Fault Detection And Diagnosis Approaches Research Articles

A whole-building data-driven fault detection and diagnosis approach for public buildings in hot climate regions

Fault detection and diagnosis (FDD) approaches comprise three main pillars: model-based, knowledge-based, and data-driven strategies. Data-driven approaches prioritise operational data and do not necessitate in-depth understanding of the system's background; yet, significant amounts of data is required, which often poses challenges to researchers. Since simulated data is inexpensive and can run numerous faults types with varying severities and time periods, it has been used in data-driven FDD analysis. However, the majority of FDD approaches are implemented at the system level of buildings. However, most buildings have numerous systems with distinct features. Furthermore, using individualised system-level analysis makes it difficult to see system-to-system relationships. Currently, there is a significant underrepresentation of research that investigate the applications of FDD models under whole-building scenarios, so as to identify a wider range of energy consumption related faults in buildings. Furthermore, since data-driven approaches significantly depend on the quantities of training data, it becomes challenging to diagnose faults that have limited features. As a result, this study diagnoses numerous building systems faults, including single and simultaneous faults with limited features. This is implemented within the context of the whole-building energy performance of religious buildings in hot climatic areas, employing data-driven FDD methodologies. Various multi-class classification approaches were investigated to classify both the normal condition and faulty classes. Furthermore, feature extraction methodologies were compared to quantify their potential for improving the diagnosis. In addition to the classification evaluation metrics, one-way ANOVA and Tukey-Kramer tests were implemented to examine the significance of the reported performance differences. RF classifier obtained highest classification accuracy during validation and testing with about 90%, indicating a promising performance in whole-building faults analysis. The adoption of feature extraction techniques did not improve classification performance, thereby emphasising that some classifiers may perform better with high-dimensional datasets.

Advances in Fault Detection and Diagnosis for Thermal Power Plants: A Review of Intelligent Techniques

Thermal power plants (TPPs) are critical to supplying energy to society, and ensuring their safe and efficient operation is a top priority. To minimize maintenance shutdowns and costs, modern TPPs have adopted advanced fault detection and diagnosis (FDD) techniques. These FDD approaches can be divided into three main categories: model-based, data-driven-based, and statistical-based methods. Despite the practical limitations of model-based methods, a multitude of data-driven and statistical techniques have been developed to monitor key equipment in TPPs. The main contribution of this paper is a systematic review of advanced FDD methods that addresses a literature gap by providing a comprehensive comparison and analysis of these techniques. The review discusses the most relevant FDD strategies, including model-based, data-driven, and statistical-based approaches, and their applications in enhancing the efficiency and reliability of TPPs. Our review highlights the novel and innovative aspects of these techniques and emphasizes their significance in sustainable energy development and the long-term viability of thermal power generation. This review further explores the recent advancements in intelligent FDD techniques for boilers and turbines in TPPs. It also discusses real-world applications, and analyzes the limitations and challenges of current approaches. The paper highlights the need for further research and development in this field, and outlines potential future directions to improve the safety, efficiency, and reliability of intelligent TPPs. Overall, this review provides valuable insights into the current state-of-the-art in FDD techniques for TPPs, and serves as a guide for future research and development.

Advanced fault detection and diagnosis in spacecraft attitude control systems: Current state and challenges

A review of advanced fault detection and diagnosis (FDD) techniques in attitude control systems (ACSs) of spacecraft is presented. In the first part of the paper, several types of ACS failure scenarios with their practical solutions are presented. Next, the existing approaches to FDD are considered and classified based on different criteria, including applications and design techniques. The literature of this part showed that to enhance ACS operational safety, predictability of failure of an ACS and/or of its components as well as reducing the possibility of failure occurrence is imperative. In addition, fast FDD of various kinds of failures is necessary to guarantee the required reliability of an ACS. The second part of this study highlights challenges involved with different FDD approaches, emphasizing their practical applicability. Current research gaps in FDD techniques such as insensitive residual signal, process monitoring methods, accurate plant model design, easy-to-use software development, FDD tuning process, dealing with noisy sensor measurements, time taken for fault management, the sensitivity of FDD system to faults, and FDD robustness are further elaborated on. Subsequently, the state-of-the-art FDD and its future needs are reflected on. The results of this study could direct spacecraft manufacturers and ACS providers to focus on future needs and improve ground testing for enhanced operational reliability and redundancy.

A Review of Data-Driven Approaches and Techniques for Fault Detection and Diagnosis in HVAC Systems.

Heating, ventilation, and air conditioning (HVAC) systems are a popular research topic because buildings' energy is mostly used for heating and/or cooling. These systems heavily rely on sensory measurements and typically make an integral part of the smart building concept. As such, they require the implementation of fault detection and diagnosis (FDD) methodologies, which should assist users in maintaining comfort while consuming minimal energy. Despite the fact that FDD approaches are a well-researched subject, not just for improving the operation of HVAC systems but also for a wider range of systems in industrial processes, there is a lack of application in commercial buildings due to their complexity and low transferability. The aim of this review paper is to present and systematize cutting-edge FDD methodologies, encompassing approaches and special techniques that can be applied in HVAC systems, as well as to provide best-practice heuristics for researchers and solution developers in this domain. While the literature analysis targets the FDD perspective, the main focus is put on the data-driven approach, which covers commonly used models and data pre-processing techniques in the field. Data-driven techniques and FDD solutions based on them, which are most commonly used in recent HVAC research, form the backbone of our study, while alternative FDD approaches are also presented and classified to properly contextualize and round out the review.

Enhancing Interpretability of Data-Driven Fault Detection and Diagnosis Methodology with Maintainability Rules in Smart Building Management

Data-driven fault detection and diagnosis (FDD) methods, referring to the newer generation of artificial intelligence (AI) empowered classification methods, such as data science analysis, big data, Internet of things (IoT), industry 4.0, etc., become increasingly important for facility management in the smart building design and smart city construction. While data-driven FDD methods nowadays outperform the majority of traditional FDD approaches, such as the physically based models and mathematically based models, in terms of both efficiency and accuracy, the interpretability of those methods does not grow significantly. Instead, according to the literature survey, the interpretability of the data-driven FDD methods becomes the main concern and creates barriers for those methods to be adopted in real-world industrial applications. In this study, we reviewed the existing data-driven FDD approaches for building mechanical & electrical engineering (M&E) services faults and discussed the interpretability of the modern data-driven FDD methods. Two data-driven FDD strategies integrating the expert reasoning of the faults were proposed. Lists of expert rules, knowledge of maintainability, international/local standards were concluded for various M&E services, including heating, ventilation air-conditioning (HVAC), plumbing, fire safety, electrical and elevator systems based on surveys of 110 buildings in Singapore. The surveyed results significantly enhance the interpretability of data-driven FDD methods for M&E services, potentially enhance the FDD performance in terms of accuracy and promote the data-driven FDD approaches to real-world facility management practices.

Statistical characterization of semi-supervised neural networks for fault detection and diagnosis of air handling units

Air handling units have been widely adopted in modern buildings for indoor air regulation and circulation tasks. The accurate and reliable fault detection and diagnosis (FDD) of air handling units (AHU) are of great significance to maintain indoor environment while ensuring the energy efficiency in building operations. Data-driven FDD approaches have gained great popularity due to their excellence and flexibilities for practical applications. Given sufficient labeled data, existing studies have validated the value of various supervised learning algorithms for FDD tasks. However, it can be very challenging, expensive, time-consuming and labor-intensive to obtain data labels for faulty operations, making it impractical to fully realize the potential of advanced supervised learning algorithms. To tackle this problem, this study proposes a novel semi-supervised FDD method using neural networks. The method adopts the self-training strategy for semi-supervised learning and has been tested for two practical applications, i.e., fault diagnosis and unseen fault detection. A number of data experiments have been conducted to statistically characterize the influence of key learning parameters, including the labeled data availabilities, the maximum semi-supervised learning iterations, the threshold and learning rate for pseudo-label data utilization. The results indicate that the method can effectively enhance model generalization performance by utilizing large amounts of unlabeled data. The insights obtained are helpful for developing advanced data-driven tools for smart building system fault detection and diagnosis.

A review on fault detection and diagnosis techniques: basics and beyond

Safety and reliability are absolutely important for modern sophisticated systems and technologies. Therefore, malfunction monitoring capabilities are instilled in the system for detection of the incipient faults and anticipation of their impact on the future behavior of the system using fault diagnosis techniques. In particular, state-of-the-art applications rely on the quick and efficient treatment of malfunctions within the equipment/system, resulting in increased production and reduced downtimes. This paper presents developments within Fault Detection and Diagnosis (FDD) methods and reviews of research work in this area. The review presents both traditional model-based and relatively new signal processing-based FDD approaches, with a special consideration paid to artificial intelligence-based FDD methods. Typical steps involved in the design and development of automatic FDD system, including system knowledge representation, data-acquisition and signal processing, fault classification, and maintenance related decision actions, are systematically presented to outline the present status of FDD. Future research trends, challenges and prospective solutions are also highlighted.

Air Data Sensor Fault Detection and Diagnosis in the Presence of Atmospheric Turbulence: Theory and Experimental Validation With Real Flight Data

Managing air data sensor fault detection and diagnosis (FDD) in the presence of atmospheric turbulence is challenging since the effects of faults and turbulence are coupled. Existing FDD approaches cannot decouple the faults from the turbulence. To address this challenge, this brief first proposes a novel kinematic model that incorporates the effects of the turbulence. This model is valid inside the entire flight envelope, and there is no need to design a linear parameter varying system. Then, the double-model adaptive estimation algorithm is extended to achieve unbiased state estimation even in the presence of unknown disturbances. The proposed approach is validated using generated turbulence data with various scale lengths and intensities. More importantly, the proposed approach is successfully validated using the real flight test data of a business jet when it is experiencing atmospheric turbulence.

A Review on Fault Detection and Process Diagnostics in Industrial Processes

The main roles of fault detection and diagnosis (FDD) for industrial processes are to make an effective indicator which can identify faulty status of a process and then to take a proper action against a future failure or unfavorable accidents. In order to enhance many process performances (e.g., quality and throughput), FDD has attracted great attention from various industrial sectors. Many traditional FDD techniques have been developed for checking the existence of a trend or pattern in the process or whether a certain process variable behaves normally or not. However, they might fail to produce several hidden characteristics of the process or fail to discover the faults in processes due to underlying process dynamics. In this paper, we present current research and developments of FDD approaches for process monitoring as well as a broad literature review of many useful FDD approaches.

Fault Detection and Diagnosis in Multi-Robot Systems: A Survey.

The use of robots has increased significantly in the recent years; rapidly expending to numerous applications. These sophisticated machines are susceptible to different types of faults that might endanger the robot or its surroundings. These faults must be detected and diagnosed in time to allow continual operation. The field of Fault Detection and Diagnosis (FDD) has been studied for many years. This research has given birth to many approaches that are applicable to different types of physical machines. However, the domain of robotics poses unique requirements that challenge traditional FDD approaches. The study of FDD for robotics is relatively new; only few surveys were presented. These surveys have focused on the single robot scenario. To the best of our knowledge, there is no survey that focuses on FDD for Multi-Robot Systems (MRS). In this paper we set out to fill this gap. This paper provides detailed insights to the world of FDD for MRS. We first describe how different attributes of MRS pose different challenges for FDD. With respect to these challenges, we survey different FDD approaches applicable for MRS. We conclude with a description of research opportunities in this field. With these contributions it is the authors’ intention to provide detailed insights to the world of FDD for MRS.

An auto-deployed model-based fault detection and diagnosis approach for Air Handling Units using BIM and Modelica

Abstract The Air Handling Unit (AHU) is one of the most energy consuming devices in building systems. Fault Detection and Diagnosis (FDD) methods integrated into AHUs can help to ensure that they comply with the intended design, and their efficiency is maintained throughout the entire operational stage of the building. Nonetheless, the implementation and deployment of FDDs at the operational stage require an extensive effort. Especially, FDD approaches that rely on first principle models (model-based FDD) need to be manually implemented, and the information necessary for this process is scattered between several exchange formats and files, thus making it time-consuming, error-prone and subject to modellers' poor judgment. This study aims at facilitating and partially automating the implementation and deployment of model-based FDD. An automated tool-chain that combines a BIM (Building Information Model)-to-BEPS (Building Energy Performance Simulation) tool with a model-based FDD approach is developed. The contribution of this paper lies in the extension of an existing BIM to Modelica BEPS method with an automated calibration approach and a novel model-based FDD. These three elements are integrated in a framework (implemented using Python) to reduce experts' involvement in FDD implementation and deployment. The developed model-based FDD combines a parity relation procedure for fault detection and profile identification for fault diagnosis. The latter uses the robust multi-objective optimisation algorithm NSGA-2. An error is detected when the difference between prediction and measured data over a specific time window is superior to a predefined threshold. The origin of the error is subsequently identified by estimating the profile of the different controllable components' control signal. The developed tool-chain was applied to an actual AHU as well as on several numerical scenarios to identify typical AHU faults such as faulty dampers, valves and sensors. This study shows that the developed model-based FDD approach can identify some of the most common faults in AHUs, but more importantly that BIM can facilitate the deployment of model-based FDD in building systems.

Semi-supervised learning for early detection and diagnosis of various air handling unit faults

Modern data-driven fault detection and diagnosis (FDD) techniques show impressive high diagnostic accuracy in recognizing various air handling units (AHUs) faults. Most existing data-driven FDD approaches simply adopt supervised machine learning techniques that presume the availability of a sufficient number of faulty training data samples. However, in real-world AHU FDD scenarios, the number of faulty training samples is not enough to support supervised learning methods, since faults are usually fixed within short periods of time. In this study, a semi-supervised learning FDD framework is proposed to deal with the above problem. By using the proposed framework, the training pool can be enriched by iteratively inserting confidently labeled testing samples, which mimics the scenario of detecting faults the earliest possible. Furthermore, the proposed framework can be easily extended with various kinds of state-of-art classifiers. Three important tradeoffs are observed through a series of experiments. With a reasonably small number of faulty training data samples available, the performance of the proposed semi-supervised learning technique is comparable to the classic supervised FDD methods.

On Fault Detection and Diagnosis in Robotic Systems

The use of robots in our daily lives is increasing. Different types of robots perform different tasks that are too dangerous or too dull to be done by humans. These sophisticated machines are susceptible to different types of faults. These faults have to be detected and diagnosed in time to allow recovery and continuous operation. The field of Fault Detection and Diagnosis (FDD) has been studied for many years. This research has given birth to many approaches and techniques that are applicable to different types of physical machines. Yet the domain of robotics poses unique requirements that are very challenging for traditional FDD approaches. The study of FDD for robotics is relatively new, and only few surveys were presented. These surveys have focused on traditional FDD approaches and how these approaches may broadly apply to a generic type of robot. Yet robotic systems can be identified by fundamental characteristics, which pose different constraints and requirements from FDD. In this article, we aim to provide the reader with useful insights regarding the use of FDD approaches that best suit the different characteristics of robotic systems. We elaborate on the advantages these approaches have and the challenges they must face. To meet this aim, we use two perspectives: (1) we elaborate on FDD from the perspective of the different characteristics a robotic system may have and give examples of successful FDD approaches, and (2) we elaborate on FDD from the perspective of the different FDD approaches and analyze the advantages and disadvantages of each approach with respect to robotic systems. Finally, we describe research opportunities for robotic systems’ FDD. With these three contributions, readers from the FDD research communities are introduced to FDD for robotic systems, and the robotics research community is introduced to the field of FDD.

Integrated fault detection and diagnosis system for longitudinal control of an autonomous all-terrain vehicle (ATV)

This paper presents a fault detection and diagnosis (FDD) method to enhance the reliability and safety for longitudinal control of an autonomous all-terrain vehicle (ATV). An integrated approach using decentralized and centralized FDD is proposed to optimize the tradeoff between sensitivity and robustness. While the decentralized approach is suitable for detecting faults in actuators and sensors directly connected to a single processor, it is sensitive to noises and disturbances and thus may result in false alarms. On the other hand, the centralized approach is based on information communicated between multiple processors, and it detects and diagnoses faults through analyzing concurrent computations of multiple hardware modules. However, its performance is still limited to isolating faults specifically in terms of components in the single hardware. To incorporate the advantages of both FDD approaches, a two-layered structure integrating both decentralized and centralized FDD is proposed and allows us to perform more robust fault detection as well as more detailed fault isolation. Finally, the proposed method is validated experimentally via field tests of an ATV.

ISSUES ON INTEGRATION OF FAULT DIAGNOSIS AND RECONFIGURABLE CONTROL IN ACTIVE FAULT-TOLERANT CONTROL SYSTEMS

In this paper, several important issues on the integrated design of Fault Detection and Diagnosis (FDD) schemes together with Reconfigurable Control (RC) for Active Fault-Tolerant Control Systems (AFTCS) are addressed and discussed. The existing approaches in FDD and RC are briefly reviewed and examined from integrated design of FDD and RC viewpoint. The role of FDD in AFTCS, the applicability, suitability and issues in existing FDD approaches for AFTCS are examined and outlined. The need, development, and design considerations for integrated design to fault detection, diagnosis and reconfigurable control are discussed.