Fault Detection And Diagnosis Systems Research Articles

Barriers and drivers for implementation of automatic fault detection and diagnosis in buildings and HVAC systems: An outlook from industry experts

This study aimed to assess the current status of Fault Detection and Diagnosis (FDD) implementation in building and Heating, Ventilation and Air Conditioning (HVAC) systems in the building industry. Semi-structured qualitative interviews were conducted with 29 experts from different HVAC company types in the building industry. In addition, a literature review was performed to investigate academic research on FDD implementation. The study identified barriers and drivers to implementing FDD systems, these included; technological and technical, economic and business, users, social and societal, and regulatory. An Automatic Fault Detection and Diagnosis (AFDD) implementation matrix was developed to evaluate FDD implementation in building systems, and all interviewed companies were classified based on their FDD knowledge, services, and type. Results show that expert-rule systems are still prevalent in the industry. The literature review revealed a scarcity of FDD implementation studies in academic research due to challenges in testing and validating results in actual building operation conditions. Lastly, this study discusses the key findings: 1) FDD does not sell, 2) Lack of actively engaging and promoting FDD services, 3) FDD seems to be an academic definition, 4) The bottlenecks: The fault handling process and user’s mindset towards FDD, and 5) Governmental regulations and legislatives drive the implementation focus.

Faulty scenarios in sour water treatment units: Simulation and AI-based diagnosis

Fault Detection and Diagnosis (FDD) is a Process System Engineering (PSE) area of great importance, especially with increased process automation. It is one of the chemical engineering fields considered promising to Artificial Intelligence (AI) application. FDD systems can be useful to supervise Sour Water Treatment Units (SWTU) behavior, as they are chemical processes that present operational difficulties when disturbances occur. SWTU remove contaminants from sour water (SW) streams generated through petroleum processing, consisting mainly of small amounts of H2S and NH3. They are considered one of the primary aqueous wastes of refineries and cannot be disposed of due to environmental regulations. However, no previous studies focused on the development of FDD systems for SWTU exist and works on its dynamics are scarce. Hence, the present work proposes to study the dynamic simulated behavior of an SWTU and develop an FDD system applying AI techniques with hyperparameters optimization. The simulation was performed in Aspen Plus Dynamics® and ran to create normal operation and six relevant faults, including occurrences in the process (e.g., inundation and fouling) and sensors. FDD was performed through data classification, and results were evaluated mainly by accuracy and confusion matrices. Even after variable reduction, FDD was satisfactory with over 87.50% accuracy in all AI techniques. RF and SVM with linear and Gaussian kernels presented the best results, with over 93% of accuracy in training and testing, and had the shortest computing times. The second column’s sump level proved to be the most relevant variable for fault identification.

A Review of Fault Detection and Diagnosis Methodologies for Air-Handling Units

HVAC (Heating, Ventilation and Air-Conditioning) systems for space heating, space cooling and ventilation of buildings consume nearly 40% of the world energy demand and present the least expensive opportunities for reducing the greenhouse gases emission. Fault Detection and Diagnosis (FDD) methods could monitor the operation of various processes and/or components allowing to detect and, if possible, even predict the presence of defects (deviations from normal or expected operation) as well as ideally identify (diagnose) the fault and/or its location, giving instructions for undertaking corrective actions. FDD techniques could be successfully used for managing the predictive maintenance and/or optimizing the energy/economic/environmental performance of HVAC units while assuring the comfort of occupants. This paper examines the current state of the art of the research on the development and implementation of FDD systems when applied to Air-Handling Units (AHUs), the main and most important device of HVAC systems. This paper describes the existing methodologies, approaches and tools for the utilization of FDD techniques, summarizes the most important findings available in current literature in reference to several case studies where FDD systems have been applied with reference to AHUs and indicates the main gaps to be further investigated.

Handling Incomplete Sensor Measurements in Fault Detection and Diagnosis for Building HVAC Systems

Due to the development of sensor networks and information technology, data-driven fault detection and diagnosis (FDD) has been made possible with real-time multiple sensor measurements. However, due to inevitable sensor errors or communication failures, the raw data are usually incomplete with corrupted values, lost values, or undetected missing values. In practice, the incomplete data are usually dealt with by directly excluding incomplete measurements and abnormal spikes. In addition, some preprocessing methods, which naively impute data though averaging or smoothing, have also been widely applied. In this article, we address the building FDD problem with incomplete data by proposing a new approach, the adjacent information recovery (AIR) filter. The AIR filter is utilized to deal with the FDD for a typical air handling unit (AHU) system with incomplete data based on the ASHRAE Research Project 1312. Experimental results show that the proposed method improves FDD performance by recovering missing sensor measurements and outperforms the state-of-the-art methods. Note to Practitioners —Fault detection and diagnosis (FDD) for smart buildings by addressing the fact that FDD systems are of great importance for saving energy and improving occupancy comfort levels and building safety levels. Existing FDD methods are mainly based on the assumption that sensor data are complete and reliable, which are rarely true in real practice. To solve the building FDD problem with incomplete data, in this article, the adjacent information recovery (AIR) filter is proposed to recover the missing data before applying FDD methods. The AIR filter takes the time series adjacency information into consideration via hidden Markov model (HMM) and includes the channel adjacency information with the collaborating filtering technique.

A Symmetrical Component Feature Extraction Method for Fault Detection in Induction Machines

Induction motors (IMs) are among the fully developed electromechanical technologies that are still in use today. Over the course of the last century, their structure, control, and operation have been undergone through several stages of development. Among stages of development, the automated control and continuous monitoring of IMs has benefited from the emergence of modern artificial intelligence (AI) methods. IM automation schemes have demonstrated the ability to provide machine fault detection and diagnosis (FDD) function. AI-based FDD methods in IMs have employed frequency-domain, time-frequency, and time-domain analyses as the basis of their feature extraction schemes. A promising feature extraction scheme is one that uses symmetrical components (SCs) in time-domain FDD systems. Current SC-based approaches, however, are limited in their generalizability to different fault classes, may require detailed machine models, and can suffer from computational limitations. In this paper, an improved feature extraction method that uses SCs for a pattern recognition based FDD scheme for three-phase (3φ) IMs will be presented. This novel feature extraction method will be implemented and verified experimentally to demonstrate high classification performance, increased generalizability, and low computational cost.

Challenges and Opportunities of Deep Learning Models for Machinery Fault Detection and Diagnosis: A Review

In the age of industry 4.0, deep learning has attracted increasing interest for various research applications. In recent years, deep learning models have been extensively implemented in machinery fault detection and diagnosis (FDD) systems. The deep architecture's automated feature learning process offers great potential to solve problems with traditional fault detection and diagnosis (TFDD) systems. TFDD relies on manual feature selection, which requires prior knowledge of the data and is time intensive. However, the high performance of deep learning comes with challenges and costs. This paper presents a review of deep learning challenges related to machinery fault detection and diagnosis systems. The potential for future work on deep learning implementation in FDD systems is briefly discussed.

Abnormal situation management: Challenges and opportunities in the big data era

Abstract Although modern chemical processes are highly automatic, abnormal situation management (ASM) still heavily relies on human operators. Process fault detection and diagnosis (FDD) are one of the most important issues of ASM but few FDD systems have been satisfactorily applied in real chemical processes since the concept of FDD was proposed about 40 years ago. In this paper, developments of chemical process FDD are briefly reviewed. The reason why FDD has not been widely implemented in the chemical process industry is discussed. One of the insights gained is that some basic problems in FDD such as how to define faults and how many faults to diagnose have not even been addressed well while researchers tirelessly try to invent new methods to diagnose fault. A new framework is proposed based on the big data in a cloud computing environment of a big chemical corporation for addressing the challenging issues in ASM.

Detection and Identification of Loss of Efficiency Faults of Flight Actuators

Abstract We propose linear parameter-varying (LPV) model-based approaches to the synthesis of robust fault detection and diagnosis (FDD) systems for loss of efficiency (LOE) faults of flight actuators. The proposed methods are applicable to several types of parametric (or multiplicative) LOE faults such as actuator disconnection, surface damage, actuator power loss or stall loads. For the detection of these parametric faults, advanced LPV-model detection techniques are proposed, which implicitly provide fault identification information. Fast detection of intermittent stall loads (seen as nuisances, rather than faults) is important in enhancing the performance of various fault detection schemes dealing with large input signals. For this case, a dedicated fast identification algorithm is devised. The developed FDD systems are tested on a nonlinear actuator model which is implemented in a full nonlinear aircraft simulation model. This enables the validation of the FDD system’s detection and identification characteristics under realistic conditions.

Automatic HVAC fault detection and diagnosis system generation based on heat flow models

This article introduces a new graph-based modeling methodology called heat flow modeling (HFM) for the purpose of mapping building information model (BIM) of HVAC systems automatically into fault detection and diagnosis (FDD) systems that can be integrated into HVAC control systems. The goal is an efficient and effective support of the maintenance of HVAC systems to detect and locate faults that may reduce energy efficiency, user comfort, or system lifetime. The nodes of the HFM model have a one-to-one relationship with HVAC system components and related building entities. The nodes are connected by arcs that model the flows in the HVAC systems, e.g., air, water, and information flows. The functionality of the nodes includes state variable estimations and failure rule evaluations. The failure rule outputs can be fed to an associative network based diagnosis engine to locate the faults. Since HFM nodes are instances of generic classes derived from small libraries, HVAC FDD systems can be automatically generated. The simulation result has shown the effectiveness of a proposed FDD approach and two software prototypes demonstrating the reduced engineering effort of fault detection for a small bank HVAC system.

가정용 열펌프 시스템의 정상냉방 운전조건에서 기준모델에 의한 잔차의 통계적 분석

. 도와 경계값을 통계적으로 계산함으로써 진단결과의 확률적 신뢰성을 보장할 수 있는 방법론을 제공하였다. Abstract: To approximate the threshold of the fault detection and diagnosis (FDD) system, validation of the measurements is mandatory. Naturally, the system shows uncertainties due to measuring sensors - mostly thermocouples or RTDs - and due to repeatability. The uncertainty of a thermocouple comes from natural variation or a drift of the thermocouple measurement. Considering the natural variation behaves like zero-mean white noise, its natural variation can be characterized closely by the steady-state standard deviation. However, residuals between measurements and no-fault references in FDD systems show a statistical distribution with various uncertainties. In this paper, steady-state variations of measurement residuals were investigated by utilizing built-in temperature sensors in a heat pump for the model development and the final application.