| 11. |
- Gallo, Vincenzo, et al.
(författare)
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Metrological Characterization of a Clip Fastener assembly fault detection system based on Deep Learning
- 2023
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Ingår i: 2023 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). - : IEEE. - 9781665453837
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Konferensbidrag (refereegranskat)abstract
- In a time when Artificial Intelligence (AI) technologies are nearly ubiquitous, railway construction and maintenance systems have not fully grasped the capabilities of such technologies. Traditional railway inspection methods rely on inspection from experienced workers, making such tasks costly from both, the monetary and the time perspective. From an overview of the state-of-the-art research in this area regarding AI-based systems, we observed that their main focus was solely on detection accuracy of different railway components. However, if we consider the critical importance of railway fastening in the overall safety of the railway, there is a need for a thorough analysis of these AI-based methodologies, to define their uncertainty also from a metrological perspective. In this article we address this issue, proposing an image-based system that detects the rotational displacement of the fastened railway clips. Furthermore, we provide an uncertainty analysis of the measurement system, where the resulting uncertainty is of 0.42°, within the 3° error margin defined by the clip manufacturer.
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| 12. |
- Hussain, Mazhar, 1980-, et al.
(författare)
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A Deep Learning Approach for Classification and Measurement of Hazardous Gases Using Multi-Sensor Data Fusion
- 2023
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Ingår i: 2023 IEEE Sensors Applications Symposium (SAS). - : IEEE conference proceedings.
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Konferensbidrag (refereegranskat)abstract
- Significant risks to public health and the environment are posed by the release of hazardous gases from industries such as pulp and paper. In this study, the aim was to develop a multi-sensor system with a minimal number of sensors to detect and identify hazardous gases. Training and test data for two gases, hydrogen sulfide and methyl mercaptan, which are known to contribute significantly to odors, were generated in a controlled laboratory environment. The performance of two deep learning models, a 1d-CNN and a stacked LSTM, for data fusion with different sensor configurations was evaluated. The performance of these models was compared with a baseline machine learning model. It was observed that the baseline model was outperformed by the deep learning models and achieved good accuracy with a four-sensor configuration. The potential of a cost-effective multi-sensor system and deep learning models in detecting and identifying hazardous gases is demonstrated by this study, which can be used to collect data from multiple locations and help guide the development of in-situ measurement systems for real-time detection and identification of hazardous gases at industrial sites. The proposed system has important implications for reducing pollution and protecting public health.
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| 13. |
- Hussain, Mazhar, 1980-, et al.
(författare)
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A Study on the Correlation between Change in the Geometrical Dimension of a Free-Falling Molten Glass Gob and Its Viscosity
- 2022
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Ingår i: Sensors. - : MDPI. - 1424-8220. ; 22:2, s. 661-661
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- To produce flawless glass containers, continuous monitoring of the glass gob is required. It is essential to ensure production of molten glass gobs with the right shape, temperature, viscosity and weight. At present, manual monitoring is common practice in the glass container industry, which heavily depends on previous experience, operator knowledge and trial and error. This results in inconsistent measurements and consequently loss of production. In this article, a multi-camera based setup is used as a non-invasive real-time monitoring system. We have shown that under certain conditions, such as keeping the glass composition constant, it is possible to do in-line measurement of viscosity using sensor fusion to correlate the rate of geometrical change in the gob and its temperature. The correlation models presented in this article show that there is a strong correlation, i.e., 0.65, between our measurements and the projected viscosity.
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| 14. |
- Hussain, Mazhar, 1980-, et al.
(författare)
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Multi-Camera Based Setup for Geometrical Measurement of Free-Falling Molten Glass Gob
- 2021
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Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 21:4
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Tidskriftsartikel (refereegranskat)abstract
- High temperatures complicate the direct measurements needed for continuous characterization of the properties of molten materials such as glass. However, the assumption that geometrical changes when the molten material is in free-fall can be correlated with material characteristics such as viscosity opens the door to a highly accurate contactless method characterizing small dynamic changes. This paper proposes multi-camera setup to achieve accuracy close to the segmentation error associated with the resolution of the images. The experimental setup presented shows that the geometrical parameters can be characterized dynamically through the whole free-fall process at a frame rate of 600 frames per second. The results achieved show the proposed multi-camera setup is suitable for estimating the length of free-falling molten objects.
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| 15. |
- Hussain, Mazhar, 1980-, et al.
(författare)
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Selection of optimal parameters to predict fuel consumption of city buses using data fusion
- 2022
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Ingår i: 2022 IEEE Sensors Applications Symposium (SAS). - : IEEE. - 9781665409810
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Konferensbidrag (refereegranskat)abstract
- The study aims to explore the fuel consumption of city buses with data fusion using a dataset with multiple parameters such as travelled distance, weekday, hour of the day, drivers, buses, and routes, that influence the trip fuel consumption. In this study, manipulated parameters such as modified driver, bus and route identification numbers are used together with original parameters to identify the optimal combination of parameters that can be used to enhance the accuracy of the prediction model. Two regression methods, i.e. cubic SVM and artificial neural networks (ANN), are used to demonstrate the performance of the proposed approach. Results shows that a combination of original parameters and processed parameters increases the performance.
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| 16. |
- Lundström, Adam, et al.
(författare)
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An interactive threshold-setting procedure for improved multivariate anomaly detection in time series
- 2023
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Ingår i: IEEE Access. - : IEEE. - 2169-3536. ; 11, s. 93898-93907
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Tidskriftsartikel (refereegranskat)abstract
- Anomaly detection in multivariate time series is valuable for many applications. In this context, unsupervised and semi-supervised deep learning methods that estimate how normal a new observation is have shown promising results on benchmark datasets. These methods are dependent on a threshold that determines which points should be regarded as anomalous and not be anomalous. However, finding the optimal threshold is not easy since no information about the ground truth is known in advance, which implies that there are limitations to automatic threshold-setting methods available today. An alternative is to utilize the expertise of users that can interact in a threshold-setting procedure, but for this to be practically feasible, the method needs to be both accurate and efficient in relation to the state-of-the-art automatic methods. Therefore, this study develops an interactive threshold-setting schema and examines to what extent it can outperform the current state-of-the-art automatic threshold-setting methods. The result of the study strongly indicates that the suggested method with little effort can provide higher accuracy than the automatic threshold-setting methods on a general basis.
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| 17. |
- Lundström, Adam, et al.
(författare)
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Factory-Based Vibration Data for Bearing-Fault Detection
- 2023
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Ingår i: DATA. - : MDPI. - 2306-5729. ; 8:7
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Tidskriftsartikel (refereegranskat)abstract
- The importance of preventing failures in bearings has led to a large amount of research being conducted to find methods for fault diagnostics and prognostics. Many of these solutions, such as deep learning methods, require a significant amount of data to perform well. This is a reason why publicly available data are important, and there currently exist several open datasets that contain different conditions and faults. However, one challenge is that almost all of these data come from a laboratory setting, where conditions might differ from those found in an industrial environment where the methods are intended to be used. This also means that there may be characteristics of the industrial data that are important to take into account. Therefore, this study describes a completely new dataset for bearing faults from a pulp mill. The analysis of the data shows that the faults vary significantly in terms of fault development, rotation speed, and the amplitude of the vibration signal. It also suggests that methods built for this environment need to consider that no historical examples of faults in the target domain exist and that external events can occur that are not related to any condition of the bearing.
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| 18. |
- Lundström, Adam, et al.
(författare)
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Improving deep learning based anomaly detection on multivariate time series through separated anomaly scoring
- 2022
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Ingår i: IEEE Access. - 2169-3536. ; 10, s. 108194-108204
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Tidskriftsartikel (refereegranskat)abstract
- The importance of anomaly detection in multivariate time series has led to the development of several prominent deep learning solutions. As a part of the anomaly detection method, the scoring method has shown to be of significant importance when separating non-anomalous points from anomalous ones. At this time, most of the solutions utilize an aggregated score which means that relevant information created by the anomaly detection model might be lost. Therefore, this study has set out to examine to what extent anomaly detection in multivariate time series based on deep learning can be improved if all the residuals from each individual channel is considered in the anomaly score. To achieve this, an aggregated and separated scoring method has been applied with a simple denoising convulutional autoencoder (DCAE). In addition, the performance has been compared with other state-of-the-art methods. The result showed that the separated approach has the potential to generate a significantly higher performance than the aggregated one. At the same time, there were some indications suggesting that an aggregated scoring is better at generalizing when no labels to base the anomaly thresholds on, are available. Therefore, the result should serve as an encouragement to use a separated scoring approach together with a small sample of labeled anomalies to optimise the thresholds. Lastly, due to the impact of the anomaly score, the result suggests that future research within this field should consider applying the same anomaly scoring method when comparing the performance of deep learning algorithms.
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| 19. |
- Mahmood, Aamir, 1980-, et al.
(författare)
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Remote-Timber : An Outlook for Teleoperated Forestry With First 5G Measurements
- 2023
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Ingår i: IEEE Industrial Electronics Magazine. - : IEEE. - 1932-4529 .- 1941-0115. ; 17:3, s. 42-53
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Tidskriftsartikel (refereegranskat)abstract
- Across all industries, digitalization and automation are on the rise under the Industry 4.0 vision, and the forest industry is no exception. The forest industry depends on distributed flows of raw materials to the industry through various phases, wherein the typical workflow of timber loading and offloading is finding traction in using automation and 5G wireless networking technologies to enhance efficiency and reduce cost. This article presents one such ongoing effort in Sweden, Remote-Timber—demonstrating a 5G-connected teleoperation use-case within a workflow of timber terminal—and disseminates its business attractiveness as well as first measurement results on network performance. Also, it outlines the future needs of the 5G network design/optimization from teleoperation perspective. Overall, the motivation of this article is to disseminate our early-stage findings and reflections to the industrial and academic communities for furthering the research and development activities in enhancing 5G networks for verticals.
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| 20. |
- Nie, Yali, et al.
(författare)
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A Deep CNN Transformer Hybrid Model for Skin Lesion Classification of Dermoscopic Images Using Focal Loss
- 2023
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Ingår i: Diagnostics. - : MDPI AG. - 2075-4418. ; 13:1
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Tidskriftsartikel (refereegranskat)abstract
- Skin cancers are the most cancers diagnosed worldwide, with an estimated > 1.5 million new cases in 2020. Use of computer-aided diagnosis (CAD) systems for early detection and classification of skin lesions helps reduce skin cancer mortality rates. Inspired by the success of the transformer network in natural language processing (NLP) and the deep convolutional neural network (DCNN) in computer vision, we propose an end-to-end CNN transformer hybrid model with a focal loss (FL) function to classify skin lesion images. First, the CNN extracts low-level, local feature maps from the dermoscopic images. In the second stage, the vision transformer (ViT) globally models these features, then extracts abstract and high-level semantic information, and finally sends this to the multi-layer perceptron (MLP) head for classification. Based on an evaluation of three different loss functions, the FL-based algorithm is aimed to improve the extreme class imbalance that exists in the International Skin Imaging Collaboration (ISIC) 2018 dataset. The experimental analysis demonstrates that impressive results of skin lesion classification are achieved by employing the hybrid model and FL strategy, which shows significantly high performance and outperforms the existing work.
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