Condition-based maintenance in hydroelectric plants: A systematic literature review

Industrial maintenance has become an essential strategic factor for profit and productivity in industrial systems. In the modern industrial context, condition-based maintenance guides the interventions and repairs according to the machine’s health status, calculated from monitoring variables and using statistical and computational techniques. Although several literature reviews address condition-based maintenance, no study discusses the application of these techniques in the hydroelectric sector, a fundamental source of renewable energy. We conducted a systematic literature review of articles published in the area of condition-based maintenance in the last 10 years. This was followed by quantitative and thematic analyses of the most relevant categories that compose the phases of condition-based maintenance. We identified a research trend in the application of machine learning techniques, both in the diagnosis and the prognosis of the generating unit’s assets, being vibration the most frequently discussed monitoring variable. Finally, there is a vast field to be explored regarding the application of statistical models to estimate the useful life, and hybrid models based on physical models and specialists’ knowledge, of turbine-generators

Modeling and Optimal Operation of Hydraulic, Wind and Photovoltaic Power Generation Systems

The transition to 100% renewable energy in the future is one of the most important ways of achieving "carbon peaking and carbon neutrality" and of reducing the adverse effects of climate change. In this process, the safe, stable and economical operation of renewable energy generation systems, represented by hydro-, wind and solar power, is particularly important, and has naturally become a key concern for researchers and engineers. Therefore, this book focuses on the fundamental and applied research on the modeling, control, monitoring and diagnosis of renewable energy generation systems, especially hydropower energy systems, and aims to provide some theoretical reference for researchers, power generation departments or government agencies

Advanced Sensing, Fault Diagnostics, and Structural Health Management

Advanced sensing, fault diagnosis, and structural health management are important parts of the maintenance strategy of modern industries. With the advancement of science and technology, modern structural and mechanical systems are becoming more and more complex. Due to the continuous nature of operation and utilization, modern systems are heavily susceptible to faults. Hence, the operational reliability and safety of the systems can be greatly enhanced by using the multifaced strategy of designing novel sensing technologies and advanced intelligent algorithms and constructing modern data acquisition systems and structural health monitoring techniques. As a result, this research domain has been receiving a significant amount of attention from researchers in recent years. Furthermore, the research findings have been successfully applied in a wide range of fields such as aerospace, manufacturing, transportation and processes

Failure analysis informing intelligent asset management

With increasing demands on the UK’s power grid it has become increasingly important to reform the methods of asset management used to maintain it. The science of Prognostics and Health Management (PHM) presents interesting possibilities by allowing the online diagnosis of faults in a component and the dynamic trending of its remaining useful life (RUL). Before a PHM system can be developed an extensive failure analysis must be conducted on the asset in question to determine the mechanisms of failure and their associated data precursors that precede them. In order to gain experience in the development of prognostic systems we have conducted a study of commercial power relays, using a data capture regime that revealed precursors to relay failure. We were able to determine important failure precursors for both stuck open failures caused by contact erosion and stuck closed failures caused by material transfer and are in a position to develop a more detailed prognostic system from this base. This research when expanded and applied to a system such as the power grid, presents an opportunity for more efficient asset management when compared to maintenance based upon time to replacement or purely on condition

Fault Management in DC Microgrids:A Review of Challenges, Countermeasures, and Future Research Trends

The significant benefits of DC microgrids have instigated extensive efforts to be an alternative network as compared to conventional AC power networks. Although their deployment is ever-growing, multiple challenges still occurred for the protection of DC microgrids to efficiently design, control, and operate the system for the islanded mode and grid-tied mode. Therefore, there are extensive research activities underway to tackle these issues. The challenge arises from the sudden exponential increase in DC fault current, which must be extinguished in the absence of the naturally occurring zero crossings, potentially leading to sustained arcs. This paper presents cut-age and state-of-the-art issues concerning the fault management of DC microgrids. It provides an account of research in areas related to fault management of DC microgrids, including fault detection, location, identification, isolation, and reconfiguration. In each area, a comprehensive review has been carried out to identify the fault management of DC microgrids. Finally, future trends and challenges regarding fault management in DC-microgrids are also discussed

Sensor Signal and Information Processing II

In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem solving. These algorithms have the capacity to generalize and discover knowledge for themselves and learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topic ranges from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspiring filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensors processing

Fault Detection of Inter-Turn Short-Circuited Stator Windings in Permanent Magnet Synchronous Machines

Vannkraftverk leverer grønn og pålitelig energi til befolkningen i Norge, og bidrar med rundt 88 % av landets årlige strømbehov. Uventede avbrudd og stans for kraftverkene vil resultere i store økonomiske tap, samt at kraftverkene ikke får levert nødvendig kraft til nettet. Med fremveksten av Industri 4.0 benytter industriene nyskapende teknologier som skytjenester, Kunstig Intelligens (KI) og tingenes internett for å forbedre de ulike operasjonene i selskapet. Innen vannkraft-industrien vil KI-baserte systemer bli brukt som grunnlag for prediktive vedlikehold. I dag utføres det meste av vedlikeholdsarbeid i henhold til en planlagt tidsplan, og industrien ser derfor på bruk av maskinlærings-metoder for tidlig feilgjenkjenning i vannkraftverkene. Denne masteroppgaven ser på anvendelsen av maskinlærings-algoritmer for å tidlig forutsi kortslutninger i aramturviklingene i en Permanent Magnet Synkronmaskin (PMSM), ved bruk av trefaset strøm-data. Data A ble samlet inn i et internt laboratorium med en Permanent Magnet Synkrongenerator (PMSG) som hadde en implementert 4.8 % kortslutning i aramturviklingen. Dataen bestod av sunne og defekte datasett med RMSverdier for den trefasede strømmen. Data B ble hentet fra et tidligere arbeid av den samme typen PMSM med en 6.0 % kortslutning i aramturviklingen. Data B bestod av signal-verdier for den trefasede strømmen. Ved bruk av Python ble de to datasettene visuelt inspisert og forbehandlet ved hjelp av ‘Z-score’-metoden for å fjerne avvikende verdier. Denne prosessen hadde imidlertid ingen merkbar effekt på nøyaktigheten til maskinlærings-modellene. Enkel signalbehandling i tidsplanet ble anvendt på strømdataene, men klarte ikke å oppdage kortslutningsfeilen implementert på den andre faseviklingen. Statistiske parameter som gjennomsnitt, standard avvik, skjevhet, kurtose, toppverdifaktor, peak-to-peak, RMS, klaringsfaktor, formfaktor og impulsfaktor ble beregnet for alle tre fasene. En Principal Component Analysis (PCA)- algoritme ble anvendt på datasettene med de statistiske parameterne og reduserte Data A fra 18 parameter til tre Principal Components. Data B ble redusert fra 33 parametere til fire Principal Components. Før dataen kjøres i maskinlørings-modellene, ble feilindikatorer som flagger verdier utenfor den 95. persentilen av gjennomsnittsverdiene til parameterne lagt til i datasettet . Fire overvåkede maskinlærings-modeller – ‘Random Forest’, ‘Decision Trees’, ‘k-NN’ og ‘Naive Bayes’ – ble kjørt for datasettene. Random Forest- og Decision Tree-modellene hadde en tendens til å overtilpasse maskinlærings-prediksjonene på datasettene som inneholdt de statistisk parameterne. Datasettet med PCA-komponentene reduserte overtilpasningen av disse modellene og forbedret nøyaktigheten til Naive Bayes-modellen. Ettersom Naive Bayes-modellen ga varierende resultater og ble ansett som inkonsekvent, samt overtilpasnings-tendensene til Random Forest og Decision Tree, ble k-NN-modellen vurdert som den mest pålitelige av maskinlærings-modellene. De beste feilindikatorene for Data A var kurtose- og skjevhet-indikatorene, mens klaringsfaktor og formfaktor ga best nøyaktighet for Data B. Videre arbeid bør unngå bruk av data som inneholder RMS-verdier, og fokusere på bruk av signalbaserte verdier slik som i Data B. Dataprosessering og feilmerking bør også utføres i frekvensplanet, ettersom en stor svakhet ved avhandlingen er at metodikken kun ble anvendt i tidplanet. Andre ytelsesindikatorer som robusthet bør også brukes for å vurdere ytelsen til maskinlærings-modellene

A review of the use of artificial intelligence methods in infrastructure systems

The artificial intelligence (AI) revolution offers significant opportunities to capitalise on the growth of digitalisation and has the potential to enable the ‘system of systems’ approach required in increasingly complex infrastructure systems. This paper reviews the extent to which research in economic infrastructure sectors has engaged with fields of AI, to investigate the specific AI methods chosen and the purposes to which they have been applied both within and across sectors. Machine learning is found to dominate the research in this field, with methods such as artificial neural networks, support vector machines, and random forests among the most popular. The automated reasoning technique of fuzzy logic has also seen widespread use, due to its ability to incorporate uncertainties in input variables. Across the infrastructure sectors of energy, water and wastewater, transport, and telecommunications, the main purposes to which AI has been applied are network provision, forecasting, routing, maintenance and security, and network quality management. The data-driven nature of AI offers significant flexibility, and work has been conducted across a range of network sizes and at different temporal and geographic scales. However, there remains a lack of integration of planning and policy concerns, such as stakeholder engagement and quantitative feasibility assessment, and the majority of research focuses on a specific type of infrastructure, with an absence of work beyond individual economic sectors. To enable solutions to be implemented into real-world infrastructure systems, research will need to move away from a siloed perspective and adopt a more interdisciplinary perspective that considers the increasing interconnectedness of these systems

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

Proceedings of COMADEM 201

A Systematic Review of Convolutional Neural Network-Based Structural Condition Assessment Techniques

With recent advances in non-contact sensing technology such as cameras, unmanned aerial and ground vehicles, the structural health monitoring (SHM) community has witnessed a prominent growth in deep learning-based condition assessment techniques of structural systems. These deep learning methods rely primarily on convolutional neural networks (CNNs). The CNN networks are trained using a large number of datasets for various types of damage and anomaly detection and post-disaster reconnaissance. The trained networks are then utilized to analyze newer data to detect the type and severity of the damage, enhancing the capabilities of non-contact sensors in developing autonomous SHM systems. In recent years, a broad range of CNN architectures has been developed by researchers to accommodate the extent of lighting and weather conditions, the quality of images, the amount of background and foreground noise, and multiclass damage in the structures. This paper presents a detailed literature review of existing CNN-based techniques in the context of infrastructure monitoring and maintenance. The review is categorized into multiple classes depending on the specific application and development of CNNs applied to data obtained from a wide range of structures. The challenges and limitations of the existing literature are discussed in detail at the end, followed by a brief conclusion on potential future research directions of CNN in structural condition assessment