| 1. |
- Russberg, Anna, et al.
(författare)
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Piska eller Morot?
- 2018
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Bok (populärvet., debatt m.m.)
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| 2. |
- Martín del Campo Barraza, Sergio, 1983-
(författare)
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Unsupervised feature learning applied to condition monitoring
- 2017
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Improving the reliability and efficiency of rotating machinery are central problems in many application domains, such as energy production and transportation. This requires efficient condition monitoring methods, including analytics needed to predict and detect faults and manage the high volume and velocity of data. Rolling element bearings are essential components of rotating machines, which are particularly important to monitor due to the high requirements on the operational conditions. Bearings are also located near the rotating parts of the machines and thereby the signal sources that characterize faults and abnormal operational conditions. Thus, bearings with embedded sensing, analysis and communication capabilities are developed. However, the analysis of signals from bearings and the surrounding components is a challenging problem due to the high variability and complexity of the systems. For example, machines evolve over time due to wear and maintenance, and the operational conditions typically also vary over time. Furthermore, the variety of fault signatures and failure mechanisms makes it difficult to derive generally useful and accurate models, which enable early detection of faults at reasonable cost. Therefore, investigations of machine learning methods that avoid some of these difficulties by automated on-line adaptation of the signal model are motivated. In particular, can unsupervised feature learning methods be used to automatically derive useful information about the state and operational conditions of a rotating machine? What additional methods are needed to recognize normal operational conditions and detect abnormal conditions, for example in terms of learned features or changes of model parameters? Condition monitoring systems are typically based on condition indicators that are pre-defined by experts, such as the amplitudes in certain frequency bands of a vibration signal, or the temperature of a bearing. Condition indicators are used to define alarms in terms of thresholds; when the indicator is above (or below) the threshold, an alarm indicating a fault condition is generated, without further information about the root cause of the fault. Similarly, machine learning methods and labeled datasets are used to train classifiers that can be used for the detection of faults. The accuracy and reliability of such condition monitoring methods depends on the type of condition indicators used and the data considered when determining the model parameters. Hence, this approach can be challenging to apply in the field where machines and sensor systems are different and change over time, and parameters have different meaning depending on the conditions. Adaptation of the model parameters to each condition monitoring application and operational condition is also difficult due to the need for labeled training data representing all relevant conditions, and the high cost of manual configuration. Therefore, neither of these solutions is viable in general. In this thesis I investigate unsupervised methods for feature learning and anomaly detection, which can operate online without pre-training with labeled datasets. Concepts and methods for validation of normal operational conditions and detection of abnormal operational conditions based on automatically learned features are proposed and studied. In particular, dictionary learning is applied to vibration and acoustic emission signals obtained from laboratory experiments and condition monitoring systems. The methodology is based on the assumption that signals can be described as a linear superposition of noise and learned atomic waveforms of arbitrary shape, amplitude and position. Greedy sparse coding algorithms and probabilistic gradient methods are used to learn dictionaries of atomic waveforms enabling sparse representation of the vibration and acoustic emission signals. As a result, the model can adapt automatically to different machine configurations, and environmental and operational conditions with a minimum of initial configuration. In addition, sparse coding results in reduced data rates that can simplify the processing and communication of information in resource-constrained systems. Measures that can be used to detect anomalies in a rotating machine are introduced and studied, like the dictionary distance between an online propagated dictionary and a set of dictionaries learned when the machine is known to operate in healthy conditions. In addition, the possibility to generalize a dictionary learned from the vibration signal in one machine to another similar machine is studied in the case of wind turbines. The main contributions of this thesis are the extension of unsupervised dictionary learning to condition monitoring for anomaly detection purposes, and the related case studies demonstrating that the learned features can be used to obtain information about the condition. The cases studies include vibration signals from controlled ball bearing experiments and wind turbines; and acoustic emission signals from controlled tensile strength tests and bearing contamination experiments. It is found that the dictionary distance between an online propagated dictionary and a baseline dictionary trained in healthy conditions can increase up to three times when a fault appears, without reference to kinematic information like defect frequencies. Furthermore, it is found that in the presence of a bearing defect, impulse-like waveforms with center frequencies that are about two times higher than in the healthy condition are learned. In the case of acoustic emission analysis, it is shown that the representations of signals of different strain stages of stainless steel appear as distinct clusters. Furthermore, the repetition rates of learned acoustic emission waveforms are found to be markedly different for a bearing with and without particles in the lubricant, especially at high rotational speed above 1000 rpm, where particle contaminants are difficult to detect using conventional methods. Different hyperparameters are investigated and it is found that the model is useful for anomaly detection with as little as 2.5 % preserved coefficients.
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| 3. |
- Hook, B, et al.
(författare)
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Prenatal and postnatal maternal smoking as risk factors for preschool children's mental health
- 2006
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Ingår i: Acta Pædiatrica. - : Wiley. - 1651-2227 .- 0803-5253. ; 95:6, s. 671-677
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Tidskriftsartikel (refereegranskat)abstract
- Aim: To identify maternal prenatal and postnatal smoking as risk factors for psychosocial behaviour problems in Swedish preschool children. Methods: This prospective, longitudinal population study compared mothers' self-reported smoking during pregnancy and when the child was 3 mo old with behaviour problems according to Achenbach's Child Behavior Checklist at 3 y ( 1428 children) and 5.5 y of age ( 677 of the children). Results: 16% of the mothers smoked during pregnancy and the same number after the birth of the child. Controlling for possible confounding variables, maternal smoking was significantly related to externalizing problems, aggressive behaviour, and destructive/delinquent behaviour both at 3 y and 5.5 y. The effect was as strong for girls as for boys. Length and weight were lower for children of smoking mothers than for children of non-smoking mothers. Conclusion: Our study supports the importance of preventing maternal smoking during pregnancy and the infant years. Even a few cigarettes per day have negative consequences for the child. The goal must be total abstinence from smoking both pre- and postnatally.
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| 4. |
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| 5. |
- Martínez-Abadias, Neus, et al.
(författare)
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Heritability of human cranial dimensions : comparing the evolvability of different cranial regions
- 2009
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Ingår i: Journal of Anatomy. - : Wiley. - 0021-8782 .- 1469-7580. ; 214:1, s. 19-35
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Forskningsöversikt (refereegranskat)abstract
- Quantitative craniometrical traits have been successfully incorporated into population genetic methods to provide insight into human population structure. However, little is known about the degree of genetic and non-genetic influences on the phenotypic expression of functionally based traits. Many studies have assessed the heritability of craniofacial traits, but complex patterns of correlation among traits have been disregarded. This is a pitfall as the human skull is strongly integrated. Here we reconsider the evolutionary potential of craniometric traits by assessing their heritability values as well as their patterns of genetic and phenotypic correlation using a large pedigree-structured skull series from Hallstatt (Austria). The sample includes 355 complete adult skulls that have been analysed using 3D geometric morphometric techniques. Heritability estimates for 58 cranial linear distances were computed using maximum likelihood methods. These distances were assigned to the main functional and developmental regions of the skull. Results showed that the human skull has substantial amounts of genetic variation, and a t-test showed that there are no statistically significant differences among the heritabilities of facial, neurocranial and basal dimensions. However, skull evolvability is limited by complex patterns of genetic correlation. Phenotypic and genetic patterns of correlation are consistent but do not support traditional hypotheses of integration of the human shape, showing that the classification between brachy- and dolicephalic skulls is not grounded on the genetic level. Here we support previous findings in the mouse cranium and provide empirical evidence that covariation between the maximum widths of the main developmental regions of the skull is the dominant factor of integration in the human skull.
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| 6. |
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| 7. |
- Rastegari, Ali, et al.
(författare)
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Online Condition Monitoring of Gas Circulation Fans in Hardening Process
- 2016
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Ingår i: Congress Proceedings of COMADEM 2016, International Congress of Condition Monitoring and Diagnostics Engineering Management COMADEM 2016, 2016.
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Konferensbidrag (refereegranskat)abstract
- Vibration analysis and the Shock Pulse Method (SPM) are two of the most popular condition monitoring techniques used in Condition-Based Maintenance (CBM) policy, especially for rotating equipment. To illustrate the extent to which advanced CBM techniques (in this case, vibration analysis and SPM) are applicable and cost effective in a manufacturing company, a pilot pro-ject was followed in real time. The pilot project was performed at a large manu-facturing site in Sweden. The purpose of the project was to implement online condition monitoring of five critical gas circulation fans in the hardening pro-cess of the manufacturing company. This paper presents some of the main findings of the online condition monitor-ing of the fans for a period of two years. Consequently, based on the empirical data, the company was able to gain great profit due to preventing production losses by preventing breakdowns of the fans.
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| 8. |
- Rastegari, Ali
(författare)
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Strategic Maintenance Development focusing on use of Condition Based Maintenance in Manufacturing Industry
- 2015
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The growth of global competition caused remarkable changes in the way manufacturing companies operate. These changes have affected maintenance and made its role even more crucial in business success. In order to stay competitive, manufacturing companies need to continuously increase the effectiveness and efficiency of their production processes. Further, by introducing lean manufacturing the concern about equipment availability is increased and so, the demand for effective maintenance. Despite the increasing demand on reliable production equipment, few manufacturing companies work with strategic maintenance development. Moreover, conventional maintenance strategies such as corrective maintenance are not sufficient today to fulfill the industrial needs on maximum reduction of failures and degradations of manufacturing systems. The concept of maintenance has evolved over the last few decades from a corrective attitude (maintenance intervention after a failure), to a predictive attitude (maintenance intervention fixed to prevent the fault). Strategies and concepts such as Condition Based Maintenance (CBM) have thus evolved to support this ideal situation. CBM is a set of maintenance actions based on real-time or near real-time assessment of equipment condition, which is obtained from embedded sensors and/or external tests and measurements taken by portable equipment and/or subjective condition monitoring. CBM is becoming recognized as the most efficient strategy for carrying out maintenance in a wide variety of industries. However, the practical implementation of advanced maintenance technologies, such as CBM, in manufacturing industry is more scarce.Therefore, the objective with this research is to study on how to implement and develop an effective and efficient CBM strategy in manufacturing industry. This thesis will start with an analysis of the overall maintenance management to illustrate how to formulate a maintenance strategy, following with the focus on CBM; cost effectiveness of implementing CBM; an introductory review of applied CBM practices and CBM implementation process, all in manufacturing industry. The data was collected through case studies mainly at one major manufacturing site. The main part of the data was collected during a pilot project to implement CBM. As the result, a formulated maintenance strategy has been developed and presented. Factors to evaluate CBM cost effectiveness have been assessed. These factors indicate the benefits of CBM mostly in reducing probability of having maximal damage in production equipment and reducing production losses particularly in high production volumes. Further, a process of CBM implementation has been presented. Some of the main elements in the process are selection of the components to be monitored, techniques and technologies as well as installation of the technologies and finally how to analyze the results from the condition monitoring.
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| 9. |
- Rastegari, Ali, et al.
(författare)
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Strategic maintenance management : Formulating maintenance strategy
- 2013
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Ingår i: International Congress Condition monitoring and Diagnostic Engineering Management. - 9789526798103
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Konferensbidrag (refereegranskat)abstract
- In recent decades, by introducing lean manufacturing the vulnerability to system disturbances has increased and so, the demand for dependable production equipment. The need for having high production equipment availability causes companies to need a more effective and efficient maintenance strategy in order to stay competitive. Despite the increasing demand on reliable production equipment, few manufacturing companies work with strategic maintenance development and a large part of the manufacturing industry lack clear maintenance strategies. It is therefore difficult to develop the maintenance work in accordance with the strategic goals of the manufacturing companies. The main objective of this paper is to define a process for formulating maintenance strategy in order to facilitate further development in a strategic way. The problem has been studied by literature review and through case study at one major manufacturing site in Sweden to investigate the company’s view on strategic maintenance development. Hence, a formulated maintenance strategy has been provided and presented. The company’s overall goals considered and translated to the strategic objective of maintenance. Moreover, balanced score card is used as a tool to make a framework of the maintenance strategy. As a result of this study, the company could easily formulate a maintenance strategy by using a simple process based on the tools that they have already used. In addition to this, the result indicated how maintenance strategy can contribute to the company’s business goals.
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| 10. |
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