| 1. |
- Ahmer, Muhammad, et al.
(författare)
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Dataset Concerning the Process Monitoring and Condition Monitoring Data of a Bearing Ring Grinder
- 2022
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Annan publikationabstract
- In the manuscript, we have investigated the effective use of sensors in a bearing ring grinder for failure classification in the condition-based maintenance context. The proposed methodology combines domain knowledge of process monitoring and condition monitoring to successfully achieve failure mode prediction with high accuracy using only a few key sensors. This enables manufacturing equipment to take advantage of advanced data processing and machine learning techniques.The grinding machine is of type SGB55 from Lidköping Machine Tools and is used to produce functional raceway surface of inner rings of type SKF-6210 deep groove ball bearing. Additional sensors like vibration, acoustic emission, force, and temperature sensors are installed to monitor machine condition while producing bearing components under different operating conditions. Data is sampled from sensors as well as the machine's numerical controller during operation. Selected parts are measured for the produced quality.
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| 2. |
- Ahmer, Muhammad, et al.
(författare)
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Failure mode classification for condition-based maintenance in a bearing ring grinding machine
- 2022
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Ingår i: The International Journal of Advanced Manufacturing Technology. - : Springer Nature. - 0268-3768 .- 1433-3015. ; 122, s. 1479-1495
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Tidskriftsartikel (refereegranskat)abstract
- Technical failures in machines are major sources of unplanned downtime in any production and result in reduced efficiency and system reliability. Despite the well-established potential of Machine Learning techniques in condition-based maintenance (CBM), the lack of access to failure data in production machines has limited the development of a holistic approach to address machine-level CBM. This paper presents a practical approach for failure mode prediction using multiple sensors installed in a bearing ring grinder for process control as well as condition monitoring. Bearing rings are produced in a set of 7 experimental runs, including 5 frequently occurring production failures in the critical subsystems. An advanced data acquisition setup, implemented for CBM in the grinder, is used to capture information about each individual grinding cycle. The dataset is pre-processed and segmented into grinding cycle stages before time and frequency domain feature extraction. A sensor ranking algorithm is proposed to optimize feature selection for failure classification and the installation cost. Random forest models, benchmarked as best performing classifiers, are trained in a two-step classification framework. The presence of failure mode is predicted in the first step and the failure mode type is identified in the second step using the same feature set. Defining the feature set in the failure detection step improves the predictor generalization with the classifiers’ performance accuracy of 99%99% on the test dataset. The presented approach demonstrates an efficient failure mode classification by selecting crucial sensors resulting in a cost-effective CBM implementation in a bearing ring grinder.
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| 3. |
- Ahmer, Muhammad
(författare)
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Intelligent fault diagnosis and predictive maintenance for a bearing ring grinder
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Predicting the failure of any structure is a difficult task in a mechanical system. However complicated and difficult the prediction might be, the first step is to know the actual condition of the system. Given the complexity of any machine tool, where a number of subsystems of electro-mechanical structures interact to perform the machining operation, failure diagnostics become more challenging due to the high demand for performance and reliability. In a production environment, this results in maintenance costs that the management always strives to reduce. Condition-based machine maintenance (CBM) is considered to be the maintenance strategy that can lead to failure prediction and reducing the maintenance cost by knowing the actual condition of the asset and planning the maintenance activities in advance.Grinding machines and grinding processes have come a long way since the inception of the centuries old grinding technique. However, we still have a number of challenges to overcome before a completely monitored and controlled machine and process can be claimed. One such challenge is to achieve a machine level CBM and predictive maintenance (PdM) setup which is addressed in this thesis. A CBM implementation framework has been proposed which combines the information sampled from sensors installed for the purpose of the process as well as condition monitoring. Accessing the machine's controller information allows the data to be processed with respect to different machine states and process stages. The successful implementation is achieved through a real-time and synchronized data acquisition setup that allows data from multiple sources to be acquired, stored, and consolidated. The dataset thus generated is used in a significant part of this project and is also published in Swedish National Data Service (SND).The thesis also presents the failure diagnostic model based on two step classification approach using benchmarked random forest models. The binary classifier predicts if there is a fault present in the machine based on crucial sensors data from the Idle segment of the grinding cycle. Multi-class random forest classifier diagnosis the fault condition. PdM, knowing when to trigger maintenance action, is achieved through predicting the overall quality of the produced parts from the feature set extracted from sensor data of the Spark-out segment of the grinding cycle. Combining fault diagnosis with the predicted quality information resulted in reliable and actionable maintenance decisions for the bearing ring grinder. The demonstrated setup, based on a production bearing ring grinder, is adaptable to similar machines in production.
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| 4. |
- Ahmer, Muhammad, et al.
(författare)
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Using Multivariate Quality Statistic for Maintenance Decision Support in a Bearing Ring Grinder
- 2022
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Ingår i: Machines. - : MDPI. - 2075-1702. ; 10:9
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Tidskriftsartikel (refereegranskat)abstract
- Grinding processes’ stochastic nature poses a challenge in predicting the quality of the resulting surfaces. Post-production measurements for form, surface roughness, and circumferential waviness are commonly performed due to infeasibility in measuring all quality parameters during the grinding operation. Therefore, it is challenging to diagnose the root cause of quality deviations in real-time resulting from variations in the machine’s operating condition. This paper introduces a novel approach to predict the overall quality of the individual parts. The grinder is equipped with sensors to implement condition-based maintenance and is induced with five frequently occurring failure conditions for the experimental test runs. The crucial quality parameters are measured for the produced parts. Fuzzy c-means (FCM) and Hotelling’s T-squared (T2) have been evaluated to generate quality labels from the multi-variate quality data. Benchmarked random forest regression models are trained using fault diagnosis feature set and quality labels. Quality labels from the T2 statistic of quality parameters are preferred over FCM approach for their repeatability. The model, trained from T2 labels achieves more than 94% accuracy when compared to the measured ring disposition. The predicted overall quality using the sensors’ feature set is compared against the threshold to reach a trustworthy maintenance decision.
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| 5. |
- Albertsson, Kim
(författare)
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Machine Learning in High-Energy Physics: Displaced Event Detection and Developments in ROOT/TMVA
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Many proposed extensions to the Standard Model of particle physics predict long-lived particles, which can decay at a significant distance from the primary interaction point. Such events produce displaced vertices with distinct detector signatures when compared to standard model processes. The Large Hadron Collider (LHC) operates at a collision rate where it is not feasible to record all generated data—a problem that will be exac-erbated in the coming high-luminosity upgrade—necessitating an online trigger system to decide which events to keep based on partial information. However, the trigger is not directly sensitive to signatures with displaced vertices from Long-lived particles (LLPs). Current LLP detection approaches require a computationally expensive reconstruction step, or rely on auxiliary signatures such as energetic particles or missing energy. An improved trigger sensitivity increases the reach of searches for extensions to the standard model.This thesis explores the possibility to apply machine learning methods directly on low-level tracking features, such as detector hits and hit-pairs to identify displaced high-mass decays while avoiding a full vertex and track reconstruction step.A dataset is developed where modelled displaced signatures from novel and known physics processes are mixed in a custom simulation environment, which models the in-ner detector of a general purpose particle detector. Two machine learning models are evaluated using the dataset: a multi-layer dense Artificial Neural Network (ANN), and a Graph Neural Network (GNN). Two case studies suggest that dense ANNs have difficulty capturing relational information in low-level data, while GNNs can feasibily discriminate heavy displaced decay signatures from a Standard Model background. Furthermore it was found that GNNs can perform at a background rejection factor of 103 and a signal efficiency of 20% in collision environments with moderate levels of pile-up interactions, i.e. low-energy particle collisions simultaneous with the primary hard scatter. Further work is required to integrate the approach into a trigger environment. In particular, detector material and measurement resolution effects should be included in the simulation, which should be scaled to model the High-Luminosity Large Hadron Collider (HL-LHC) with its more complicated geometry and its high levels of pile-up.In parallel, the machine learning landscape is quickly evolving and concentrating into large software frameworks with expanding scope, while the High-Energy Physics (HEP) community maintains its own set of tools and frameworks, one example being the Toolkit for Multivariate Analysis (TMVA) which is part of the ROOT framework. This thesis discusses the long- and short-term evolution of these tools, both current trends and some relations to parallel developments in Industry 4.0.
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| 6. |
- Borngrund, Carl, 1992-, et al.
(författare)
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Automating the Short-Loading Cycle: Survey and Integration Framework
- 2024
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Ingår i: Applied Sciences. - : MDPI. - 2076-3417. ; 14:11
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Tidskriftsartikel (refereegranskat)abstract
- The short-loading cycle is a construction task where a wheel loader scoops material from a nearby pile in order to move that material to the tipping body of a dump truck. The short-loading cycle is a vital task performed in high quantities and is often part of a more extensive never-ending process to move material for further refinement. This, together with the highly repetitive nature of the short-loading cycle, makes it a suitable candidate for automation. However, the short-loading cycle is a complex task where the mechanics of the wheel loader together with the interaction between the wheel loader and the environment needs to be considered. This must be achieved while maintaining some productivity goal and, concurrently, minimizing the used energy. The main objective of this work is to analyze the short-loading cycle, assess the current state of research in this field, and discuss the steps required to progress towards a minimal viable product consisting of individual automation solutions that can perform the short-loading cycle well enough to be used by early adopters. This is achieved through a comprehensive literature study and consequent analysis of the review results. From this analysis, the requirements of an MVP are defined and some gaps which are currently hindering the realization of the MVP are presented.
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| 7. |
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| 8. |
- Borngrund, Carl, 1992-, et al.
(författare)
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Deep-learning-based vision for earth-moving automation
- 2022
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Ingår i: Automation in Construction. - : Elsevier. - 0926-5805 .- 1872-7891. ; 133
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Forskningsöversikt (refereegranskat)abstract
- Earth-moving machines are heavy-duty vehicles designed for construction operations involving earthworks. The tasks performed by such machines typically involve navigation and interaction with materials such as soil, gravel, and blasted rock. Skilled operators use a combination of visual, sound, tactile and possibly motion feedback to perform tasks efficiently. We survey the literature in this research area and analyse the relative importance of different sensor system modalities focusing on deep-learning-based vision and automation for the short-cycle loading task. This is a common and repetitive task that is attractive to automate. The analysis indicates that computer vision, in combination with onboard sensors, is more critical than coordinate-based positioning. Furthermore, we find that data-driven approaches, in general, have high potential in terms of productivity, adaptability, versatility and wear and tear with respect to automation system solutions. The main knowledge gaps identified relate to loading non-fine heterogeneous material and navigation during loading and unloading.
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| 9. |
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| 10. |
- Borngrund, Carl, 1992-, et al.
(författare)
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Machine Vision for Construction Equipment by Transfer Learning with Scale Models
- 2020
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Ingår i: 2020 International Joint Conference on Neural Networks (IJCNN). - : IEEE.
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Konferensbidrag (refereegranskat)abstract
- Machine vision is required by autonomous heavy construction equipment to navigate and interact with the environment. Wheel loaders need the ability to identify different objects and other equipment to perform the task of automatically loading and dumping material on dump trucks, which can be achieved using deep neural networks. Training such networks from scratch requires the iterative collection of potentially large amounts of video data, which is challenging at construction sites because of the complexity of safely operating heavy equipment in realistic environments. Transfer learning, for which pretrained neural networks can be retrained for use at construction sites, is thus attractive, especially if data can be acquired without full-scale experiments. We investigate the possibility of using scalemodel data for training and validating two different pretrained networks and use real-world test data to examine their generalization capability. A dataset containing 268 images of a 1:16 scale model of a Volvo A60H dump truck is provided, as well as 64 test images of a full-size Volvo A25G dump truck. The code and dataset are publicly available 1 . The networks, both pretrained on the MS-COCO dataset, were fine-tuned to the created dataset, and the results indicate that both networks can learn the features of the scale-model dump truck (validation mAP of 0.82 for YOLOv3 and 0.95 for RetinaNet). Both networks can transfer these learned features to detect objects on a full-size dump truck with no additional training (test mAP of 0.70 for YOLOv3 and 0.79 for RetinaNet).
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