| 1. |
- Glimelius, Bengt, et al.
(författare)
-
U-CAN : a prospective longitudinal collection of biomaterials and clinical information from adult cancer patients in Sweden.
- 2018
-
Ingår i: Acta Oncologica. - : Taylor & Francis. - 0284-186X .- 1651-226X. ; 57:2, s. 187-194
-
Tidskriftsartikel (refereegranskat)abstract
- Background: Progress in cancer biomarker discovery is dependent on access to high-quality biological materials and high-resolution clinical data from the same cases. To overcome current limitations, a systematic prospective longitudinal sampling of multidisciplinary clinical data, blood and tissue from cancer patients was therefore initiated in 2010 by Uppsala and Umeå Universities and involving their corresponding University Hospitals, which are referral centers for one third of the Swedish population.Material and Methods: Patients with cancer of selected types who are treated at one of the participating hospitals are eligible for inclusion. The healthcare-integrated sampling scheme encompasses clinical data, questionnaires, blood, fresh frozen and formalin-fixed paraffin-embedded tissue specimens, diagnostic slides and radiology bioimaging data.Results: In this ongoing effort, 12,265 patients with brain tumors, breast cancers, colorectal cancers, gynecological cancers, hematological malignancies, lung cancers, neuroendocrine tumors or prostate cancers have been included until the end of 2016. From the 6914 patients included during the first five years, 98% were sampled for blood at diagnosis, 83% had paraffin-embedded and 58% had fresh frozen tissues collected. For Uppsala County, 55% of all cancer patients were included in the cohort.Conclusions: Close collaboration between participating hospitals and universities enabled prospective, longitudinal biobanking of blood and tissues and collection of multidisciplinary clinical data from cancer patients in the U-CAN cohort. Here, we summarize the first five years of operations, present U-CAN as a highly valuable cohort that will contribute to enhanced cancer research and describe the procedures to access samples and data.
|
|
| 2. |
- Ahmer, Muhammad, et al.
(författare)
-
Dataset Concerning the Process Monitoring and Condition Monitoring Data of a Bearing Ring Grinder
- 2022
-
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.
|
|
| 3. |
- Ahmer, Muhammad, et al.
(författare)
-
Failure mode classification for condition-based maintenance in a bearing ring grinding machine
- 2022
-
Ingår i: The International Journal of Advanced Manufacturing Technology. - : Springer Nature. - 0268-3768 .- 1433-3015. ; 122, s. 1479-1495
-
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.
|
|
| 4. |
- Ahmer, Muhammad
(författare)
-
Intelligent fault diagnosis and predictive maintenance for a bearing ring grinder
- 2023
-
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.
|
|
| 5. |
- Ahmer, Muhammad, et al.
(författare)
-
Using Multivariate Quality Statistic for Maintenance Decision Support in a Bearing Ring Grinder
- 2022
-
Ingår i: Machines. - : MDPI. - 2075-1702. ; 10:9
-
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.
|
|
| 6. |
- Albertsson, Kim
(författare)
-
Machine Learning in High-Energy Physics: Displaced Event Detection and Developments in ROOT/TMVA
- 2021
-
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.
|
|
| 7. |
- Ali, Ashfaq, et al.
(författare)
-
Quantitative proteomics and transcriptomics of potato in response to Phytophthora infestans in compatible and incompatible interactions
- 2014
-
Ingår i: BMC Genomics. - : Springer Science and Business Media LLC. - 1471-2164. ; 15
-
Tidskriftsartikel (refereegranskat)abstract
- Background: In order to get global molecular understanding of one of the most important crop diseases worldwide, we investigated compatible and incompatible interactions between Phytophthora infestans and potato (Solanum tuberosum). We used the two most field-resistant potato clones under Swedish growing conditions, which have the greatest known local diversity of P. infestans populations, and a reference compatible cultivar. Results: Quantitative label-free proteomics of 51 apoplastic secretome samples (PXD000435) in combination with genome-wide transcript analysis by 42 microarrays (E-MTAB-1515) were used to capture changes in protein abundance and gene expression at 6, 24 and 72 hours after inoculation with P. infestans. To aid mass spectrometry analysis we generated cultivar-specific RNA-seq data (E-MTAB-1712), which increased peptide identifications by 17%. Components induced only during incompatible interactions, which are candidates for hypersensitive response initiation, include a Kunitz-like protease inhibitor, transcription factors and an RCR3-like protein. More secreted proteins had lower abundance in the compatible interaction compared to the incompatible interactions. Based on this observation and because the well-characterized effector-target C14 protease follows this pattern, we suggest 40 putative effector targets. Conclusions: In summary, over 17000 transcripts and 1000 secreted proteins changed in abundance in at least one time point, illustrating the dynamics of plant responses to a hemibiotroph. Half of the differentially abundant proteins showed a corresponding change at the transcript level. Many putative hypersensitive and effector-target proteins were single representatives of large gene families.
|
|
| 8. |
- Aminu Sanda, Mohammed, et al.
(författare)
-
Lean instrumentation framework for sensor pruning and optimization in condition monitoring
- 2011
-
Ingår i: The Eighth International Conference on Condition Monitoring and Machinery Failure Prevention Technologies. - Longborough, Glos : Coxmoor Publishing Co.. - 9781618390141 ; , s. 202-215
-
Konferensbidrag (refereegranskat)abstract
- This paper discusses a lean instrumentation framework for guiding the introduction of the lean concept in condition monitoring in order to enhance the organizational capability (i.e. human, technical and management trichotomy) and reduce the complexity in the maintenance management systems of industrial companies. Additionally, decision-making, based on severity diagnosis and prognosis in condition monitoring, is a complex maintenance function which is based on large data-set of sensors measurements. Yet, the entirety of such decision-making is not dependent on only the sensors measurements, but also on other important indices, such as the human factors, organizational aspects and knowledge management. This is because, the ability to identify significant features from large amount of measured data is a major challenge for automated defect diagnosis, a situation that necessitate the need to identify signal transformations and features in new domains. The need for the lean instrumentation framework is justified by the desire to have a modern condition monitoring system with the capability of pruning to the optimal level the number of sensors required for efficient and effective serviceability of the maintenance process. It is concluded that there are methodologies that can be developed to enable more efficient condition monitoring systems, with benefits for many processes along the value chain.
|
|
| 9. |
- Antberg, Linn, et al.
(författare)
-
Critical Comparison of Multidimensional Separation Methods for Increasing Protein Expression Coverage
- 2012
-
Ingår i: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 11:5, s. 2644-2652
-
Tidskriftsartikel (refereegranskat)abstract
- We present a comparison of two-dimensional separation methods and how they affect the degree of coverage of protein expression in complex mixtures. We investigated the relative merits of various protein and peptide separations prior to acidic reversed-phase chromatography directly coupled to an ion trap mass spectrometer. The first dimensions investigated were density gradient organelle fractionation of cell extracts, 1D SDS-PAGE protein separation followed by digestion by trypsin or GluC proteases, strong cation exchange chromatography, and off-gel isoelectric focusing of tryptic peptides. The number of fractions from each first dimension and the total data accumulation RP-HPLC-MS/MS time was kept constant and the experiments were run in triplicate. We find that the most critical parameters are the data accumulation time, which defines the level of under-sampling and the avoidance of peptides from high expression level proteins eluting over the entire gradient.
|
|
| 10. |
- Arranz, Miguel Castano
(författare)
-
Robust methods for control structure selection in paper making processes
- 2010
-
Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Process industries have to operate in a very competitive and globalized environment, requiring efficient and sustainable production processes. As a result, production targets need to be translated into control objectives which are usually formulated as performance specifications of the process, i.e. tracking of references or rejection of process disturbances. This is often a hard and difficult task which involves assumptions and simplications because of the process complexity. Complexity arises often due to the large scale character of a process, i.e. a pulp and paper can host thousands of control loops. A critical step in the design of these loops is the choice of the structure of the control, which means that controllers need to be placed between sensors and actuators.Current methods for control structure selection include the Interaction Measures (IMs). The IMs help the designer to select a subset of the most significant input-output channels, which will form a reduced model on which the control design will be based. The IMs are traditionally evaluated using a nominal model of the process. However, all process models are affected by uncertainties as simplifications and approximations are unavoidable during modeling. Thus, the validity of the control structure suggested by the IMs cannot be assessed by only analyzing the nominal model. The first part of this thesis focuses in analyzing the sensitivity of the IMs to model uncertainties in order to determine a robust control structure which is feasible for all the uncertainty set.It also becomes clear that, control structure selection requires extensive knowledge about how the multiple process variables are interconnected. The second part of this thesis focuses on creating IMs which can help the control designers to understand the propagation of effects in the process, and express this propagation in directed graphs for an intuitive understanding of the process which will help to design a feasible control structure. These methods have been inspired by coherence analysis used in brain connectivity.Neurons and neural populations interact with each other in different brain processes related to events as perception, or cognition. Electroencephalography (EEG) is a measure of electrical activity in the brain which is acquired from sensors positioned on the surface of the head, each of the electrodes collects the aggregated voltage of a neuron population. Analyzing the flow of information between populations of neurons allows to understand the communication between different parts of the brain in different brain processes. In a very similar way, analyzing the flow of information between variables in an industrial process will provide designers with the required information to understand the behavior of the plant.
|
|
