Predictive Maintenance of Norwegian Road Network Using Deep Learning Models

Hassan, Muhammad Umair; Steinnes, Ole-Martin Hagen; Gustafsson, Eirik Gribbestad; Løken, Sivert; Hameed, Ibrahim A.

doi:10.3390/s23062935

Cited by 10 publications

(2 citation statements)

References 45 publications

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“…Optimizing maintenance schedules, increasing equipment lifespan, and enhancing the overall performance of electrical machines are some of the objectives of this study. Maintenance optimization is increasingly being explored using deep learning models [5][6][7][8], which is the focus of the method presented in this paper.…”

Section: Introductionmentioning

confidence: 99%

Machine Fault Detection Using a Hybrid CNN-LSTM Attention-Based Model

Borrè

Seman

Camponogara

et al. 2023

Sensors

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The predictive maintenance of electrical machines is a critical issue for companies, as it can greatly reduce maintenance costs, increase efficiency, and minimize downtime. In this paper, the issue of predicting electrical machine failures by predicting possible anomalies in the data is addressed through time series analysis. The time series data are from a sensor attached to an electrical machine (motor) measuring vibration variations in three axes: X (axial), Y (radial), and Z (radial X). The dataset is used to train a hybrid convolutional neural network with long short-term memory (CNN-LSTM) architecture. By employing quantile regression at the network output, the proposed approach aims to manage the uncertainties present in the data. The application of the hybrid CNN-LSTM attention-based model, combined with the use of quantile regression to capture uncertainties, yielded superior results compared to traditional reference models. These results can benefit companies by optimizing their maintenance schedules and improving the overall performance of their electric machines.

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Section: Introductionmentioning

confidence: 99%

Machine Fault Detection Using a Hybrid CNN-LSTM Attention-Based Model

Borrè

Seman

Camponogara

et al. 2023

Sensors

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“…With the development of preventive maintenance techniques [11,12], scholars have undertaken more in-depth research on the classification of maintenance sections, and the importance of asphalt pavement distress characteristics in section classification has increased [13][14][15]. The development of more efficient classification algorithms has become a major research hotspot, aiming to help fully exploit the differentiated information for asphalt pavement sections [16][17][18].…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Clustering of Asphalt Pavement Maintenance Sections Based on 3D Ground-Penetrating Radar and Principal Component Techniques

Liu¹,

Zheng²,

et al. 2023

Buildings

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Asphalt pavement maintenance section classification is an important prerequisite for accurately determining asphalt pavement maintenance needs and formulating accurate maintenance plans. This paper introduces the three-dimensional (3D) ground-penetrating radar (GPR) pavement internal crack rate index on the basis of an original road surface performance data matrix, and the dimensionality of the road section classification data matrix was reduced through the principal component technique. An analysis of variance was used to compare the significance of the differences in the results for road section classification using different clustering methods and different clustering data and to investigate the influence of the clustering method, principal component technique and crack rate index on the maintenance road section classification results. The results showed that the principal component technique could reduce the dimensionality of the data matrix by 33% and retain more than 84% of the information. There was a genetic relationship between the clustering data and the technical characteristics of the classified sub-sections, and the internal crack rate was important for the characterisation of internal defects in asphalt pavement sub-sections and the determination of maintenance needs. The results of section classification varied considerably between clustering methods, and the choice of clustering method had a relationship to the pavement maintenance objectives. The dynamic clustering method combined with principal component analysis could significantly improve the significance of the differences in the clustering results, effectively improving the division of maintenance sections.

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Research on the Application of Machine Learning in Predictive Maintenance of Building Structures

Jin

2024

Learning and Analytics in Intelligent Systems

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Predictive Maintenance of Norwegian Road Network Using Deep Learning Models

Cited by 10 publications

References 45 publications

Machine Fault Detection Using a Hybrid CNN-LSTM Attention-Based Model

Machine Fault Detection Using a Hybrid CNN-LSTM Attention-Based Model

Clustering of Asphalt Pavement Maintenance Sections Based on 3D Ground-Penetrating Radar and Principal Component Techniques

Research on the Application of Machine Learning in Predictive Maintenance of Building Structures

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