597 research outputs found

    A Literature Review of Fault Diagnosis Based on Ensemble Learning

    Get PDF
    The accuracy of fault diagnosis is an important indicator to ensure the reliability of key equipment systems. Ensemble learning integrates different weak learning methods to obtain stronger learning and has achieved remarkable results in the field of fault diagnosis. This paper reviews the recent research on ensemble learning from both technical and field application perspectives. The paper summarizes 87 journals in recent web of science and other academic resources, with a total of 209 papers. It summarizes 78 different ensemble learning based fault diagnosis methods, involving 18 public datasets and more than 20 different equipment systems. In detail, the paper summarizes the accuracy rates, fault classification types, fault datasets, used data signals, learners (traditional machine learning or deep learning-based learners), ensemble learning methods (bagging, boosting, stacking and other ensemble models) of these fault diagnosis models. The paper uses accuracy of fault diagnosis as the main evaluation metrics supplemented by generalization and imbalanced data processing ability to evaluate the performance of those ensemble learning methods. The discussion and evaluation of these methods lead to valuable research references in identifying and developing appropriate intelligent fault diagnosis models for various equipment. This paper also discusses and explores the technical challenges, lessons learned from the review and future development directions in the field of ensemble learning based fault diagnosis and intelligent maintenance

    Rule - based Fault Diagnosis Expert System for Wind Turbine

    Full text link

    Artificial intelligence for digital twins in energy systems and turbomachinery: development of machine learning frameworks for design, optimization and maintenance

    Get PDF
    The expression Industry4.0 identifies a new industrial paradigm that includes the development of Cyber-Physical Systems (CPS) and Digital Twins promoting the use of Big-Data, Internet of Things (IoT) and Artificial Intelligence (AI) tools. Digital Twins aims to build a dynamic environment in which, with the help of vertical, horizontal and end-to-end integration among industrial processes, smart technologies can communicate and exchange data to analyze and solve production problems, increase productivity and provide cost, time and energy savings. Specifically in the energy systems field, the introduction of AI technologies can lead to significant improvements in both machine design and optimization and maintenance procedures. Over the past decade, data from engineering processes have grown in scale. In fact, the use of more technologically sophisticated sensors and the increase in available computing power have enabled both experimental measurements and highresolution numerical simulations, making available an enormous amount of data on the performance of energy systems. Therefore, to build a Digital Twin model capable of exploring these unorganized data pools collected from massive and heterogeneous resources, new Artificial Intelligence and Machine Learning strategies need to be developed. In light of the exponential growth in the use of smart technologies in manufacturing processes, this thesis aims at enhancing traditional approaches to the design, analysis, and optimization phases of turbomachinery and energy systems, which today are still predominantly based on empirical procedures or computationally intensive CFD-based optimizations. This improvement is made possible by the implementation of Digital Twins models, which, being based primarily on the use of Machine Learning that exploits performance Big-Data collected from energy systems, are acknowledged as crucial technologies to remain competitive in the dynamic energy production landscape. The introduction of Digital Twin models changes the overall structure of design and maintenance approaches and results in modern support tools that facilitate real-time informed decision making. In addition, the introduction of supervised learning algorithms facilitates the exploration of the design space by providing easy-to-run analytical models, which can also be used as cost functions in multi-objective optimization problems, avoiding the need for time-consuming numerical simulations or experimental campaings. Unsupervised learning methods can be applied, for example, to extract new insights from turbomachinery performance data and improve designers’ understanding of blade-flow interaction. Alternatively, Artificial Intelligence frameworks can be developed for Condition-Based Maintenance, allowing the transition from preventive to predictive maintenance. This thesis can be conceptually divided into two parts. The first reviews the state of the art of Cyber-Physical Systems and Digital Twins, highlighting the crucial role of Artificial Intelligence in supporting informed decision making during the design, optimization, and maintenance phases of energy systems. The second part covers the development of Machine Learning strategies to improve the classical approach to turbomachinery design and maintenance strategies for energy systems by exploiting data from numerical simulations, experimental campaigns, and sensor datasets (SCADA). The different Machine Learning approaches adopted include clustering algorithms, regression algorithms and dimensionality reduction techniques: Autoencoder and Principal Component Analysis. A first work shows the potential of unsupervised learning approaches (clustering algorithms) in exploring a Design of Experiment of 76 numerical simulations for turbomachinery design purposes. The second work takes advantage of a nonsequential experimental dataset, measured on a rotating turbine rig characterized by 48 blades divided into 7 sectors that share the same baseline rotor geometry but have different tip designs, to infer and dissect the causal relationship among different tip geometries and unsteady aero-thermodynamic performance via a novel Machine-Learning procedure based on dimensionality reduction techniques. The last application proposes a new anomaly detection framework for gensets in DH networks, based on SCADA data that exploits and compares the performance of regression algorithms such as XGBoost and Multi-layer Perceptron

    Design of software-oriented technician for vehicle’s fault system prediction using AdaBoost and random forest classifiers

    Get PDF
    Detecting and isolating faults on heavy duty vehicles is very important because it helps maintain high vehicle performance, low emissions, fuel economy, high vehicle safety and ensures repair and service efficiency. These factors are important because they help reduce the overall life cycle cost of a vehicle. The aim of this paper is to deliver a Web application model which aids the professional technician or vehicle user with basic automobile knowledge to access the working condition of the vehicles and detect the fault subsystem in the vehicles. The scope of this system is to visualize the data acquired from vehicle, diagnosis the fault component using trained fault model obtained from improvised Machine Learning (ML) classifiers and generate a report. The visualization page is built with plotly python package and prepared with selected parameter from On-board Diagnosis (OBD) tool data. The Histogram data is pre-processed with techniques such as null value Imputation techniques, Standardization and Balancing methods in order to increase the quality of training and it is trained with Classifiers. Finally, Classifier is tested and the Performance Metrics such as Accuracy, Precision, Re-call and F1 measure which are calculated from the Confusion Matrix. The proposed methodology for fault model prediction uses supervised algorithms such as Random Forest (RF), Ensemble Algorithm like AdaBoost Algorithm which offer reasonable Accuracy and Recall. The Python package joblib is used to save the model weights and reduce the computational time. Google Colabs is used as the python environment as it offers versatile features and PyCharm is utilised for the development of Web application. Hence, the Web application, outcome of this proposed work can, not only serve as the perfect companion to minimize the cost of time and money involved in unnecessary checks done for fault system detection but also aids to quickly detect and isolate the faulty system to avoid the propagation of errors that can lead to more dangerous cases

    Upgrading decision support systems with Cloud-based environments and machine learning

    Get PDF
    Business Intelligence (BI) is a process for analyzing raw data and displaying it in order to make it easier for business users to take the right decision at the right time. Inthe market we can find several BI platforms. One commonly used BI solution is calledMicroStrategy, which allows users to build and display reports.Machine Learning (ML) is a process of using algorithms to search for patterns in data which are used to predict and/or classify other data.In recent years, these two fields have been integrated into one another in order to try to complement the prediction side of BI to enable higher quality results for the client.The consulting company (CC) where I have worked on has several solutions related to Data & Analytics built on top of Micro Strategy. Those solutions were all demonstrable in a server installed on-premises. This server was also utilized to build proofs of concept(PoC) to be used as demos for other potential clients. CC also develops new PoCs for clients from the ground up, with the objective of show casing what is possible to display to the client in order to optimize business management.CC was using a local, out of date server to demo the PoCs to clients, which suffered from stability and reliability issues. To address these issues, the server has been migrated and set up in a cloud based solution using a Microsoft Azure-based Virtual Machine,where it now performs similar functions compared to its previous iteration. This move has made the server more reliable, as well as made developing new solutions easier forthe team and enabled a new kind of service (Analytics as a Service).My work at CC was focused on one main task: Migration of the demo server for CCsolutions (which included PoCs for testing purposes, one of which is a machine learning model to predict wind turbine failures). The migration was successful as previously stated and the prediction models, albeit with mostly negative results, demonstrated successfully the development of large PoCs.Business Intelligence (BI) é um processo para analizar dados não tratados e mostrá-los para ajudar gestores a fazer a decisão correcta no momento certo. No mercado, pode-se encontrar várias plataformas de BI. Uma solução de BI comum chama-se MicroStrategy,que permite com que os utilizadores construam e mostrem relatórios.Machine Learning (ML) é um processo de usar algoritmos para procurar padrões em dados que por sua vez são usados para prever e/ou classificar outros dados.Nos últimos anos, estes campos foram integrados um no outro para tentar complementar o lado predictivo de BI para possibilitar resultados de mais alta qualidade para o cliente.A empresa de consultoria (EC) onde trabalhei tem várias soluções relacionadas com Data e Analytics construídas com base no MicroStrategy. Essas soluções eram todas demonstráveis num servidor instalado no local. Este servidor também era usado para criar provas de conceito (PoC) para serem usadas como demos para outros potenciais clientes.A EC também desenvolve novas PoCs para clientes a partir do zero, com o objectivo de demonstrar ao cliente o que é possível mostrar para optimizar a gestão do negócio.A EC estava a utilizar um servidor local desactualizado para demonstrar os PoCs aos clientes, que tinha problemas de estabilidade e fiabilidade. Para resolver estes problemas,o servidor foi migrado e configurado numa solução baseada na cloud com o uso de uma Máquina Virtual baseada no Microsoft Azure, onde executa funções semelhantes à versão anterior. Esta migração tornou o servidor mais fiável, simplificou o processo de desenvolver novas soluções para a equipa e disponibilizou um novo tipo de serviço (Analytics as a Service).O meu trabalho na EC foi focado numa tarefas principal: Migração do servidor de demonstrações de soluções CC (que inclui PoCs para propósitos de testes, uma das quais é um modelo de aprendizagem de máquina para prever falhas em turbinas eólicas). A migração foi efectuada com sucesso (como mencionado previamente) e os modelos testados,apesar de terem maioritariamente resultados negativos, demonstraram com sucesso que é possível desenvolver PoCs de grande dimensão

    Fault Detection and Diagnosis of Electric Drives Using Intelligent Machine Learning Approaches

    Get PDF
    Electric motor condition monitoring can detect anomalies in the motor performance which have the potential to result in unexpected failure and financial loss. This study examines different fault detection and diagnosis approaches in induction motors and is presented in six chapters. First, an anomaly technique or outlier detection is applied to increase the accuracy of detecting broken rotor bars. It is shown how the proposed method can significantly improve network reliability by using one-class classification technique. Then, ensemble-based anomaly detection is utilized to compare different methods in ensemble learning in detection of broken rotor bars. Finally, a deep neural network is developed to extract significant features to be used as input parameters of the network. Deep autoencoder is then employed to build an advanced model to make predictions of broken rotor bars and bearing faults occurring in induction motors with a high accuracy

    Data-driven performance monitoring, fault detection and dynamic dashboards for offshore wind farms

    Get PDF

    New Transfer Learning Approach Based on a CNN for Fault Diagnosis

    Get PDF
    Induction motors operate in difficult environments in the industry. Monitoring the performance of motors in such circumstances is significant, which can provide a reliable operation system. This paper intends to develop a new model for fault diagnosis based on the knowledge of transfer learning using the ImageNet dataset. The development of this framework provides a novel technique for the diagnosis of single and multiple induction motor faults. A transfer learning model based on a VGG-19 convolutional neural network (CNN) was implemented, which provided a quick and fast training process with higher accuracy. Thermal images with different induction motor conditions were captured with the help of an FLIR camera and applied as inputs to investigate the proposed model. The implementation of this task involved the use of a VGG-19 CNN-based pre-trained network, which provides autonomous features learning based on minimum human intervention. Next, a dense-connected classifier was applied to predict the true class. The experimental results confirmed the robustness and reliability of the developed technique, which was successfully able to classify the induction motor faults, achieving a classification accuracy of 99.8%. The use of a VGG-19 network allowed the attributes to be automatically extracted and associated with the decision-making part. Furthermore, this model was further compared with other applications based on related topics; it successfully proved its superiority and robustness

    Advanced signal processing methods for condition monitoring

    Get PDF
    Condition monitoring of induction motors (IM) among with the predictive maintenance concept are currently among the most promising research topics of manufacturing industry. Production efficiency is an important parameter of every manufacturing plant since it directly influences the final price of products. This research article presents a comprehensive overview of conditional monitoring techniques, along with classification techniques and advanced signal processing techniques. Compared methods are either based on measurement of electrical quantities or nonelectrical quantities that are processed by advanced signal processing techniques. This article briefly compares individual techniques and summarize results achieved by different research teams. Our own testbed is briefly introduced in the discussion section along with plans for future dataset creation. According to the comparison, Wavelet Transform (WT) along with Empirical Mode Decomposition (EMD), Principal Component Analysis (PCA) and Park's Vector Approach (PVA) provides the most interesting results for real deployment and could be used for future experiments.Web of Scienc
    corecore