Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review

From MDPI via Jisc Publications RouterHistory: accepted 2021-09-14, pub-electronic 2021-09-20Publication status: PublishedModern wind turbines operate in continuously transient conditions, with varying speed, torque, and power based on the stochastic nature of the wind resource. This variability affects not only the operational performance of the wind power system, but can also affect its integrity under service conditions. Condition monitoring continues to play an important role in achieving reliable and economic operation of wind turbines. This paper reviews the current advances in wind turbine condition monitoring, ranging from conventional condition monitoring and signal processing tools to machine-learning-based condition monitoring and usage of big data mining for predictive maintenance. A systematic review is presented of signal-based and data-driven modeling methodologies using intelligent and machine learning approaches, with the view to providing a critical evaluation of the recent developments in this area, and their applications in diagnosis, prognosis, health assessment, and predictive maintenance of wind turbines and farms

Fault Diagnosis Approach of Main Drive Chain in Wind Turbine Based on Data Fusion

This article belongs to the Special Issue Electrification of Smart Cities.Copyright: © 2021 by the authors. The construction and operation of wind turbines have become an important part of the development of smart cities. However, the fault of the main drive chain often causes the outage of wind turbines, which has a serious impact on the normal operation of wind turbines in smart cities. In order to overcome the shortcomings of the commonly used main drive chain fault diagnosis method that only uses a single data source, a fault feature extraction and fault diagnosis approach based on data source fusion is proposed. By fusing two data sources, the supervisory control and data acquisition (SCADA) real-time monitoring system data and the main drive chain vibration monitoring data, the fault features of the main drive chain are jointly extracted, and an intelligent fault diagnosis model for the main drive chain in wind turbine based on data fusion is established. The diagnosis results of actual cases certify that the fault diagnosis model based on the fusion of two data sources is able to locate faults of the main drive chain in the wind turbine accurately and provide solid technical support for the high-efficient operation and maintenance of wind turbines.China Southern Power Grid (Research Program of Digital Grid Research Institute, Grant YTYZW20010)

30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201

Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems

Over the last few decades, the field of fault diagnostics and structural health management has been experiencing rapid developments. The reliability, availability, and safety of engineering systems can be significantly improved by implementing multifaceted strategies of in situ diagnostics and prognostics. With the development of intelligence algorithms, smart sensors, and advanced data collection and modeling techniques, this challenging research area has been receiving ever-increasing attention in both fundamental research and engineering applications. This has been strongly supported by the extensive applications ranging from aerospace, automotive, transport, manufacturing, and processing industries to defense and infrastructure industries

Physicochemical, antioxidant and sensory properties of Mango Sorbet containing L-theanine as a potential functional food product

The non-proteinous amino acid L-theanine (L-THE) is associated with a range of health benefits including improvements in immune function, cardiovascular outcomes and cognition. The aims of this study were to develop a food product (mango sorbet; ms-L-THE) containing physiologically relevant doses of L-THE (0.2/100 g w/w) and determine its antioxidant, physicochemical and sensory properties in comparison to a mango sorbet without L-THE (ms). Total phenolic and flavanol content, and antioxidant analysis (DPPH, FRAP and ABTS) were determined spectrophotometrically. Both products were also evaluated for acceptability and likeability in healthy participants using the 9-point hedonic scale. Any differences that could be caused by the addition of L-THE were examined using the triangle test. Results indicated no significant differences between ms-L-THE and ms in taste of the products (p > 0.05), and the ms-L-THE was well received and accepted as a potential commercial product. Findings of the DPPH assay indicated significant difference between the two products (p < 0.05). In conclusion, we have successfully created a mango sorbet that contains a potentially physiologically relevant concentration of L-THE with antioxidant properties that could be used as a novel method of L-THE delivery to clinical and healthy populations

A Self-Sustainable and Micro-Second Time Synchronized Multi-Node Wireless System for Aerodynamic Monitoring on Wind Turbines

Wind energy generation plays a vital role in transitioning from fossil fuel-based energy sources and in alleviating the impacts of global warming. However, global wind energy coverage still needs to rise, while requiring a significant step up in conversion efficiency: monitoring wind flow and operational parameters of wind turbines is an essential prerequisite for coverage and conversion efficiency optimization. This paper presents a low-power, self-sustainable, and time-synchronised system for aerodynamic and acoustic measurements on operating wind turbines. It includes 40 high-accuracy barometers, 10 microphones, 5 differential pressure sensors, and implements a coarse time synchronisation on top of a Bluetooth Low Energy 5.1 protocol tuned for long-range communications. Moreover, we field-assessed the node capability to collect precise and accurate aerodynamic data with a multi-node setup. Outdoor experimental tests revealed that the system can acquire heterogeneous data with a time synchronisation error below 100 mu s and sustain a data rate of 600 kbps over 400 m with up to 5 sensor nodes, enough to fully instrument a wind turbine. The proposed method does not add any traffic overhead on the Bluetooth Low Energy 5.1 protocol, fully relying only on connection events and withstands transmission discontinuity often present in long range wireless communications

Non-invasive inspections: a review on methods and tools

Non-Invasive Inspection (NII) has become a fundamental tool in modern industrial maintenance strategies. Remote and online inspection features keep operators fully aware of the health of industrial assets whilst saving money, lives, production and the environment. This paper conducted crucial research to identify suitable sensing techniques for machine health diagnosis in an NII manner, mainly to detect machine shaft misalignment and gearbox tooth damage for different types of machines, even those installed in a hostile environment, using literature on several sensing tools and techniques. The researched tools are critically reviewed based on the published literature. However, in the absence of a formal definition of NII in the existing literature, we have categorised NII tools and methods into two distinct categories. Later, we describe the use of these tools as contact-based, such as vibration, alternative current (AC), voltage and flux analysis, and non-contact-based, such as laser, imaging, acoustic, thermographic and radar, under each category in detail. The unaddressed issues and challenges are discussed at the end of the paper. The conclusions suggest that one cannot single out an NII technique or method to perform health diagnostics for every machine efficiently. There are limitations with all of the reviewed tools and methods, but good results possible if the machine operational requirements and maintenance needs are considered. It has been noted that the sensors based on radar principles are particularly effective when monitoring assets, but further comprehensive research is required to explore the full potential of these sensors in the context of the NII of machine health. Hence it was identified that the radar sensing technique has excellent features, although it has not been comprehensively employed in machine health diagnosis

Design of an intelligent embedded system for condition monitoring of an industrial robot

PhD ThesisIndustrial robots have long been used in production systems in order to improve productivity, quality and safety in automated manufacturing processes. There are significant implications for operator safety in the event of a robot malfunction or failure, and an unforeseen robot stoppage, due to different reasons, has the potential to cause an interruption in the entire production line, resulting in economic and production losses. Condition monitoring (CM) is a type of maintenance inspection technique by which an operational asset is monitored and the data obtained is analysed to detect signs of degradation, diagnose the causes of faults and thus reduce maintenance costs. So, the main focus of this research is to design and develop an online, intelligent CM system based on wireless embedded technology to detect and diagnose the most common faults in the transmission systems (gears and bearings) of the industrial robot joints using vibration signal analysis. To this end an old, but operational, PUMA 560 robot was utilized to synthesize a number of different transmission faults in one of the joints (3 - elbow), such as backlash between the gear pair, gear tooth and bearing faults. A two-stage condition monitoring algorithm is proposed for robot health assessment, incorporating fault detection and fault diagnosis. Signal processing techniques play a significant role in building any condition monitoring system, in order to determine fault-symptom relationships, and detect abnormalities in robot health. Fault detection stage is based on time-domain signal analysis and a statistical control chart (SCC) technique. For accurate fault diagnosis in the second stage, a novel implementation of a time-frequency signal analysis technique based on the discrete wavelet transform (DWT) is adopted. In this technique, vibration signals are decomposed into eight levels of wavelet coefficients and statistical features, such as standard deviation, kurtosis and skewness, are obtained at each level and analysed to extract the most salient feature related to faults; the artificial neural network (ANN) is then used for fault classification. A data acquisition system based on National Instruments (NI) software and hardware was initially developed for preliminary robot vibration analysis and feature extraction. The transmission faults induced in the robot can change the captured vibration spectra, and the robot’s natural frequencies were established using experimental modal analysis, and also the fundamental fault frequencies for the gear transmission and bearings were obtained and utilized for preliminary robot condition monitoring. In addition to simulation of different levels of backlash fault, gear tooth and bearing faults which have not been previously investigated in industrial robots, with several levels of ii severity, were successfully simulated and detected in the robot’s joint transmission. The vibration features extracted, which are related to the robot healthy state and different fault types, using the data acquisition system were subsequently used in building the SCC and ANN, which were trained using part of the measured data set that represents the robot operating range. Another set of data, not used within the training stage, was then utilized for validation. The results indicate the successful detection and diagnosis of faults using the key extracted parameters. A wireless embedded system based on the ZigBee communication protocol was designed for the application of the proposed CM algorithm in real-time, using an Arduino DUE as the core of the wireless sensor unit attached on the robot arm. A Texas Instruments digital signal processor (TMS320C6713 DSK board) was used as the base station of the wireless system on which the robot’s fault diagnosis algorithm is run. To implement the two stages of the proposed CM algorithm on the designed embedded system, software based on the C programming language has been developed. To demonstrate the reliability of the designed wireless CM system, experimental validations were performed, and high reliability was shown in the detection and diagnosis of several seeded faults in the robot. Optimistically, the established wireless embedded system could be envisaged for fault detection and diagnostics on any type of rotating machine, with the monitoring system realized using vibration signal analysis. Furthermore, with some modifications to the system’s hardware and software, different CM techniques such as acoustic emission (AE) analysis or motor current signature analysis (MCSA), can be applied.Iraqi government, represented by the Ministry of Higher Education and Scientific Research, the Iraqi Cultural Attaché in London, and the University of Technology in Baghda

On the use of context information for an improved application of data-based algorithms in condition monitoring

xi, 124 p.En el campo de la monitorización de la condición, los algoritmos basados en datos cuentan con un amplio recorrido. Desde el uso de los gráficos de control de calidad que se llevan empleando durante casi un siglo a técnicas de mayor complejidad como las redes neuronales o máquinas de soporte vectorial que se emplean para detección, diagnóstico y estimación de vida remanente de los equipos. Sin embargo, la puesta en producción de los algoritmos de monitorización requiere de un estudio exhaustivo de un factor que es a menudo obviado por otros trabajos de la literatura: el contexto. El contexto, que en este trabajo es considerado como el conjunto de factores que influencian la monitorización de un bien, tiene un gran impacto en la algoritmia de monitorización y su aplicación final. Por este motivo, es el objeto de estudio de esta tesis en la que se han analizado tres casos de uso. Se ha profundizado en sus respectivos contextos, tratando de generalizar a la problemática habitual en la monitorización de maquinaria industrial, y se ha abordado dicha problemática de monitorización de forma que solucionen el contexto en lugar de cada caso de uso. Así, el conocimiento adquirido durante el desarrollo de las soluciones puede ser transferido a otros casos de uso que cuenten con contextos similares