Embedded Files
Soft-sensor development for product quality estimation with time delay and feature selection in industrial MDI production
Soft-sensor development for product quality estimation with time delay and feature selection in industrial MDI production
Methylenediphenyl diisocyanate (MDI) is an aromatic isocyanate produced in the highest quantities globally and serves as the raw material for numerous polyurethane products. The reaction system of MDI is intricate, characterized by multiple reactions, side reactions, and by-products with variations in quantity and quality, which pose challenges for analytical identification and monitoring. As such, presently, there exists no kinetic model in the scientific literature with adequate precision to accurately describe the synthesis of MDI or predict its coloration. Consequently, our aim is to develop soft sensors leveraging real industrial data to estimate the coloration of MDI mixtures in an explainable and interpretable manner.
In the course of our study, we employed five distinct feature selection techniques: MRMR, F-test, RReliefF, correlation-based methods, and their combined results, to derive an optimal feature set. Correlation-based techniques were utilized for each operational parameter to determine and incorporate the optimal time delays, which significantly influenced the model accuracy. We evaluated the performance of five different machine learning models, incorporating Bayesian hyperparameter optimization where applicable, namely Linear Regression, Regression Tree, Neural Network, Support Vector Machine Learning, and Gaussian Process Regression, among which the Gaussian models exhibited superior performance. To clarify the results of the Gaussian model, Partial Dependence Plots were generated, displayed and evaluated in an explainable way based on industrial experience and knowledge. Ultimately, a sensitivity analysis was conducted to evaluate the robustness of the optimal solution and to assess the responsiveness of the objective function to variations in each operational parameter.
Data Reconciliation-Based Hierarchical Fusion of Machine Learning Models
Data Reconciliation-Based Hierarchical Fusion of Machine Learning Models
In the context of hierarchical system modeling, ensuring constraints between different hierarchy levels are met, so, for instance, ensuring the aggregation constraints are satisfied, is essential. However, modelling and forecasting each element of the hierarchy independently introduce errors. To mitigate this balance error, it is recommended to employ an optimal data reconciliation technique with an emphasis on measurement and modeling errors. In this study, three different machine learning methods for development were investigated. The first method involves no data reconciliation, relying solely on machine learning models built independently at each hierarchical level. The second approach incorporates measurement errors by adjusting the measured data to satisfy each constraint, and the machine learning model is developed based on this dataset. The third method is based on directly fine-tuning the machine learning predictions based on the prediction errors of each model. The three methods were compared using three case studies with different complexities, namely mineral composition estimation with 9 elements, forecasting of retail sales with 14 elements, and waste deposition forecasting with more than 3000 elements. From the results of this study, the conclusion can be drawn that the third method performs the best, and reliable machine learning models can be developed.
Machine learning-based software sensors for machine state monitoring-The role of SMOTE-based data augmentation
Machine learning-based software sensors for machine state monitoring-The role of SMOTE-based data augmentation
A method for flexible vibration sensor-based retrofitting of CNC machines is proposed. As different states leave different fingerprints in the power spectrum plane, the states of the machine can be distinguished based on the features extracted from the spectrum map. Due to some states, like tool replacement, are less frequent than others, like production state, monitoring the machine states is considered an imbalanced classification problem. The key idea is to use Borderline-Synthetic Minority Oversampling Technique (Borderline-SMOTE) to augment the data set. The concept is validated in an industrial case study. Soft sensors based on four machine learning algorithms with and without SMOTE to predict the states of the machine were implemented. The results show that the SMOTE-based data augmentation improved the performance of the models by 50%.
Machine learning-based soft-sensor development for road quality classification
Machine learning-based soft-sensor development for road quality classification
Vibrations in road vehicles cause several harmful effects, health problems can occur for the passengers, and mechanical damage can occur to the vehicle components. Given the health, safety, and financial issues that arise, keeping the road network in good condition and detecting road defects as early as possible requires an extensive monitoring system. Related to this, our study presents the development of hardware and software for a low-cost, multi-sensor road quality monitoring system for passenger vehicles. The developed monitoring system can classify road sections according to their quality parameters into four classes. In order to detect vibrations in the vehicle, accelerometers and gyroscope sensors are installed at several points. Then, a machine learning-based soft-sensor development is introduced. Besides noise filtering, each data point is resampled by spatial frequency to reduce the velocity dependence. Subsequently, a decision tree-based classification model is trained using features from the power spectrum and principal component analysis. The classification algorithm is validated and tested with measurement data in a real-world environment. In addition to reviewing the accuracy of the model, we examine the correlation of the data measured in the cabin and on the suspension to see how much additional information is provided by the sensor on the axle.
Edge-computing and machine-learning-based framework for software sensor development
Edge-computing and machine-learning-based framework for software sensor development
The present research presents a framework that supports the development and operation of machine-learning (ML) algorithms to develop, maintain and manage the whole lifecycle of modeling software sensors related to complex chemical processes. Our motivation is to take advantage of ML and edge computing and offer innovative solutions to the chemical industry for difficult-to-measure laboratory variables. The purpose of software sensor models is to continuously forecast the quality of products to achieve effective quality control, maintain the stable production condition of plants, and support efficient, environmentally friendly, and harmless laboratory work. As a result of the literature review, quite a few ML models have been developed in recent years that support the quality assurance of different types of materials. However, the problems of continuous operation, maintenance and version control of these models have not yet been solved. The method uses ML algorithms and takes advantage of cloud services in an enterprise environment. Industrial 4.0 devices such as the Internet of Things (IoT), edge computing, cloud computing, ML, and artificial intelligence (AI) are core techniques. The article outlines an information system structure and the related methodology based on data from a quality-assurance laboratory. During the development, we encountered several challenges resulting from the continuous development of ML models and the tuning of their parameters. The article discusses the development, version control, validation, lifecycle, and maintenance of ML models and a case study. The developed framework can continuously monitor the performance of the models and increase the amount of data that make up the models. As a result, the most accurate, data-driven and up-to-date models are always available to quality-assurance engineers with this solution.
Page updated
Google Sites
Report abuse