Advancements in condition monitoring and fault diagnosis of rotating machinery: A comprehensive review of image-based intelligent techniques for induction motors

Recently, condition monitoring (CM) and fault detection and diagnosis (FDD) techniques for rotating machinery (RM) have witnessed substantial advancements in recent decades, driven by the increasing demand for enhanced reliability, efficiency, and safety in industrial operations. CM of valuable and high-cost machinery is crucial for performance tracking, reducing maintenance costs, enhancing efficiency and reliability, and minimizing mechanical failures. While various FDD methods for RM have been developed, these predominantly focus on signal processing diagnostics techniques encompassing time, frequency, and time-frequency domains, intelligent diagnostics, image processing, data fusion, data mining, and expert systems. However, there is a noticeable knowledge gap regarding the specific review of image-based CM and FDD. The objective of this research is to address the aforementioned gap in the literature by conducting a comprehensive review of image-based intelligent techniques for CM and fault FDD specifically applied to induction motors (IMs). The focus of the study is to explore the utilization of image-based methods in the context of IMs, providing a thorough examination of the existing literature, methodologies, and applications. Furthermore, the integration of image-based techniques in CM and FDD holds promise for enhanced accuracy, as visual information can provide valuable insights into the physical condition and structural integrity of the IMs, thereby facilitating early FDD and proactive maintenance strategies. The review encompasses the three main faults associated with IMs, namely bearing faults, stator faults, and rotor faults. Furthermore, a thorough assessment is conducted to analyze the benefits and drawbacks associated with each approach, thereby enabling an evaluation of the efficacy of image-based intelligent techniques in the context of CM and FDD. Finally, the paper concludes by highlighting key issues and suggesting potential avenues for future research

Enhancement of Condition Monitoring Information from the Control Data of Electrical Motors Based on Machine Learning Techniques

Centrifugal pumps are widely used in many manufacturing processes, including power plants, petrochemical industries, and water supplies. Failures in centrifugal pumps not only cause significant production interruptions but can be responsible for a large proportion of the maintenance budget. Early detection of such problems would provide timely information to take appropriate preventive actions. Currently, the motor current signature analysis (MCSA) is regarded to be a promising cost-effective condition monitoring technique for centrifugal pumps. However, conventional data analysis methods such as statistical and spectra parameters often fail to detect damage under different operating conditions, which can be attributed to the present, limited understandings of the fluctuations in current signals arising from the many different possible faults. These include the fluctuations due to changes in operating pressure and flow rate, electromagnetic interference, control accuracy and the measured signals themselves. These combine to make it difficult for conventional data analyses methods such as Fourier based analysis to accurately capture the necessary information to achieve high-performance diagnostics. Therefore, this study focuses on the improvement of data analysis through machine learning (ML) paradigms for promoting the performance of centrifugal pump monitoring. Within the paradigms, data characterisation methods such as empirical mode decomposition (EMD) and the intrinsic time-scale decomposition (ITD) reveal features based purely on the data, rather than finding pre-specified similarities to basic functions. With this data-driven approach, subtle changes are more likely to be captured and provide more effective and accurate fault detection and diagnosis. This study reports the application of two of the above data-driven approaches, using MCSA for a centrifugal pump operated under normal and abnormal conditions to detect faults seeded into the pump. The research study has shown that the use of the ITD and EMD signatures combined with envelope spectra of the current signals proved to be competent in detecting the presence of the centrifugal pump fault conditions under different flow rates. The successful analysis was able to produce a more accurate analysis of these abnormal conditions compared to conventional analytical methods. The effectiveness of these approaches is mainly due to the inclusion of high-frequency information, which is largely ignored by conventional MCSA. Finally, a comprehensive diagnostic approach is suggested based on the support vector machine (SVM) as a diagnosing method for three seeded centrifugal pump defects (two bearing defects and compound defect outer race fault with impeller blockage) under different flow rates. It is confirmed that this novel data-driven paradigm is effective for pump diagnostics. The proposed method based on a combined ITD and SVM technique for extracting meaningful features and distinguishing between seeded faults is significantly more effective and accurate for fault detection and diagnosis when compared with the results obtained from other means, such as envelope, EMD and discrete wavelet transform (DWT) based features

Maintenance Management of Wind Turbines

“Maintenance Management of Wind Turbines” considers the main concepts and the state-of-the-art, as well as advances and case studies on this topic. Maintenance is a critical variable in industry in order to reach competitiveness. It is the most important variable, together with operations, in the wind energy industry. Therefore, the correct management of corrective, predictive and preventive politics in any wind turbine is required. The content also considers original research works that focus on content that is complementary to other sub-disciplines, such as economics, finance, marketing, decision and risk analysis, engineering, etc., in the maintenance management of wind turbines. This book focuses on real case studies. These case studies concern topics such as failure detection and diagnosis, fault trees and subdisciplines (e.g., FMECA, FMEA, etc.) Most of them link these topics with financial, schedule, resources, downtimes, etc., in order to increase productivity, profitability, maintainability, reliability, safety, availability, and reduce costs and downtime, etc., in a wind turbine. Advances in mathematics, models, computational techniques, dynamic analysis, etc., are employed in analytics in maintenance management in this book. Finally, the book considers computational techniques, dynamic analysis, probabilistic methods, and mathematical optimization techniques that are expertly blended to support the analysis of multi-criteria decision-making problems with defined constraints and requirements