A method to classify steel plate faults based on ensemble learning

With the industrial revolution 4.0, machine learning methods are widely used in all aspects of manufacturing to perform quality prediction, fault diagnosis, or maintenance. In the steel industry, it is important to precisely detect faults/defects in order to produce high-quality steel plates. However, determining the exact first-principal model between process parameters and mechanical properties is a challenging process. In addition, steel plate defects are detected through manual, costly, and less productive offline inspection in the traditional manufacturing process of steel. Therefore, it is a great necessity to enable the automatic detection of steel plate faults. To this end, this study explores the capabilities of the following three machine learning models Adaboost, Bagging, and Random Forest in detecting steel plate faults. The well-known steel plate failure dataset provided by Communication Sciences Research Centre Semeion was used in this study. The aim of many studies using this dataset is to correctly classify defects in steel plates using traditional machine learning models, ignoring the applicability of the developed models to real-world problems. Manufacturing is a dynamic process with constant adjustments and improvements. For this reason, it is necessary to establish a learning process that determines the best model based on the arrival of new information. Contrary to previous studies on the steel plate failure dataset, this article presents a systematic modelling approach that includes the normalization step in the data preparation stage to reduce the effects of outliers, the feature selection step in the dimension reduction stage to develop a machine learning model with fewer inputs, and hyperparameter optimization step in the model development stage to increase the accuracy of the machine learning model. The performances of the developed machine learning models were compared according to statistical metrics in terms of precision, recall, sensitivity, and accuracy. The results revealed that AdaBoost performed well on this dataset, achieving accuracy scores of 93.15% and 91.90% for the training and test datasets, respectively

Structural health monitoring of offshore wind turbines: A review through the Statistical Pattern Recognition Paradigm

Offshore Wind has become the most profitable renewable energy source due to the remarkable development it has experienced in Europe over the last decade. In this paper, a review of Structural Health Monitoring Systems (SHMS) for offshore wind turbines (OWT) has been carried out considering the topic as a Statistical Pattern Recognition problem. Therefore, each one of the stages of this paradigm has been reviewed focusing on OWT application. These stages are: Operational Evaluation; Data Acquisition, Normalization and Cleansing; Feature Extraction and Information Condensation; and Statistical Model Development. It is expected that optimizing each stage, SHMS can contribute to the development of efficient Condition-Based Maintenance Strategies. Optimizing this strategy will help reduce labor costs of OWTs׳ inspection, avoid unnecessary maintenance, identify design weaknesses before failure, improve the availability of power production while preventing wind turbines׳ overloading, therefore, maximizing the investments׳ return. In the forthcoming years, a growing interest in SHM technologies for OWT is expected, enhancing the potential of offshore wind farm deployments further offshore. Increasing efficiency in operational management will contribute towards achieving UK׳s 2020 and 2050 targets, through ultimately reducing the Levelised Cost of Energy (LCOE)

Self-adaptive step fruit fly algorithm optimized support vector regression model for dynamic response prediction of magnetorheological elastomer base isolator

© 2016 Elsevier B.V. Parameter optimization of support vector regression (SVR) plays a challenging role in improving the generalization ability of machine learning. Fruit fly optimization algorithm (FFOA) is a recently developed swarm optimization algorithm for complicated multi-objective optimization problems and is also suitable for optimizing SVR parameters. In this work, parameter optimization in SVR using FFOA is investigated. In view of problems of premature and local optimum in FFOA, an improved FFOA algorithm based on self-adaptive step update strategy (SSFFOA) is presented to obtain the optimal SVR model. Moreover, the proposed method is utilized to characterize magnetorheological elastomer (MRE) base isolator, a typical hysteresis device. In this application, the obtained displacement, velocity and current level are used as SVR inputs while the output is the shear force response of the device. Experimental testing of the isolator with two types of excitations is applied for model performance evaluation. The results demonstrate that the proposed SSFFOA-optimized SVR (SSFFOA_SVR) has perfect generalization ability and more accurate prediction accuracy than other machine learning models, and it is a suitable and effective method to predict the dynamic behaviour of MRE isolator

Machine Learning-Based Analysis of a Wind Turbine Manufacturing Operation: A Case Study

This study analyzes the lead time of the bending operation in the wind turbine tower manufacturing process. Since the operation involves a significant amount of employee interaction and the parts processed are heavy and voluminous, there is considerable variability in the recorded lead times. Therefore, a machine learning regression analysis has been applied to the bending process. Two machine learning algorithms have been used: a multivariate Linear Regression and the M5P method. The goal of the analysis is to gain a better understanding of the effect of several factors (technical, organizational, and experience-related) on the bending process times, and to attempt to predict these operation times as a way to increase the planning and controlling capacity of the plant. The inclusion of the experience-related variables serves as a basis for analyzing the impact of age and experience on the time-wise efficiency of workers. The proposed approach has been applied to the case of a Spanish wind turbine tower manufacturer, using data from the operation of its plant gathered between 2018 and 2021. The results show that the trained models have a moderate predictive power. Additionally, as shown by the output of the regression analysis, there are variables that would presumably have a significant impact on lead times that have been found to be non-factors, as well as some variables that generate an unexpected degree of variability

Master of Science

thesisNondestructive evaluation (NDE) is a means of assessing the reliability and integrity of a structural component and provides such information as the presence, location, extent, and type of damage in the component. Structural health monitoring (SHM) is a subfield of NDE, and focuses on a continuous monitoring of a structure while in use. SHM has been applied to structures such as bridges, buildings, pipelines, and airplanes with the goal of detecting the presence of damage as a means of determining whether a structure is in need of maintenance. SHM can be posed as a modeling problem, where an accurate model allows for a more reliable prediction of structural behavior. More reliable predictions make it easier to determine if something is out of the ordinary with the structure. Structural models can be designed using analytical or empirical approaches. Most SHM applications use purely analytical models based on finite element analysis and fundamental wave propagation equations to construct behavioral predictions. Purely empirical models exist, but are less common. These often utilize pattern recognition algorithms to recognize features that indicate damage. This thesis uses a method related to the k-means algorithm known as dictionary learning to train a wave propagation model from full wavefield data. These data are gathered from thin metal plates that exhibit complex wavefields dominated by multipath interference. We evaluate our model for its ability to detect damage in structures on which the model was not trained. These structures are similar to the training structure, but variable in material type and thickness. This evaluation will demonstrate how well learned dictionaries can both detect damage in a complex wavefield with multipath interference, and how well the learned model generalizes to structures with slight variations in properties. The damage detection and generalization results achieved using this empirical model are compared to similar results using both an analytical model and a support vector machine model