12 research outputs found
Next Generation Pump Systems Enable New Opportunities For Asset Management And Economic Optimization
No full textLecturepg. 9There is growing interest in cost-effective techniques that can detect the earliest stage of degradation or malfunction and predict machinery failure. New diagnostic and prognostic techniques may be effectively coupled with novel control techniques in the context of an intelligent motor-pump-control system. An integrated intelligent system is described for pumping applications that can sense the operating condition and health of the components of a hydraulic system and automatically change the operation of the motor-pump system. The change in control is goal-directed whereby the prescribed operating change is intended to achieve previously defined operating goals or performance objectives. The operation of an intelligent motor-pump system in an integrated, coordinated manner can achieve unprecedented and important capabilities for protecting critical processes, process equipment, operations personnel, and the environment. This system also provides a basis for dynamic optimization of critical operating and financial objectives such as longest mean time between failure, lowest life-cycle cost, or lowest cost per gallon pumped. Future intelligent systems will provide the basis for next generation condition-based monitoring systems, future distributed intelligent systems, and autonomous, agent-based systems
Managed Complexity in An Agent-based Vent Fan Control System Based on Dynamic Re-configuration
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Neural net based torque sensor using birefringent materials Sensors Actuators A 70 243–9
No full textAbstract Photoelasticity can be used to accurately measure surface strains or stresses in a part or structure. In this paper we describe the use of a photoelastic transducer and neural net image processing to estimate the torque of stationary and rotating shafts. A strain sensitive (photoelastic) plastic cylinder is attached to the shaft and illuminated by polarized light. As the shaft torque varies the photoelastic plastic displays the corresponding shaft strain as a 2-D fringe pattern when viewed through an optical polarizer. The strain that causes this observed optical pattern is a complex function of the torque applied to the shaft. In this paper, we describe the use of neural net image processing to determine function value to realize an optical torque sensor. A CCD camera/image processing system was used to acquire and process the optical patterns. A neural net torque estimator was trained with these fringe patterns and tested against a laboratory strain gauge torque sensor. Our experiments show that the neural net torque estimator can accurately estimate (to within a few percent) the applied torque for both static and slowly rotating (<20 rpm) shafts.
Test and Validation of a Smart Exercise Bike for Motor Rehabilitation in Individuals With Parkinson’s Disease
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Analysis of Movement Entropy during Community Dance Programs for People with Parkinson’s Disease and Older Adults: A Cohort Study
No full textDance therapy can improve motor skills, balance, posture, and gait in people diagnosed with Parkinson’s disease (PD) and healthy older adults (OA). It is not clear how specific movement patterns during dance promote these benefits. The purpose of this cohort study was to identify differences and complexity in dance movement patterns among different dance styles for PD and OA participants in community dance programs using approximate entropy (ApEn) analysis. The hypothesis was that PD participants will show greater ApEn during dance than OA participants and that the unique dance style of tango with more pronounced foot technique and sharp direction changes will show greater ApEn than smoother dance types such as foxtrot and waltz characterized by gradual changes in direction and gliding movement with rise and fall. Individuals participated in one-hour community dance classes. Movement data were captured using porTable 3D motion capture sensors attached to the arms, torso and legs. Classes were also video recorded to assist in analyzing the dance steps. Movement patterns were captured and ApEn was calculated to quantify the complexity of movements. Participants with PD had greater ApEn in right knee flexion during dance movements than left knee flexion (p = 0.02), greater ApEn of right than left hip flexion (p = 0.05), and greater left hip rotation than right (p = 0.03). There was no significant difference in ApEn of body movements (p > 0.4) or mean body movements (p > 0.3) at any body-segment in OA. ApEn analysis is valuable for quantifying the degree of control and predictability of dance movements and could be used as another tool to assess the movement control of dancers and aid in the development of dance therapies
