Statistical gear health analysis which is robust to fluctuating loads and operating speeds

Condition-based maintenance is concerned with the collection and interpretation of data to support maintenance decisions. The non-intrusive nature of vibration data enables the monitoring of enclosed systems such as gearboxes. It remains a significant challenge to analyze vibration data that are generated under fluctuating operating conditions. This is especially true for situations where relatively little prior knowledge regarding the specific gearbox is available. It is therefore investigated how an adaptive time series model, which is based on Bayesian model selection, may be used to remove the non-fault related components in the structural response of a gear assembly to obtain a residual signal which is robust to fluctuating operating conditions. A statistical framework is subsequently proposed which may be used to interpret the structure of the residual signal in order to facilitate an intuitive understanding of the condition of the gear system. The proposed methodology is investigated on both simulated and experimental data from a single stage gearbox. © 2011 Elsevier Ltd. All rights reserved.

Preliminary model development for predicting strength and stiffness of cement-stabilized soils using artificial neural networks

This paper presents ongoing work on data collection and collation from a large number of laboratory cement-stabilization projects worldwide. The aim is to employ Artificial Neural Networks (ANN) to establish relationships between variables, which define the properties of cement-stabilized soils, and the two parameters determined by the Unconfined Compression Test, the Unconfined Compressive Strength (UCS), and stiffness, using E50 calculated from UCS results. Bayesian predictive neural network models are developed to predict the UCS values of cement-stabilized inorganic clays/silts, as well as sands as a function of selected soil mix variables, such as grain size distribution, water content, cement content and curing time. A model which can predict the stiffness values of cement-stabilized clays/silts is also developed and compared to the UCS model. The UCS model results emulate known trends better and provide more accurate estimates than the results from the E50 stiffness model. © 2013 American Society of Civil Engineers.

Assessment of dry weight by monitoring changes in blood volume during hemodialysis using Crit-Line.

BACKGROUND: Routine assessment of dry weight in chronic hemodialysis patients relies primarily on clinical evaluation of patient fluid status. We evaluated whether measurement of postdialytic vascular refill could assist in the assessment of dry weight. METHODS: Twenty-eight chronic, stable hemodialysis patients were studied during routine treatment sessions using constant dialysate temperature and dialysate sodium concentration, and relative changes in blood volume were monitored using Crit-Line III monitors throughout this study. The study was divided into three phases. Phase 1 studies evaluated the time-dependence of vascular compartment refill after completion of hemodialysis. Phase 2 studies evaluated the relationships in patient subgroups between intradialytic changes in blood volume and the presence of postdialytic vascular compartment refill during that last 10 minutes of hemodialysis after stopping ultrafiltration. Phase 3 studies evaluated the extent of dry weight changes following the application of a protocol for blood volume reduction, postdialytic vascular compartment refill, and correlation with clinical evidence of intradialytic hypovolemia and/or postdialytic fatigue. Phase 3 included anywhere from three to five treatments. RESULTS: Phase 1 studies demonstrated that despite interpatient variability in the magnitude of postdialytic vascular compartment refill, when significant refill was evident, it always continued for at least 30 minutes. However, the majority of refill took place within 10 minutes postdialysis. Phase 2 studies identified 3 groups of patients: those who exhibited intradialytic reductions in blood volume but not postdialytic vascular compartment refill (group 1), those who exhibited intradialytic reductions in blood volume and postdialytic vascular compartment refill (group 2), and those whose blood volume did not change substantially during hemodialysis treatment (group 3). In phase 3 studies, use of an ultrafiltration protocol for blood volume reduction and monitoring of postdialytic vascular compartment refill combined with clinical assessment of hypovolemia and postdialytic fatigue demonstrated that patients often had a clinical dry weight assessment which was too low or too high. In all 28 patients studied, dry weight was either increased or decreased following use of this protocol. CONCLUSION: Determination of the extent of both intradialytic decreases in blood volume and postdialytic vascular compartment refill, combined with clinical assessment of intradialytic hypovolemia and postdialytic fatigue, can help assess patient dry weight and optimize volume status while reducing dialysis associated morbidity. The number of hospital admissions due to fluid overload may be reduced.

Achievements and challenges in machine vision-based inspection of large concrete structures

Large concrete structures need to be inspected in order to assess their current physical and functional state, to predict future conditions, to support investment planning and decision making, and to allocate limited maintenance and rehabilitation resources. Current procedures in condition and safety assessment of large concrete structures are performed manually leading to subjective and unreliable results, costly and time-consuming data collection, and safety issues. To address these limitations, automated machine vision-based inspection procedures have increasingly been proposed by the research community. This paper presents current achievements and open challenges in vision-based inspection of large concrete structures. First, the general concept of Building Information Modeling is introduced. Then, vision-based 3D reconstruction and as-built spatial modeling of concrete civil infrastructure are presented. Following that, the focus is set on structural member recognition as well as on concrete damage detection and assessment exemplified for concrete columns. Although some challenges are still under investigation, it can be concluded that vision-based inspection methods have significantly improved over the last 10 years, and now, as-built spatial modeling as well as damage detection and assessment of large concrete structures have the potential to be fully automated.