Diagnosis of wind turbine faults using generator current signature analysis: a review

2019 ◽  
Vol 26 (3) ◽  
pp. 431-458 ◽  
Author(s):  
Yuri Merizalde ◽  
Luis Hernández-Callejo ◽  
Oscar Duque-Pérez ◽  
Víctor Alonso-Gómez
Keyword(s):  
Fault Detection ◽  
Mathematical Models ◽  
Research Area ◽  
Appropriate Technology ◽  
Fault Detection And Diagnosis ◽  
Signature Analysis ◽  
Content Type ◽  
Detection And Diagnosis ◽  
Doubly Fed ◽  
Current Signature

Purpose Despite the wide dissemination and application of current signature analysis (CSA) in general industry, CSA is not commonly used in the wind industry, where the use of vibration signals predominates. Therefore, the purpose of this paper is to review the use of generator CSA (GCSA) in the online fault detection and diagnosis of wind turbines (WTs). Design/methodology/approach This is a bibliographical investigation in which the use of GCSA for the maintenance of WTs is analyzed. A section is dedicated to each of the main components, including the theoretical foundations on which GCSA is based and the methodology, mathematical models and signal processing techniques used by the proposals that exist on this topic. Findings The lack of appropriate technology and mathematical models, as well as the difficulty involved in performing actual studies in the field and the lack of research projects, has prevented the expansion of the use of GCSA for fault detection of other WT components. This research area has yet to be explored, and the existing investigations mainly focus on the gearbox and the doubly fed induction generator; however, modern signal treatment and artificial intelligence techniques could offer new opportunities in this field. Originality/value Although literature on the use of GCSA for the detection and diagnosis of faults in WTs has been published, these papers address specific applications for each of the WT components, especially gearboxes and generators. For this reason, the main contribution of this study is providing a comprehensive vision for the use of GCSA in the maintenance of WTs.

scholarly journals Fault Detection and Efficiency Assessment for HVAC Systems Using Non-Intrusive Load Monitoring: A Review

Energies ◽  
2022 ◽  
Vol 15 (1) ◽  
pp. 341
Author(s):  
Amir Rafati ◽  
Hamid Reza Shaker ◽  
Saman Ghahghahzadeh
Keyword(s):  
Energy Efficiency ◽  
Fault Detection ◽  
Research Area ◽  
Hvac Systems ◽  
Fault Detection And Diagnosis ◽  
Future Research ◽  
Efficiency Assessment ◽  
Load Monitoring ◽  
Detection And Diagnosis ◽  
Inefficient Operation

Heat, ventilation, and air conditioning (HVAC) systems are some of the most energy-intensive equipment in buildings and their faulty or inefficient operation can significantly increase energy waste. Non-Intrusive Load Monitoring (NILM), which is a software-based tool, has been a popular research area over the last few decades. NILM can play an important role in providing future energy efficiency feedback and developing fault detection and diagnosis (FDD) tools in smart buildings. Therefore, the review of NILM-based methods for FDD and the energy efficiency (EE) assessment of HVACs can be beneficial for users as well as buildings and facilities operators. To the best of the authors’ knowledge, this paper is the first review paper on the application of NILM techniques in these areas and highlights their effectiveness and limitations. This review shows that even though NILM could be successfully implemented for FDD and the EE evaluation of HVACs, and enhance the performance of these techniques, there are many research opportunities to improve or develop NILM-based FDD methods to deal with real-world challenges. These challenges and future research works are also discussed in-depth.

Mixed fault diagnosis scheme for satellite formation

2018 ◽  
Vol 90 (2) ◽  
pp. 427-434
Author(s):  
Xiaobin Lian ◽  
Jiafu Liu ◽  
Laohu Yuan ◽  
Naigang Cui
Keyword(s):  
Fault Detection ◽  
Differential Inclusion ◽  
Fault Isolation ◽  
Fault Detection And Diagnosis ◽  
Fault System ◽  
Engineering Practice ◽  
Satellite Formation ◽  
Content Type ◽  
State Observers ◽  
Detection And Diagnosis

Purpose The purpose of this paper is to present a solution for the uncertain fault with the propulsion subsystem of satellite formation, using the Lur’e differential inclusion linear state observers (DILSOs) and fuzzy wavelet neural network (FWNN) to perform fault detection and diagnosis. Design/methodology/approach The uncertain fault system cannot be described based on the accurate differential equations. The set-value mapping is introduced into the state equations to solve the problem of uncertainty, but it will cause output uncertainty. The problem can be solved by linearization of Lur’e differential inclusion state observers. The Lur’e DILSOs can be used to detect uncertain fault. The fault isolation and estimation can be performed using the FWNN. Findings The mixed approach from fault detection and diagnosis has featured fast and correct to found the uncertain fault. The simulation results to indicate that the methods of design are not only effective but also have the advantages of good approximation effect, fast detection speed, relatively simple structure and prior knowledge and realization of adaptive learning. Research limitations/implications The hybrid algorithm can be extensively applied to engineering practice and find uncertain faults of the propulsion subsystem of satellite formation promptly. Originality/value This paper provides a fast, effective and simple mixed fault detection and diagnosis scheme for satellite formation.

Fault Diagnosis of Induction Motors Using Motor Current Signature Analysis

2019 ◽  
pp. 1-37
Author(s):  
Muthiah Geethanjali ◽  
Hemavathi Ramadoss
Keyword(s):  
Fault Diagnosis ◽  
Induction Motors ◽  
Fault Detection And Diagnosis ◽  
Future Research ◽  
Signature Analysis ◽  
Damage Spreading ◽  
Motor Current ◽  
Motor Current Signature Analysis ◽  
Detection And Diagnosis ◽  
Current Signature

Induction motors are termed as horses of modern industry because they are playing a vital role in industries. They are simple, efficient, robust, rugged, and highly reliable. The feasibility of mishap in induction motors is less, but they are prone to faults, which are left unobserved most of the time. Hence, more attention has been paid to detection and diagnosis of incipient faults to prevent damage spreading and increase the lifetime of the motor. To detect and diagnose the faults, online condition monitoring of the machine has been utilized in a wide manner. At present, focus is made on optimization procedures for fault diagnosis in induction motors to obtain a quick assessment at industry level. This chapter discloses an overview of various types of possible faults in induction motors. In addition, the conventional (invasive) and innovative techniques (noninvasive), especially motor current signature analysis (MCSA), techniques for fault detection and diagnosis in induction machines are covered with a focus on future research.

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