| 1. |
- Jakobsson, Erik, 1987-
(författare)
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Condition Monitoring in Mobile Mining Machinery
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The global mining industry is currently facing a huge transition from manually operated individual vehicles, to autonomous vehicles being part of an industrial process-like environment. The change is driven by the never ending need for efficient, safe, and environmentally friendly operations. One intentional consequence is an increased distance between the operator, and the machine being operated. This enables safer working environments and reduced cost for ventilation and other supporting systems in a mine, but it also results in the loss of the systems most important sensor. The transition from manual to autonomous operation requires this gap to be filled from a system awareness perspective, which lately has become evident with the large resources that car manufacturers use to develop self-driving cars. This thesis also targets system awareness, but of the internal kind. By this we mean knowing the condition of the machine and its capabilities. The operator is the most important sensor also for internal condition, and if no operator is present on the machine, this gap needs to be filled.The mining industry is categorized by small series and significant customization of machinery. This is a direct result of the geological prerequisites, where differently shaped ore bodies cause large differences in mine layout and mining methods. This thesis explores how methods estimating the health of mining vehicles can be used in this setting, by utilizing sensor signals to make assessments of the current vehicle condition and tasks.The resulting health information can be used both to aid in tasks such as maintenance planning, but also as an important input to decision making for the planning system, i.e. how to run the vehicle for minimum wear and damage, while maintaining other mission objectives.Two applications are studied. Mine trucks have slow degradation modes, such as crack propagation and fatigue, that are difficult to handle with data driven approaches since data collection requires significant amounts of time. A contribution in this thesis, is a method to utilize short term measurement data together with data driven methods to obtain the loads of a vehicle, and then to use physics based approaches to estimate the actual damage.The second application considers monitoring faults in hydraulic rock drills using online measurements during operation. The rock drill is a specifically difficult case, since severe vibration levels limits the locations and types of sensors that can be used. The main contribution is a method to handle individual differences when classifying internal faults using a single pressure sensor on the hydraulic supply line. A complicating factor is the large influence of wave propagation, causing different individuals to show different behavior.
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| 2. |
- Jakobsson, Erik, 1987-, et al.
(författare)
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Data driven modeling and estimation of accumulated damage in mining vehicles using on-board sensors
- 2017
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Ingår i: PHM 2017. Proceedings of the Annual Conference of the Prognostics and Health Management Society 2017, St. Petersburg, Florida, USA, October 2–5, 2017. - : Prognostics and Health Management Society. - 9781936263264 ; , s. 98-107
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Konferensbidrag (refereegranskat)abstract
- The life and condition of a MT65 mine truck frame is to a large extent related to how the machine is used. Damage from different stress cycles in the frame are accumulated over time, and measurements throughout the life of the machine are needed to monitor the condition. This results in high demands on the durability of sensors used. To make a monitoring system cheap and robust enough for a mining application, a small number of robust sensors are preferred rather than a multitude of local sensors such as strain gauges. The main question to be answered is whether a low number of robust on-board sensors can give the required information to recreate stress signals at various locations of the frame. Also the choice of sensors among many different locations and kinds are considered. A final question is whether the data could also be used to estimate road condition. By using accelerometer, gyroscope and strain gauge data from field tests of an Atlas Copco MT65 mine truck, coherence and Lasso-regression were evaluated as means to select which signals to use. ARX-models for stress estimation were created using the same data. By simulating stress signals using the models, rain flow counting and damage accumulation calculations were performed. The results showed that a low number of on-board sensors like accelerometers and gyroscopes could give enough information to recreate some of the stress signals measured. Together with a linear model, the estimated stress was accurate enough to evaluate the accumulated fatigue damage in a mining truck. The accumulated damage was also used to estimate the condition of the road on which the truck was traveling. To make a useful road monitoring system some more work is required, in particular regarding how vehicle speed influences damage accumulation.
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| 3. |
- Jakobsson, Erik, 1987-
(författare)
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Data-driven Condition Monitoring in Mining Vehicles
- 2019
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Situation awareness is a crucial capability of any autonomous system, including mining vehicles such as drill rigs and mine trucks. Typically situation awareness is interpreted as the capability of an autonomous system to interpret its surroundings and the intentions of other agents. The internal system awareness however, is often not receiving the same focus, even though the success of any given mission is completely dependent of the condition of the agents themselves. The internal system awareness in the form of vehicle health is the focus of this thesis.As the mining industry becomes increasingly automated, and vehicles become increasingly advanced, the need for condition monitoring and prognostics will continue to rise. This thesis explores data-driven methods that estimate the health of mining vehicles to accommodate those needs. We do so by utilizing available sensor signals, common on a large amount of mining vehicles, to make assessments of the current vehicle condition and tasks. The mining industry is characterized by small series of highly specialized vehicles, which affects the possibility to use more traditional prognostic solutions.The resulting health information can be used both to aid in tasks such as maintenance planning, but also as an important input to decision making for the planning system, i.e. how to run the vehicle for minimum wear and damage, while maintaining other mission objectives.The contributions include: a) A method to use operational data to estimate damage on the frame of a mine truck. This is done using system identification to find a model describing stresses in the structure with input from other sensors such as accelerometers, load sensors and pressure sensors. The estimated stress time signal is in turn used to calculate accumulated damage, and is shown to reveal interesting conclusions on driver behavior. b) A method to characterize the different driving tasks by using an accelerometer and a convolutional neural network. We show that the model is capable of classifying the vehicle task correctly in 96 % of the cases. And finally c), a system for underground road monitoring, where a quarter car model and a Kalman filter are used to generate an estimate of the road profile, while positioning the vehicle using inertial measurements and access point signal strength.
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| 4. |
- Jakobsson, Erik, 1987-, et al.
(författare)
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Fatigue Damage Monitoring for Mining Vehicles using Data Driven Models
- 2020
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Ingår i: International Journal of Prognostics and Health Management. - Rochester, NY, United States : Prognostics and Health Management Society. - 2153-2648. ; 11:1
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Tidskriftsartikel (refereegranskat)abstract
- The life and condition of a mine truck frame are related to how the machine is used. Damage from stress cycles is accumulated over time, and measurements throughout the life of the machine are needed to monitor the condition. This results in high demands on the durability of sensors, especially in a harsh mining application. To make a monitoring system cheap and robust, sensors already available on the vehicles are preferred rather than additional strain gauges. The main question in this work is whether the existing on-board sensors can give the required information to estimate stress signals and calculate accumulated damage of the frame. Model complexity requirements and sensors selection are also considered. A final question is whether the accumulated damage can be used for prognostics and to increase reliability. The investigation is performed using a large data set from two vehicles operating in real mine applications. Coherence analysis, ARX-models, and rain flow counting are techniques used. The results show that a low number of available on-board sensors like load cells, damper cylinder positions, and angle transducers can give enough information to recreate some of the stress signals measured. The models are also used to show significant differences in usage by different operators, and its effect on the accumulated damage.
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| 5. |
- Jakobsson, Erik, 1987-, et al.
(författare)
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Time Series Fault Classification for Wave Propagation Systems with Sparse Fault Data
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- In this work Time Series Classification techniques are investigated, and especially their applicability in applications where there are significant differences between the individuals where data is collected, and the individuals where the classification is evaluated. Classification methods are applied to a fault classification case, where a key assumption is that data from a fault free reference case for each specific individual is available. For the investigated application, wave propagation cause almost chaotic changes of a measured pressure signal, and physical modeling is difficult. Direct application of One-Nearest-Neighbor Dynamic Time Warping, a common technique for this kind of problem, and other machine learning techniques are shown to fail for this case and new methods to improve the situation are presented. By using relative features describing the difference from the reference case rather than the absolute time series, improvements are made compared to state-of-the-art time series classification algorithms.
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| 6. |
- Ai Lucas, R., et al.
(författare)
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Targeting workload to ameliorate risk of heat stress in industrial sugarcane workers
- 2023
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Ingår i: Scandinavian Journal of Work Environment & Health. - : Scandinavian Journal of Work, Environment and Health. - 0355-3140 .- 1795-990X. ; 49:1, s. 43-52
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Tidskriftsartikel (refereegranskat)abstract
- Objective The aim of this study was to quantify the physiological workload of manual laborers in industrial sugarcane and assess the effect of receiving a rest, shade, and hydration intervention to reduce heat stress exposure risk.Methods In an observational study, physiological workload was evaluated for burned cane cutters (BCC), seed cutters (SC) and drip irrigation repair workers (DIRW) using heart rate (HR) recorded continuously (Polar (R)) across a work shift. Workers' percentage of maximal HR (%HRmax), time spent in different HR zones, and estimated core temperature (ECTemp) were calculated. The effect of increasing rest across two harvests was evaluated for BCC and SC.Results A total of 162 workers participated in this study [52 BCC (all male), 71 SC (13 female) and 39 DIRW (16 female)]. Average %HRmax across a work shift was similar between BCC and SC (BCC: 58%, SC: 59%), but lower in DIRW (51%). BCC and SC spent similar proportions of work shifts at hard/very hard intensities (BCC: 13%, SC: 15%), versus DIRW who worked mostly at light (46%) or light-moderate (39%) intensities. SC maximum ECTemp reached 38.2 degrees C, BCC 38.1 degrees C; while DIRW only reached 37.7 degrees C. Females performed at a higher %HRmax than males across work shifts (SC 64% versus 58%; DIRW 55% versus 49%). An additional rest period was associated with a lower average %HRmax across a work shift in BCC.Conclusion In this setting, BCC and SC both undertake very physiologically demanding work. Females maintained a higher workload than male co-workers. Regulated rest periods each hour, with water and shade access, appears to reduce physiological workload/strain.
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| 7. |
- Andersson, Axel G, et al.
(författare)
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Large difference but high correlation between creatinine and cystatin C estimated glomerular filtration rate in Mesoamerican sugarcane cutters.
- 2022
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Ingår i: Occupational and environmental medicine. - : BMJ. - 1470-7926 .- 1351-0711. ; 79:7, s. 497-502
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Tidskriftsartikel (refereegranskat)abstract
- To explore the relationship between creatinine and cystatin C based estimated glomerular filtration rate (eGFR) in actively working sugarcane cutters.This cohort study included 458 sugarcane cutters from Nicaragua and El Salvador. Serum samples were taken before and at end of harvest seasons and analysed for creatinine and cystatin C. Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formulas were used to calculate eGFRs based on creatinine (eGFRcr), cystatin C (eGFRcys) and both creatinine and cystatin C (eGFRcrcys) at each time point. Bland-Altman plots and paired t-tests were used to compare the difference between eGFRcr and eGFRcys, and the difference in eGFRs between before and at end of the harvest seasons.The mean eGFRcr was higher than eGFRcys in both cohorts; absolute difference 22mL/min/1.73 m2 (95%CI 21 to 23) in Nicaragua and 13mL/min/1.73 m2 (95%CI 11 to 15) in El Salvador. Correlations between eGFRcr and eGFRcys were high, with r=0.69, 0.77 and 0.67 in Nicaragua at pre-harvest, end-harvest and cross-harvest, and r=0.89, 0.89 and 0.49 in El Salvador.Creatinine increases among heat-stressed workers reflect reduced glomerular filtration as estimated using eGFRcys, a marker independent of muscle mass and metabolism. The discrepancy between eGFRcr and eGFRcys may indicate reduced glomerular filtration of larger molecules and/or systemic bias in CKD-EPI performance in this population.
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| 8. |
- Ekström, Ulf, et al.
(författare)
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Insufficient mixing of thawed serum samples leading to erroneous results - experience from a field study and use of a correction procedure
- 2020
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Ingår i: Scandinavian Journal of Clinical & Laboratory Investigation. - : Informa UK Limited. - 0036-5513 .- 1502-7686. ; 80:2, s. 99-105
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Tidskriftsartikel (refereegranskat)abstract
- Incorrect analysis results that are close to expected might not be recognized in scientific studies or routine patient care. In two field studies we obtained unexpected results in a large number of samples. The present study aimed to identify the source of error in the samples from these studies and to validate a method to obtain correct results. Pre-analytical procedures were scrutinized, giving no indications of inappropriate pre-analytical sample handling in the field or during transport in a tropical climate. Using a new set of samples from volunteers in simulation experiments, we observed the known concentration gradient of analytes sampled in gel as well as plain tubes after freezer storage and thawing. Experiments demonstrated that mixing of samples by vortexing alone was not sufficient to disrupt the gradient formed by freezing and thawing, which appeared to cause the problem encountered when we in field studies analyzed and biobanked large sample sets by robot pipetting. A correction procedure was introduced, in which the obtained value of an analyte was multiplied by a correction factor calculated for each sample using the expected sodium level (140 mmol/L) divided by the measured sodium value. When it was validated on results from the simulation experiments, we repeatedly found that the correction lead to results very close to true values for analytes of different size and charge. Usefulness of the procedure was demonstrated when applied to a large set of field study results.
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| 9. |
- Glaser, J., et al.
(författare)
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Preventing kidney injury among sugarcane workers: Promising evidence from enhanced workplace interventions
- 2020
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Ingår i: Occupational and Environmental Medicine. - : BMJ. - 1351-0711 .- 1470-7926. ; 77:8, s. 527-534
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Tidskriftsartikel (refereegranskat)abstract
- Objectives: To assess if improvement of working conditions related to heat stress was associated with improved kidney health outcomes among sugarcane harvest workers in Chichigalpa, Nicaragua, a region heavily affected by the epidemic of chronic kidney disease of non-traditional origin. Methods: Based on our findings during the 2017-2018 harvest (harvest 1), recommendations that enhanced the rest schedule and improved access to hydration and shade were given before the 2018-2019 harvest (harvest 2). Actual work conditions during harvest 2 were then observed. Serum creatinine (SCr) was measured before and at end-harvest, and cross-harvest changes in estimated glomerular filtration rate (eGFR) and incident kidney injury (IKI, ie, SCr increase by ≥0.30 mg/dL or ≥1.5 times the baseline value) were compared between harvest 1 and harvest 2 for three jobs with different physical workloads using regression modelling. Workers who left during harvest were contacted at home, to address the healthy worker selection effect. Results: In burned cane cutters, mean cross-harvest eGFR decreased 6 mL/min/1.73 m2 (95% CI 2 to 9 mL/min/1.73 m2) less and IKI was 70% (95% CI 90% to 50%) lower in harvest 2 as compared with harvest 1 data. No such improvements were seen among seed cutters groups with less successful intervention implementation. Conclusion: Kidney injury risk was again elevated in workers with strenuous jobs. The results support further efforts to prevent kidney injury among sugarcane workers, and other heat-stressed workers, by improving access to water, rest and shade. The distinction between design and implementation of such interventions should be recognised. © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.
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| 10. |
- Glaser, J., et al.
(författare)
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Workplace Intervention for Heat Stress: Essential Elements of Design, Implementation, and Assessment
- 2022
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Ingår i: International Journal of Environmental Research and Public Health. - : MDPI AG. - 1660-4601. ; 19:7
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Tidskriftsartikel (refereegranskat)abstract
- Heat stress is associated with numerous health effects that potentially harm workers, especially in a warming world. This investigation occurred in a setting where laborers are confronted with occupational heat stress from physically demanding work in high environmental temperatures. Collaboration with a major Nicaraguan sugarcane producer offered the opportunity to study interventions to prevent occupational heat-stress-related kidney disease. Two aims for this study of a rest-shade-water intervention program were: (1) describe the evolving intervention, summarize findings that motivated proposed improvements, assess impact of those improvements, and identify challenges to successful implementation and (2) extract primary lessons learned about intervention research that have both general relevance to investigations of work-related disease prevention and specific relevance to this setting. The learning curve for the various stakeholders as well as the barriers to success demonstrate that effectiveness of an intervention cannot be adequately assessed without considerations of implementation. Designing, effectively implementing, and assessing both health impacts and implementation quality is a resource-intensive endeavor requiring a transdisciplinary approach. Both general and specific lessons learned are presented for decisions on study design and study elements, implementation assessment, and management engagement in understanding how productivity and health can be successfully balanced and for building effective communication between investigators and all levels of management.
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