge prediction method compared with measurement is within 8%. This technology makes refrigerant charge amount available at the technician’s fingertips and leads to shorter maintenance time and fewer site visits.

Li, Zhenning↗

DOE OSTI2022

Connected Loads – Grid Connected Appliances: Deployment IoT Solution for Fault Detection and Diagnostics

As one of the most energy-intensive end-uses in the commercial buildings sector, supermarkets consume around 50 kWh/ft 2 ( or 537.6 kWh/m 2 ) of electricity annually, or more than 2 million kWh of electricity per year for a typical store. The biggest consumer of energy in a supermarket is its refrigeration system, which accounts for 40–60% of its total electricity usage and is equivalent to about 2–3% of the total energy consumed by commercial buildings in United States, or around 0.5 quadrillion Btu (or 0.53 quadrillion KJ). Also, the supermarket refrigeration system is one of the biggest consumers of refrigerants. Current supermarket refrigeration systems rely on high global warming potential hydrofluorocarbon refrigerants. Reducing refrigerant usage or using environment friendly alternatives can result in significant climate benefits. Transcritical CO2 refrigeration systems have attracted more attention in recent years because of their zero-carbon emission advantages compared with traditional refrigerants. These systems are widely used in commercial buildings such as supermarkets. The refrigeration system can also be adapted to handle flexible building loads and be integrated into grid response transactive control to balance the supply and demand of the electric grid. Even minor improvements in the efficiency and operational reliability of supermarket refrigeration systems can create significant value in terms of saving energy, improving food quality, protecting the environment, reducing carbon footprint, and enhancing electric grid resilience. For decarbonization, the new administration has set a target to reduce greenhouse gas emissions by 50– 52% by 2030 and targeting a carbon-neutral economy by 2050. For electrification, the goal is to achieve 100% clean electricity by 2035. Such decarbonization and electrification in the building sector require that energy consumption in buildings be reduced significantly. Therefore, the building sector must continuously adopt new technologies to achieve its energy and carbon emission goals. One of the most fundamental technologies is the Internet of Things (IoT). IoT has proven to be an effective solution for the building domain, including building information/energy modeling, smart buildings, etc. Although much progress has been made in the development of IoT-based building energy systems, there is still a lack of reliable, scalable, and affordable IoT-based automated fault and degradation diagnostics (AFDDs) solutions. Such solutions would enable deployment of advanced algorithms into real systems to archive the projected energy benefits. This study reviews existing IoT solutions developed for building energy– related application and developed a simple but effective AFDD IoT deployment solution, including developing a suitable IoT architecture and conducting easy and scalable deployment by leveraging a common cloud-based IoT service.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Development and validation of aggregation method for fault detection and diagnostics in HVAC systems

Not Available

Kim, Woohyun↗

DOE OSTI2021

Connected Loads – Grid Connected Appliances: Commercial Refrigeration System Fault Detection and Diagnostics

As one of the most energy-intensive end-uses in the commercial buildings sector, supermarkets consume around 50 kWh/ft 2 (or 537.6 kWh/m 2 ) of electricity annually, or more than 2 million kWh of electricity per year for a typical store. The biggest consumer of energy in a supermarket is its refrigeration system, which accounts for 40–60% of its total electricity usage and is equivalent to about 2–3% of the total energy consumed by commercial buildings in United States, or around 0.5 quadrillion Btu (or 0.53 quadrillion KJ). Also, the supermarket refrigeration system is one of the biggest consumers of refrigerants. Current supermarket refrigeration systems rely on high global warming potential hydrofluorocarbon refrigerants. Reducing refrigerant usage or using environment friendly alternatives can result in significant climate benefits. Transcritical CO2 refrigeration systems have attracted more attention in recent years because of their zero-carbon emission advantages compared with traditional refrigerants. These systems are widely used in commercial buildings such as supermarkets. The refrigeration system can also be adapted to handle flexible building loads and be integrated into grid response transactive control to balance the supply and demand of the electric grid. Even minor improvements in the efficiency and operational reliability of supermarket refrigeration systems can create significant value in terms of saving energy, improving food quality, protecting the environment, reducing carbon footprint, and enhancing electric grid resilience.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗