| 1. |
- Hassler, Donald M., et al.
(författare)
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Mars’ surface radiation environment measured with the Mars Science Laboratory’s Curiosity Rover
- 2014
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Ingår i: Science. - : American Association for the Advancement of Science (AAAS). - 0036-8075 .- 1095-9203. ; 343:6169
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Tidskriftsartikel (refereegranskat)abstract
- The Radiation Assessment Detector (RAD) on the Mars Science Laboratory’s Curiosity rover began making detailed measurements of the cosmic ray and energetic particle radiation environment on the surface of Mars on 7 August 2012. We report and discuss measurements of the absorbed dose and dose equivalent from galactic cosmic rays and solar energetic particles on the martian surface for ~300 days of observations during the current solar maximum. These measurements provide insight into the radiation hazards associated with a human mission to the surface of Mars and provide an anchor point with which to model the subsurface radiation environment, with implications for microbial survival times of any possible extant or past life, as well as for the preservation of potential organic biosignatures of the ancient martian environment.
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| 2. |
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| 3. |
- Kim, Myung-Hee Y., et al.
(författare)
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Comparison of Martian surface ionizing radiation measurements from MSL-RAD with Badhwar-O'Neill 2011/HZETRN model calculations
- 2014
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Ingår i: Journal of Geophysical Research - Planets. - 2169-9097 .- 2169-9100. ; 119:6, s. 1311-1321
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Tidskriftsartikel (refereegranskat)abstract
- Dose rate measurements from Mars Science Laboratory-radiation assessment detector (MSL-RAD) for 300 sols on Mars are compared to simulation results using the Badhwar-O'Neill 2011 galactic cosmic ray (GCR) environment model and the high-charge and energy transport (HZETRN) code. For the nuclear interactions of primary GCR through Mars atmosphere and Curiosity rover, the quantum multiple scattering theory of nuclear fragmentation is used. Daily atmospheric pressure is measured at Gale Crater by the MSL Rover Environmental Monitoring Station. Particles impinging on top of the Martian atmosphere reach RAD after traversing varying depths of atmosphere that depend on the slant angles, and the model accounts for shielding of the RAD “E” detector (used for dosimetry) by the rest of the instrument. Simulation of average dose rate is in good agreement with RAD measurements for the first 200 sols and reproduces the observed variation of surface dose rate with changing heliospheric conditions and atmospheric pressure. Model results agree less well between sols 200 and 300 due to subtleties in the changing heliospheric conditions. It also suggests that the average contributions of albedo particles (charge number Z < 3) from Martian regolith comprise about 10% and 42% of the average daily point dose and dose equivalent, respectively. Neutron contributions to tissue-averaged effective doses will be reduced compared to point dose equivalent estimates because a large portion of the neutron point dose is due to low-energy neutrons with energies.
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| 4. |
- Rafkin, Scot C.R., et al.
(författare)
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Diurnal variations of energetic particle radiation at the surface of Mars as observed by the Mars Science Laboratory Radiation Assessment Detector
- 2014
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Ingår i: Journal of Geophysical Research - Planets. - 2169-9097 .- 2169-9100. ; 119:6, s. 1345-1358
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Tidskriftsartikel (refereegranskat)abstract
- The Radiation Assessment Detector onboard the Mars Science Laboratory rover Curiosity is detecting the energetic particle radiation at the surface of Mars. Data collected over the first 350 Martian days of the nominal surface mission show a pronounced diurnal cycle in both the total dose rate and the neutral particle count rate. The diurnal variations detected by the Radiation Assessment Detector were neither anticipated nor previously considered in the literature. These cyclic variations in dose rate and count rate are shown to be the result of changes in atmospheric column mass driven by the atmospheric thermal tide that is characterized through pressure measurements obtained by the Rover Environmental Monitoring Station, also onboard the rover. In addition to bulk changes in the radiation environment, changes in atmospheric shielding forced by the thermal tide are shown to disproportionately affect heavy ions compared to H and He nuclei.
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| 5. |
- Martin del Campo Barraza, Sergio, 1983-, et al.
(författare)
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Detection of particle contaminants in rolling element bearings with unsupervised acoustic emission feature learning
- 2019
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Ingår i: Tribology International. - : Elsevier. - 0301-679X .- 1879-2464. ; 132, s. 30-38
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Tidskriftsartikel (refereegranskat)abstract
- The detection of contaminants in the lubricant of rolling element bearings using acoustic emission signals is a challenging problem, in particular at high rotational speeds. This problem calls for new analysis methods beyond the conventional amplitude- and frequency-based methods. Feature learning is successfully used in the machine learning field to characterize complex signals. Here we use an unsupervised feature learning approach to distinguish acoustic emission signals. We investigate the repetition rates of features identified with shift-invariant dictionary learning and find that the signature of contaminated lubricant is significantly stronger than the effect on conventional condition indicators like the RMS and the enveloped RMS at rotational speeds above 300 rpm and up to 3000 rpm.
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| 6. |
- Martin del Campo Barraza, Sergio, 1983-, et al.
(författare)
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Kinematic Frequencies of Rotating Equipment Identified with Sparse Coding and Dictionary Learning
- 2019
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Ingår i: Proceedings of the Annual Conference of the Prognostics and Health Management Society 2019. - Scottsdale, AZ, USA : Prognostics and Health Management Society.
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Konferensbidrag (refereegranskat)abstract
- The detection of faults and operational abnormalities in rotating machine elements like rolling element bearings and gears requires information about kinematic properties, such as ball-pass and gear mesh frequencies. Typically, condition monitoring experts obtain such information from the manufacturers for diagnostics purposes. However, the reliability of such information can be compromised during installation and maintenance, for example, if components are replaced and do not match the documented specifications. Thus, methods enabling verification and online extraction of such kinematic properties are needed to improve diagnostic reliability. Unsupervised machine learning methods, like sparse coding with dictionary learning, enable automatic modeling and characterization of repeating signal structures in the time domain, which are naturally generated by rotating equipment. Sparse coding with dictionary learning represents a vibration signal as a linear superposition of noise and atomic waveforms. The activation rate of the atomic waveforms typically possesses a cyclic nature in rotating environments, similar to how bearing kinematic frequencies correlate with faults in a rolling element bearing. However, there is no explicit relationship between the activation rates of the atoms and the bearing kinematic frequencies. This motivates this investigation of the possibility to extract bearing kinematic frequencies from sparse representations. Former work describes the use of dictionary learning for the detection of anomalies in rolling element bearings. In this paper, we describe how a similar unsupervised machine learning method can be used to extract kinematic frequencies of bearings and gears, for example for anomaly detection purposes and comparisons with an expected signature. We study the activation rates and changes of atoms learned from vibration signals in two case studies. The first case is based on data from a well-known controlled experiment with faults seeded in the bearings. The second case is based on a public dataset recorded from the high-speed shaft of a wind turbine with a bearing failure. Furthermore, we compare the activation rates and weights of the atoms to the bearing kinematic frequencies and harmonics. Sparse coding with dictionary learning offers a possibility for self-learningof the kinematic frequencies of a bearing, which can be useful for the further improvement of automated anomaly detection methods in condition monitoring.
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| 7. |
- Martin-del-Campo, Sergio, 1983-, et al.
(författare)
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Algorithmic performance constraints for wind turbine condition monitoring via convolutional sparse coding with dictionary learning
- 2021
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Ingår i: Journal of Risk and Reliability. - : Sage Publications. - 1748-006X .- 1748-0078. ; 235:4, s. 660-675
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Tidskriftsartikel (refereegranskat)abstract
- We analyze vibration signals from wind turbines with dictionary learning and investigate the relation between dictionary distances and faults occurring in a wind turbine output shaft rolling element bearing and gearbox under different data and compute constraints. Dictionary learning is an unsupervised machine learning method for signal processing, which permits learning a set of signal-specific features that have been used to monitor the condition of rotating machines, including wind turbines. Dictionary distance is one such feature, and its effectiveness depends on an adequate selection of the dictionary learning hyperparameters and the data availability, which typically is constrained in condition monitoring systems for remotely located wind farms. Here we evaluate the characteristics of the dictionary distance feature under healthy and faulty conditions of the wind turbines using different options for the selection of the pretrained dictionary, the sparsity of the signal model which determines the compute requirements, and the interval between data samples. Furthermore, we compare the dictionary distance feature to the typical time-domain features used in condition monitoring. We find that the dictionary distance based feature of a faulty wind turbine deviates by a factor of two or more from the population distribution several weeks before the gearbox bearing fault was reported, using a data sampling interval as long as 24 h and a model sparsity as low as 2.5%.
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| 8. |
- Martin-del-Campo, Sergio, 1983-, et al.
(författare)
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Exploratory Analysis of Acoustic Emissions in Steel using Dictionary Learning
- 2016
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Ingår i: IEEE Ultrasonics Symposium 2016, Tours France, September 18-21, 2016. - Piscataway, NJ : IEEE conference proceedings. - 9781467398978 - 9781467398985
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Konferensbidrag (refereegranskat)abstract
- Analysis of acoustic emissions (AE) from steel deformation is a challenging condition monitoring problem due to the high frequencies and data rates involved, and the difficulty to separate signals from noise. The problem to characterize and identify different AE sources calls for methods that goes beyond conventional time and frequency domain analysis. Feature learning is common in the field of machine learning and is successfully used to approximate and classify other kinds of complex signals. Former studies show that AE classification results depend on the choice of predefined features that are extracted from the raw AE signal, but little is known about feature learning in this context. Here we use dictionary learning and sparse coding to optimize a set of shift-invariant features to the AE signal measured in a steel tensile strength test. The specimen undergoes elastic and plastic deformation and eventually cracks. We investigate the learned features and their repetition rates and use principal component analysis (PCA) to illustrate that the resulting sparse AE code is useful for classification of the three strain stages, without reference to the signal amplitude. Therefore, feature learning is a potentially useful approach to the AE analysis problem, which also opens up for further studies of automated methods for anomaly detection in AE.
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