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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Automated fault detection and diagnosis of airflow and refrigerant charge faults in residential HVAC systems using IoT-enabled measurements

While automated fault detection and diagnosis (AFDD) in residential heating, ventilation, and air-conditioning (HVAC) using smart thermostat data is gaining increasing attention in recent times, it still requires in-depth investigation for market adoption, especially with real-life data. Furthermore, this paper proposes an Internet of Things (IoT) - based approach that adds a smart sensor to the smart thermostat data to carry out AFDD. The approach uses a model which predicts enthalpy change across the evaporator and compares the prediction to the measured enthalpy change. Deviations which exceed analytically determined thresholds then signal faults in the HVAC system. The faults detected are either installation related or degradation related. Experimental tests were carried out in four homes located in Norman, Oklahoma. From the tests, installation issues like indoor/outdoor mismatch were detected in two homes, while a 30% low charge and low indoor airflow rate were detected in one home. The results show that the proposed AFDD algorithm was able to successfully detect two prevalent faults, namely low indoor airflow and low refrigerant charge. Unlike most of the smart thermostat-based approaches, the proposed IoT-based approach can detect and diagnose both faults but only require one additional sensor which is provided by smart thermostat manufacturers.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Analysis of Automated Fault Detection and Diagnosis Records as an Indicator of HVAC Fault Prevalence: Methodology and Preliminary Results

Faults in commercial buildings can cause energy waste and other performance problems such as reduced occupant comfort, reduced equipment longevity, and increased noise. However, it is currently unknown how commonly faults occur in different equipment types. A method has been developed to estimate the prevalence of faults in air handling units, air terminal units, and rooftop units. This method includes two types of data. The first is data from several automated fault detection and diagnostics (AFDD) software technologies. This type of data provides a large sample that represents a wide range of building types, geographical locations, and equipment types. It includes fault diagnoses from thousands of buildings around the United States, as well as anonymized metadata describing the building and equipment characteristics. The number of fault records is in the order of 107. However, despite the size and richness of the data sample, this data contains some degree of inaccuracy, i.e., false positive and false negative findings. Therefore, the study includes a second type of data, coming from manual inspection of buildings that have had the same AFDD methods applied to them (from the commercial AFDD offerings). Since the field tests are conducted in buildings with AFDD-generated fault prevalence data, they can be combined with the larger sample size to provide insight into the potential biases or lower sensitivity of the AFDD data. Once a library of fault prevalence data is built, it will be studied to provide further insight into the drivers of fault prevalence, for example, whether prevalence is correlated with building type, geographical location (which is tied to climate and to utility rates), building size, etc. This paper describes the methods developed for this study and illustrates them with preliminary data. It discusses some of the challenges of harmonizing disparate outputs from multiple AFDD vendors, application of a unifying fault taxonomy, and fault prevalence metrics.

Ebrahimi Fakhar, Amir↗
DOE OSTI2022

Automated fault detection and diagnosis deployment Internet of Things solution for building energy system

The building sector is the highest energy consumer and ranks first in terms of carbon emissions among all sectors. To address these issues, decarbonization and electrification in the building energy sector are two critical missions of the new US administration. 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 diagnostic (AFDD) solutions. Such solutions would enable deployment of advanced algorithms into real systems to archive the projected energy benefits. Here, 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.

42 ENGINEERING↗
DOE OSTI2022

A review of computing-based automated fault detection and diagnosis of heating, ventilation and air conditioning systems

We report faults in Heating, Ventilation, and Air Conditioning (HVAC) systems of buildings result in significant energy waste in building operation. With fast-growing sensing data availability and advancement in computing, computational modeling has demonstrated strong capability to detect and diagnose HVAC system faults, hence, ensuring efficient building operation. This paper comprehensively reviews the state-of-the-art computing-based fault detection and diagnosis (FDD) for HVAC systems. Overall, the reviewed computing-based FDD methods are classified as two major approaches: knowledge-based and data-driven approaches. We then identify multiple important topics, including data availability, training data size, data quality, approach generality, capability, interpretability, and required modeling efforts, along with corresponding metrics to summarize the most updated FDD development. Generally, the knowledge-based approaches are further divided as physics-based modeling, Diagnostic Bayesian Network, and performance indicator-based methods while data-driven approaches include supervised learning, unsupervised learning, and regression and statistics-based methods. State-of-the-art FDD development, remaining challenges, and future research directions are further discussed to push forward FDD in practice. Availability of fault data, capability of existing methods to deal with complex fault situations (such as simultaneous faults), modeling interpretability for data-driven methods, and required engineering efforts for physics-based methods are identified as remaining challenges in FDD development. Improving modeling fidelity and reducing modeling efforts are essential for applying physics-based methods in real buildings. Meanwhile, addressing fault data availability, increasing algorithm adaptability, and handling multiple faults are essential to further enhance the applicability of data-driven FDD approaches.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Automating Detection and Diagnosis of Faults, Failures, and Underperformance in PV Plants

The project developed hybrid physics-based and machine-learning methods for near-real-time detection of balance-of-system faults (e.g., string, combiner, and tracker outages) in utility-scale Photovoltaic plants, achieving over 50% true positive rates with under 10% false positives and significantly reducing engineering setup time. In the extended phase, the scope expanded to plant-level underperformance analysis and industry benchmarking through the SUPER.epri.com platform. SUPER standardizes data processing and performance metrics across more than 9 GWac and 120+ plants, enabling robust comparisons and insights into loss rates, inverter downtime, and capacity degradation.

14 SOLAR ENERGY↗
DOE OSTI2022

A digital twin approach to system-level fault detection and diagnosis for improved equipment health monitoring

Automating the task of fault detection and diagnosis is crucial in the effort to reduce the operation and maintenance cost in the nuclear industry. This paper describes a physics-based approach for system-level diagnosis in thermal-hydraulic systems in nuclear power plants. The inclusion of physics information allows for the creation of virtual sensors, which provide improved fault diagnosis capability. The physics information also serves to better constrain diagnostic solutions to the physical domain. As a demonstration, various test cases for fault diagnosis in a high-pressure feedwater system were considered. The use of virtual sensors allows constructing performance models for two first-point feedwater heaters which would not have been possible otherwise due to the limited sensor set. Real-time plant data provided by a utility partner were used to assess the diagnostic approach. The detection of an abnormal event immediate after a plant startup pointed to faulty behaviors in the two first-point feedwater heaters. Further, this double-blind fault diagnosis was subsequently confirmed by the plant operator. In addition, several simulated sensor fault events demonstrated the capability of our algorithms in detecting and discriminating sensor faults.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

IoT-Based Comfort Control and Fault Diagnostics System for Energy-Efficient Homes

This project studies an Internet of Things (IoT)-based comfort control and fault diagnostics system (referred as iComfort in this report) for energy-efficient homes. The system delivers an occupant-comfort-oriented thermal environment adaptive to fault scenarios and achieves HVAC energy savings in a cost-effective and straightforward way. This smart iComfort home system consists of the following key features. 1) Cost-effectiveness and scalability of the entire hardware and software system: The system includes low-cost temperature, humidity, and airflow sensors, and a Raspberry Pi-based local hub that interfaces with the cloud and IoT-enabled devices. The cost is low, not only for sensors, but also the costs associated with sensor installation, system setup and commissioning, data communication and storage, and data analytics (e.g., the development of automated fault detection and diagnosis (AFDD), as well as adaptive control strategies that are both computationally efficient and practical to implement). 2) Energy performance and user satisfaction: The system delivers user satisfaction and energy savings. This includes a) ease of use, b) optimal occupant thermal comfort, and c) accurate system feedback (e.g., low false alarm of AFDD strategies). 3) Favorable demonstrated prototype performance: The prototype tested at the Pacific Northwest National Laboratory (PNNL) Lab Homes demonstrates the accuracy of fault detections and diagnoses and shows thermal comfort improvement and energy savings through adaptive and optimal HVAC operations.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Metadata Schemas and Ontologies for Building Energy Applications: A Critical Review and Use Case Analysis

With the increasing digitalization of processes throughout the lifecycle of buildings, data exchanged between stakeholders and between building systems has grown significantly. However, a lack of semantic interoperability between data in different systems is still prevalent, hindering the development of applications that can be reused across buildings and limiting the scalability of innovative solutions. Semantics refers to the description of the meaning of the data in a way that can be consistently understood by applications. Recently, several competing initiatives have been developing metadata schemas and ontologies to express this semantic information for different applications in the building domain. This paper systematically reviews these schemas and conducts an analysis of five of them to evaluate their applicability to three high-value use cases for building operations: energy audits, automated fault detection and diagnostics and optimal control. The survey finds 40 schemas published in the last 10 years but but their actual use in industry is difficult to estimate. Among the five selected ontologies, several gaps are highlighted in relation to the three use cases. Recommendations for the future include better harmonization of these initiatives, more centralized repositories and search engines for these schemas as well as better industry engagement to facilitate their adoption.

Smart Building, Sematic, Metadata, Ontology, Data ↗
DOE OSTI2024

Testing and validating SMDS algorithms implemented in the cloud

Pacific Northwest National Laboratory (PNNL) provided technical assistance to NorthWrite Inc. under the Small Business Vouchers (SBV) Pilot. NorthWrite delivered services to owners of small commercial buildings, using a cloud-based service to monitor, control, and optimize building operations, saving energy and reducing operating costs for the owners while ensuring that occupant comfort needs were consistently met. NorthWrite had a longstanding desire to add a new suite of diagnostic capabilities to their service offering and had been trying, without success, to incorporate several diagnostic algorithms published by PNNL. These algorithms arise from research previously supported by BTO. Through the SBV awarded to NorthWrite, PNNL made available technical knowledge regarding the derivation and application of the following sets of algorithms for use in the NorthWrite Cloud-based service delivery system: • algorithms for monitoring rooftop packaged air conditioners and heat pumps (often referred to as rooftop units or RTUs) and diagnosing faults in these units, • advanced algorithms for automated fault detection and diagnosis of other equipment found in small buildings, and • algorithms for identifying, prioritizing, and assessing the economic effectiveness of implementing energy saving measures in small commercial buildings based on sensed data. The PNNL researchers involved were the original developers of these algorithms, had a unique understanding of the derivation of the algorithms, and had the source data used for this derivation. Further, the PNNL researchers had extensive experience using these algorithms in the laboratory, unique experience applying these algorithms in real-world small commercial buildings and could solve a number of key problems that NorthWrite and other users faced in using these algorithms at scale. In addition, PNNL had recently installed a pair of RTUs in a laboratory setting that were instrumented and connected with data acquisition systems that provided a unique test rig for validating the algorithms before NorthWrite began to deploy their new services in the field.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Residential HVAC Fault Data Collection Plan – Refrigerant Undercharge and Overcharge Faults

Heating, ventilation, and air-conditioning (HVAC) systems can develop faults due to poor installation practices or gradual wear and tear, leading to decreased HVAC system’s efficiency, compromised thermal comfort, and shortened equipment lifespan (EERE, 2018). Automated fault detection and diagnosis (AFDD) technologies offer a solution by identifying energy-wasting HVAC faults, such as inadequate indoor airflow and incorrect refrigerant charge, and guiding technicians to enhance system efficiency. In the realm of residential HVAC, AFDD can be implemented through various fault detection and diagnosis capabilities, sensor configurations, and target applications. These technologies typically fall into three categories: smart diagnostic tools, original equipment manufacturer (OEM)-embedded tools, and add-on tools. Smart diagnostic tools employ temporarily installed sensors to directly measure HVAC system characteristics, while OEM-embedded tools utilize factory-installed sensors to identify faults or assess system performance. However, both these types of AFDD technologies are often only accessible for high-end HVAC equipment or require additional sensor installation by qualified technicians, resulting in high investment costs and limited applicability for low-income residential buildings. On the other hand, add-on tools rely solely on data from smart thermostats and meters to detect faults by continuously analyzing equipment runtime or energy usage. As smart thermostat and meter costs decrease and their prevalence increases, these tools can be readily deployed in low-income residential buildings. However, they possess limited capabilities as they rely solely on basic trend analysis. Enhancing such tools with advanced machine learning algorithms can significantly improve their effectiveness.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Development and Validation of Home Comfort System for Total Performance Deficiency/Fault Detection and Optimal Comfort Control

In this project, we developed and tested a learning-based home thermal model that facilitates the operation of a model predictive control (MPC)-based optimization agent and an automated fault detection and diagnosis (AFDD) agent. The home thermal model was constructed using a two-node resistor-capacitor model. Moreover, two accompanying parameter identification methods were introduced, least-squares and optimization. Based on the home thermal model, the MPC-based optimization agent was developed to optimize residential HVAC operation. Using two FDD methods, the AFDD agent was constructed to detect and diagnose two prevalent residential AC faults, airflow reduction and refrigerant undercharge. The home thermal model, along with the MPC-based optimization agent and AFDD agent, were tested at the Norman Test House, Miami Test House, Pacific Northwest National Laboratory (PNNL) Test House A, and PNNL Test House B. Finally, they were also field tested in nine demonstration homes with real occupants.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1990

A two-level structure for advanced space power system automation

The tasks to be carried out during the three-year project period are: (1) performing extensive simulation using existing mathematical models to build a specific knowledge base of the operating characteristics of space power systems; (2) carrying out the necessary basic research on hierarchical control structures, real-time quantitative algorithms, and decision-theoretic procedures; (3) developing a two-level automation scheme for fault detection and diagnosis, maintenance and restoration scheduling, and load management; and (4) testing and demonstration. The outlines of the proposed system structure that served as a master plan for this project, work accomplished, concluding remarks, and ideas for future work are also addressed.

Loparo, Kenneth A.↗
DOE OSTI2020

Provider and User Perspectives on Automated Fault Detection and Diagnostic Products for Packaged Rooftop Units

The goal of this project was to understand, based on the primary reporting of AFDD providers and building owners, 1) the features currently available for RTU AFDD in the market today, 2) how RTU AFDD is used by building owners and managers, 3) how well RTU AFDD meets their needs, and 4) what barriers stand in the way of its broad industry adoption. To achieve this goal, 13 AFDD providers were asked about their products and seven building owners were questioned about their practical experience related to the automated detection, diagnosis, and communication of RTU faults.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2013

Putting Integrated Systems Health Management Capabilities to Work: Development of an Advanced Caution and Warning System for Next-Generation Crewed Spacecraft Missions

Integrated System Health Management (ISHM) technologies have advanced to the point where they can provide significant automated assistance with real-time fault detection, diagnosis, guided troubleshooting, and failure consequence assessment. To exploit these capabilities in actual operational environments, however, ISHM information must be integrated into operational concepts and associated information displays in ways that enable human operators to process and understand the ISHM system information rapidly and effectively. In this paper, we explore these design issues in the context of an advanced caution and warning system (ACAWS) for next-generation crewed spacecraft missions. User interface concepts for depicting failure diagnoses, failure effects, redundancy loss, "what-if" failure analysis scenarios, and resolution of ambiguity groups are discussed and illustrated.

integrated system management↗
DOE OSTI2023

Sensitivity analysis of an automated fault detection algorithm for residential air-conditioning systems

The state of the art of fault detection and diagnosis (FDD) for residential air-conditioning systems is expensive and not yet amenable to widespread implementation. FDD for homes can significantly reduce utility costs, and increase the lifespan of the equipment. The cost barriers currently, however, make FDD for homes economically unviable for large scale implementation. In prior work, we offered a solution to reduce FDD costs by proposing an automated fault detection algorithm to serve as a screening step before more expensive FDD tests can be conducted. The algorithm uses only the home thermostat and local weather information to identify thermodynamic parameters and detect high-impact air-conditioning faults, including those that occur during equipment installation. We had tested the algorithm on a single EnergyPlus™ model of a home in Orlando, Florida. The thermodynamic parameter identification process is highly nonconvex involving several local optimal solutions. In this paper we propose a novel method to select the best model for fault detection from among the list of local optimal solutions to make the algorithm more robust to homes of different construction, without which the fault detection process would be infeasible. Another unique contribution of the paper is implementing the solution on real-world data. We also bring the algorithm closer to market by testing it on real-world data. We implement the algorithm on data obtained from experiments conducted by the Florida Solar Energy Center (FSEC) on a laboratory home equipped with a heat pump where faults were intentionally added for a period of seven months. The algorithm successfully detected an undercharge fault with 70.6% accuracy, concurrent duct leakage and undercharge faults with 85.2% accuracy, and duct leakage faults with 69.1% accuracy. A sensitivity analysis is also performed on EnergyPlus models of nine types of homes that vary in construction to demonstrate the robustness of the algorithm. Finally, the algorithm achieves an average accuracy of 71% for no-fault condition, 77% for 40% undercharge fault, and 76% for duct-leak fault.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1987

An evaluation of a real-time fault diagnosis expert system for aircraft applications

A fault monitoring and diagnosis expert system called Faultfinder was conceived and developed to detect and diagnose in-flight failures in an aircraft. Faultfinder is an automated intelligent aid whose purpose is to assist the flight crew in fault monitoring, fault diagnosis, and recovery planning. The present implementation of this concept performs monitoring and diagnosis for a generic aircraft's propulsion and hydraulic subsystems. This implementation is capable of detecting and diagnosing failures of known and unknown (i.e., unforseeable) type in a real-time environment. Faultfinder uses both rule-based and model-based reasoning strategies which operate on causal, temporal, and qualitative information. A preliminary evaluation is made of the diagnostic concepts implemented in Faultfinder. The evaluation used actual aircraft accident and incident cases which were simulated to assess the effectiveness of Faultfinder in detecting and diagnosing failures. Results of this evaluation, together with the description of the current Faultfinder implementation, are presented.

Schutte, Paul C.↗
NASA NTRS1993

Implementing a real time reasoning system for robust diagnosis

The objective of the Thermal Control System Automation Project (TCSAP) is to develop an advanced fault detection, isolation, and recovery (FDIR) capability for use on the Space Station Freedom (SSF) External Active Thermal Control System (EATCS). Real-time monitoring, control, and diagnosis of the EATCS will be performed with a knowledge based system (KBS). Implementation issues for the current version of the KBS are discussed.

Hill, Tim↗