Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors

On robust statistical outlier analysis for damage identification

This thesis aims to contribute towards the development of reliable and accurate damage detection monitoring frameworks, applicable for a range of structural health and condition monitoring problems. Central to this purpose, is to be able to detect damage patterns embedded in a system's vibration signal responses sufficiently early. This will enable a condition-based maintenance and inspection to be carried out so as to prevent potentially catastrophic events, as related to each application domain. Firstly, to obviate reliance on data labels, an inclusive outlier analysis study is conducted by means of robust multivariate statistical analysis and a range of other (more common) outlier detection techniques, in both multivariate and time-series settings. Given the parametric nature of robust multivariate statistical techniques, it has also been possible to characterise outliers according to their influence on a method's estimates. Secondly, novelty detection is explored, in which a set of samples representing the nominal state of the system, is assumed to be available. This set includes observations from a system with its dynamics being significantly influenced by environmental and operational variability. Finally, this thesis explored the potential of utilising certain robust techniques as a pre-processing step on damage sensitive features (contaminated with outliers) for novelty detection tasks. Given the large volume of observations, both experimental and computational, different damage sensitive features were extracted, some of which were specific to the range of problems / types of damage being investigated. The performance, in terms of both sensitivity in damage detection and immunity to environmental and operational variability, was assessed for each damage sensitive feature, in combination to the outlier and novelty detection technique used. This thesis has introduced to the condition and structural health monitoring fields a range of methods from robust statistics with attractive properties, such as the effective unmasking of outliers

Optimización de procesos industriales con técnicas de Minería de Datos: mantenimiento de aerogeneradores y fabricación con tecnologías láser

En este trabajo se emplean técnicas de Minería de Datos para mejorar la eficiencia de dos procesos industriales: el diagnóstico de fallos en aerogeneradores y la fabricación de piezas metálicas de geometría compleja mediante tecnologías láser. Se mejora la validación experimental de estudios anteriores, en los que no se usó validación cruzada ni se tuvieron en cuenta algunas particularidades de los problemas analizados. Para el diagnóstico de fallos en aerogeneradores, se identifica la técnica de clasificación más adecuada para relacionar medidas de vibraciones con el tipo de fallo. Además, se define la métrica más adecuada para evaluar su precisión. Para la fabricación de piezas metálicas de geometría compleja, se estima la técnica de clasificación más adecuada para predecir la calidad superficial obtenida con pulido superficial láser, así como la técnica de regresión para predecir los errores en los distintos requerimientos geométricos de piezas obtenidas mediante microfresado 3D láser.Ministerio de Economía y Competitividad, proyecto TIN-2011-24046

Induction Motors

AC motors play a major role in modern industrial applications. Squirrel-cage induction motors (SCIMs) are probably the most frequently used when compared to other AC motors because of their low cost, ruggedness, and low maintenance. The material presented in this book is organized into four sections, covering the applications and structural properties of induction motors (IMs), fault detection and diagnostics, control strategies, and the more recently developed topology based on the multiphase (more than three phases) induction motors. This material should be of specific interest to engineers and researchers who are engaged in the modeling, design, and implementation of control algorithms applied to induction motors and, more generally, to readers broadly interested in nonlinear control, health condition monitoring, and fault diagnosis

Advances in Vibration Analysis Research

Vibrations are extremely important in all areas of human activities, for all sciences, technologies and industrial applications. Sometimes these Vibrations are useful but other times they are undesirable. In any case, understanding and analysis of vibrations are crucial. This book reports on the state of the art research and development findings on this very broad matter through 22 original and innovative research studies exhibiting various investigation directions. The present book is a result of contributions of experts from international scientific community working in different aspects of vibration analysis. The text is addressed not only to researchers, but also to professional engineers, students and other experts in a variety of disciplines, both academic and industrial seeking to gain a better understanding of what has been done in the field recently, and what kind of open problems are in this area

Quantitative Risk Analysis using Real-time Data and Change-point Analysis for Data-informed Risk Prediction

Incidents in highly hazardous process industries (HHPI) are a major concern for various stakeholders due to the impact on human lives, environment, and potentially huge financial losses. Because process activities, location and products are unique, risk analysis techniques applied in the HHPI has evolved over the years. Unfortunately, some limitations of the various quantitative risk analysis (QRA) method currently employed means alternative or more improved methods are required. This research has obtained one such method called Big Data QRA Method. This method relies entirely on big data techniques and real-time process data to identify the point at which process risk is imminent and provide the extent of contribution of other components interacting up to the time index of the risk. Unlike the existing QRA methods which are static and based on unvalidated assumptions and data from single case studies, the big data method is dynamic and can be applied to most process systems. This alternative method is my original contribution to science and the practice of risk analysis The detailed procedure which has been provided in Chapter 9 of this thesis applies multiple change-point analysis and other big data techniques like, (a) time series analysis, (b) data exploration and compression techniques, (c) decision tree modelling, (d) linear regression modelling. Since the distributional properties of process data can change over time, the big data approach was found to be more appropriate. Considering the unique conditions, activities and the process systems use within the HHPI, the dust fire and explosion incidents at the Imperial Sugar Factory and the New England Wood Pellet LLC both of which occurred in the USA were found to be suitable case histories to use as a guide for evaluation of data in this research. Data analysis was performed using open source software packages in R Studio. Based on the investigation, multiple-change-point analysis packages strucchange and changepoint were found to be successful at detecting early signs of deteriorating conditions of component in process equipment and the main process risk. One such process component is a bearing which was suspected as the source of ignition which led to the dust fire and explosion at the Imperial Sugar Factory. As a result, this this research applies the big data QRA method procedure to bearing vibration data to predict early deterioration of bearings and final period when the bearing’s performance begins the final phase of deterioration to failure. Model-based identification of these periods provides an indication of whether the conditions of a mechanical part in process equipment at a particular moment represent an unacceptable risk. The procedure starts with selection of process operation data based on the findings of an incident investigation report on the case history of a known process incident. As the defining components of risk, both the frequency and consequences associated with the risk were obtained from the incident investigation reports. Acceptance criteria for the risk can be applied to the periods between the risks detected by the two change-point packages. The method was validated with two case study datasets to demonstrate its applicability as procedure for QRA. The procedure was then tested with two other case study datasets as examples of its application as a QRA method. The insight obtained from the validation and the applied examples led to the conclusion that big data techniques can be applied to real-time process data for risk assessment in the HHPI

Mining Technologies Innovative Development

The present book covers the main challenges, important for future prospects of subsoils extraction as a public effective and profitable business, as well as technologically advanced industry. In the near future, the mining industry must overcome the problems of structural changes in raw materials demand and raise the productivity up to the level of high-tech industries to maintain the profits. This means the formation of a comprehensive and integral response to such challenges as the need for innovative modernization of mining equipment and an increase in its reliability, the widespread introduction of Industry 4.0 technologies in the activities of mining enterprises, the transition to "green mining" and the improvement of labor safety and avoidance of man-made accidents. The answer to these challenges is impossible without involving a wide range of scientific community in the publication of research results and exchange of views and ideas. To solve the problem, this book combines the works of researchers from the world's leading centers of mining science on the development of mining machines and mechanical systems, surface and underground geotechnology, mineral processing, digital systems in mining, mine ventilation and labor protection, and geo-ecology. A special place among them is given to post-mining technologies research

Use of rate of change of torque to detect damage in gear systems

With the improvement and expansion of technology, systems are becoming more expensive to operate and maintain. In an attempt to reduce these costs structural performance and condition monitoring systems have seen a significant boost in interest as companies try to reduce unplanned down time and extend component life. Whilst vibration monitoring and acoustic emission are the favoured monitoring methods at the moment, they have significant flaws when it comes to their application to rotating machinery. As an alternative monitoring technique, this thesis investigates the application of a non-contact magnetic technique for monitoring rate of change of torque to gear systems. Using both existing and newly developed novel test rigs, data was captured at realistic conditions allowing for four studies to be undertaken. These studies evaluate the performance of the rate of change of torque technique (ROC) and compare it to an already established monitoring method. The work showed that fundamentally the ROC technique was capable of successfully detecting very small levels: 2.7μm, of tooth bend damage on test gears under realistic operating conditions. Tests conducted on back-to-back gear testing rigs used sensors positioned both outside the torque loop, measuring ROC in parasitic losses, and inside the torque loop making direct measurements of ROC within the loaded gear pairs. These data have been analysed using a range of metrics and other analysis techniques, largely derived from current practice in vibration analysis. Whilst some techniques have been shown to be more suitable than others, it is clear that further development of ROC-specific analysis methods is required in order to achieve a robust methodology for repeatable and unambiguous detection and characterisation of gear tooth faults based on ROC signals. A further study examined signals generated during tests conducted whilst a bearing was failing. This bearing was positioned on the drive system of the test rig, external to the torque loop. No evidence was found in the in-loop ROC signals from this bearing failure. This was due to the relative power levels in and out of the torque loop, with the much higher amplitude ROC signals generated by the gear meshes swamping any indications of bearing failure. However, in a realistic application (i.e. a bearing in a torque loop) the work in this thesis, demonstrating the high sensitivity of the ROC technique, gives confidence that it is suitable for damage detection in bearings as well as gears. The final study compared ROC and vibration signals captured simultaneously during a set of gear tests. Whilst both techniques were found to be capable of detecting larger scale tooth bend defects, ROC was found to be more consistent in general, and more capable of detecting the 2.7 μm bend tested. The thesis concludes that ROC has the potential to be a highly sensitive and reliable condition monitoring technique for rotating machinery. Due to its novelty, further development is required of enhanced techniques for signal processing and classification

Thermal Stress Based Model Predictive Control of Power Electronic Converters in Electric Drives Applications

Power electronics is used increasingly in a wide range of application fields such as variable speed drives, electric vehicles and renewable energy systems. It has become a crucial component for the further development of emerging application fields such as lighting, more-electric aircrafts and medical systems. The reliable operation over the designed lifetime is essential for any power electronic system, particularly because the reliability of power electronics is becoming a prerequisite for the system safety in several key areas like energy, medicine and transportation. The thermal stress of power electronic components is one of the most important causes of their failure. Proper thermal management plays an important role for more reliable and cost effective energy conversion. As one of the most vulnerable and expensive components, power semiconductors, are the focus of this thesis. Active thermal control is a possibility to control the junction temperatures of power semiconductors in order to reduce the thermal stress. For this purpose the finite control-set model predictive control (FCS-MPC) is chosen. In FCS-MPC the switching vector is selected using a multi-parameter optimization that can include non-linear electric and thermal stress related models. This switching vector is directly applied to the physical system. This allows the direct control of the switching-state and the current through each semiconductor at each time instant. For cost-effective control of the thermal stress a measure for the degradation of the semiconductor's lifetime is necessary. Existing lifetime models in literature are based on the thermal cycling amplitudes and maximum values of recorded junction temperature profiles. For online estimation of the degradation, a method to detect the junction temperatures of the semiconductors during operation is designed and validated. An existing and proven lifetime model is adapted for online estimation of the thermal stress. An algorithm for the FCS-MPC is written that utilizes this model to drive the inverter with reduced stress and equalize the degradation of the semiconductors in a power module. The algorithm is demonstrated in simulation and validated in experiment. A technique to find the optimal trade-off between reduction of the thermal stress and allowing additional losses in the system is given. The effect of rotor flux variation of the machine on the junction temperatures of the driving inverter is investigated. It can be used as another parameter to control the junction temperature. This allows increasing the maximal thermal cycling amplitude that can be compensated by an active thermal controller. A suitable controller is proposed and validated in experiment. The integration of this technique into the FCS-MPC is presented

Modeling and Simulation in Engineering

The Special Issue Modeling and Simulation in Engineering, belonging to the section Engineering Mathematics of the Journal Mathematics, publishes original research papers dealing with advanced simulation and modeling techniques. The present book, “Modeling and Simulation in Engineering I, 2022”, contains 14 papers accepted after peer review by recognized specialists in the field. The papers address different topics occurring in engineering, such as ferrofluid transport in magnetic fields, non-fractal signal analysis, fractional derivatives, applications of swarm algorithms and evolutionary algorithms (genetic algorithms), inverse methods for inverse problems, numerical analysis of heat and mass transfer, numerical solutions for fractional differential equations, Kriging modelling, theory of the modelling methodology, and artificial neural networks for fault diagnosis in electric circuits. It is hoped that the papers selected for this issue will attract a significant audience in the scientific community and will further stimulate research involving modelling and simulation in mathematical physics and in engineering