Experimental investigation of unbalance and misalignment in rotor bearing system using order analysis

Unbalance and misalignment are the main causes of vibration in rotating machinery. Vibration analysis is the important tool for fault diagnosis in rotating machinery. In this paper, order analysis technique of vibration analysis for unbalance and misalignment fault diagnosis is proposed. In order analysis, both phase and amplitude are obtained. From phase and amplitude, the fault type and location are usually identified. Experimental results show order analysis is an effective technique for fault diagnosis

A Machine Learning Approach

Mutemi, A., Bação, F. (2023). The Discriminants of Long and Short Duration Failures in Fulfillment Sortation Equipment: A Machine Learning Approach. Journal of Engineering, 2023. https://doi.org/10.1155/2023/8557487Due to the difficulties inherent in diagnostics and prognostics, maintaining machine health remains a substantial issue in industrial production. Current approaches rely substantially on human engagement, making them costly and unsustainable, especially in high-volume industrial complexes like fulfillment centers. The length of time that fulfillment center equipment failures last is particularly important because it affects operational costs dramatically. A machine learning approach for identifying long and short equipment failures is presented using historical equipment failure and fault data. Under a variety of hyperparameter configurations, we test and compare the outcomes of eight different machine learning classification algorithms, seven individual classifiers, and a stacked ensemble. The gradient boosting classifier (GBC) produces state-of-the-art results in this setting, with precision of 0.76, recall of 0.82, and false positive rate (FPR) of 0.002. This model has since been applied successfully to automate the detection of long- and short-term defects, which has improved equipment maintenance schedules and personnel allocation towards fulfillment operations. Since its launch, this system has contributed to saving over $500 million in fulfillment expenses. It has also resulted in a better understanding of the flaws that cause long-term failures, which is now being used to build more sophisticated failure prediction and risk-mitigation systems for fulfillment equipment.publishersversionpublishe

Recent advances in intelligent-based structural health monitoring of civil structures

This survey paper deals with the structural health monitoring systems on the basis of methodologies involving intelligent techniques. The intelligent techniques are the most popular tools for damage identification in terms of high accuracy, reliable nature and the involvement of low cost. In this critical survey, a thorough analysis of various intelligent techniques is carried out considering the cases involved in civil structures. The importance and utilization of various intelligent tools to be mention as the concept of fuzzy logic, the technique of genetic algorithm, the methodology of neural network techniques, as well as the approaches of hybrid methods for the monitoring of the structural health of civil structures are illustrated in a sequential manner

Aplicacion de modelos matematicos para el mantenimiento predictivo

149 p.La presente memoria de tesis presenta una revisión sobre la actividad de investigación aplicada que se ha realizado mediante varios proyectos relacionados con el mantenimiento predictivo asociado a procesos industriales. Uno de los resultados principales es la realización de una herramienta web que permite al operador consultar el tiempo estimado hasta el fallo en un proceso de mecanizado y junto a ello un histórico de datos del sistema. Se han obtenido otros resultados que generan una evolución en el mantenimiento de los sistemas estudiados, lo que reduce el coste y aumenta la productividad de estos. Para ello se han aplicado metodologías híbridas donde el objetivo principal radicaba en la creación de una metodología de mantenimiento predictivo para cada uno de los procesos y en algún caso la posibilidad de generalización de esta a procesos similares

Aplicacion de modelos matematicos para el mantenimiento predictivo

149 p.La presente memoria de tesis presenta una revisión sobre la actividad de investigación aplicada que se ha realizado mediante varios proyectos relacionados con el mantenimiento predictivo asociado a procesos industriales. Uno de los resultados principales es la realización de una herramienta web que permite al operador consultar el tiempo estimado hasta el fallo en un proceso de mecanizado y junto a ello un histórico de datos del sistema. Se han obtenido otros resultados que generan una evolución en el mantenimiento de los sistemas estudiados, lo que reduce el coste y aumenta la productividad de estos. Para ello se han aplicado metodologías híbridas donde el objetivo principal radicaba en la creación de una metodología de mantenimiento predictivo para cada uno de los procesos y en algún caso la posibilidad de generalización de esta a procesos similares

Advances in Evolutionary Algorithms

With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field

Improving Energy Efficiency through Data-Driven Modeling, Simulation and Optimization

In October 2014, the EU leaders agreed upon three key targets for the year 2030: a reduction by at least 40% in greenhouse gas emissions, savings of at least 27% for renewable energy, and improvements by at least 27% in energy efficiency. The increase in computational power combined with advanced modeling and simulation tools makes it possible to derive new technological solutions that can enhance the energy efficiency of systems and that can reduce the ecological footprint. This book compiles 10 novel research works from a Special Issue that was focused on data-driven approaches, machine learning, or artificial intelligence for the modeling, simulation, and optimization of energy systems

Evolutionary Neuro-Computing Approaches to System Identification

System models are essentially required for analysis, controller design and future prediction. System identification is concerned with developing models of physical system. Although linear system identification got enriched with several useful classical methods, nonlinear system identification always remained active area of research due to the reason that most of the real world systems are nonlinear in nature and moreover, having non-unique models. Among the several conventional system identification techniques, the Volterra series, Hammerstein-Wiener and polynomial model identification involve considerable computational complexities. The other techniques based on regression models such as nonlinear autoregressive exogenous (NARX) and nonlinear autoregressive moving average exogenous (NARMAX), also suffer from dfficulty in choosing regressors