The use of musculoskeletal simulation software has become a useful tool for modelling joint and muscle forces during human activity, including in reduced gravity because direct experimentation is difficult. Knowledge of muscle and joint loads can better inform the design of exercise protocols and exercise countermeasure equipment. In this study, the LifeModeler™ (San Clemente, CA, USA) biomechanics simulation software was used to model a squat exercise. The initial model using default parameters yielded physiologically reasonable hip-joint forces but no activation was predicted in some large muscles such as rectus femoris, which have been shown to be active in 1-g performance of the activity. Parametric testing was conducted using Monte Carlo methods and combinatorial reduction to find a muscle parameter set that more closely matched physiologically observed activation patterns during the squat exercise. The rectus femoris was predicted to peak at 60.1% activation in the same test case compared to 19.2% activation using default parameters. These results indicate the critical role that muscle parameters play in joint force estimation and the need for exploration of the solution space to achieve physiologically realistic muscle activation.
System Health Management (SHM) systems have found their way into many safety-critical aerospace and industrial applications. A SHM system processes readings from sensors throughout the system and uses a Health Management (HM) model to detect and identify potential faults (diagnosis) and to predict possible failures in the near future (prognosis). It is essential that a SHM system, which monitors a safety-critical component, must be at least as reliable and safe as the component itself-false alarms or missed adverse events can potentially result in catastrophic failures. The SHM system including the HM model, a piece of software, must therefore undergo rigorous Verification and Validation (V&V).In this paper, we will describe an advanced technique for the analysis and V&V of Health Management models. Although our technique is generally applicable, we investigate in this paper HM models in the form of Bayesian networks (BNs). BNs are a powerful modeling paradigm to express notions of cause and effect, probability, and reliability. A BN model typically contains many parameters (e.g., thresholds for discretization and conditional probability tables); they need to be set carefully for reliable and accurate HM reasoning. We are investigating the use of Parametric Testing (PT), which uses a combination of n-factor and Monte Carlo methods, to exercise our HM model with variations of perturbed parameters. Multivariate clustering on the analysis is used to automatically find structure in the data set and to support visualization. Our approach can yield valuable insights regarding the sensitivity of parameters and helps to detect safety margins and boundaries.As a case study we use HM models from the NASA Advanced Diagnostics and Prognostics Testbed (ADAPT), which is a realistic hardware setup for a distributed power system as found in spacecraft or aircraft.
Abstract-This paper considers the problem of providing, for computational processes, soft real-time (or reactive) response without the use of a hard real-time operating system. In particular, we focus on the problem of reactively computing fault diagnosis by means of different Bayesian network inference algorithms on non-real-time operating systems where low-criticality (background) process activity and system load is unpredictable.To address this problem, we take in this paper a reconfigurable adaptive control approach. Computation time is modeled using an ARX model where the input consists of the maximum number of background processes allowed to run at any given time. To ensure that the reactive (high-criticality) diagnosis is computed within a set time frame, we introduce a minimum degree pole placement controller to impose a limit on the maximum number of low-criticality processes. Experimentally, we perform electrical power system diagnosis using a Bayesian network model of and data from a NASA electrical power network. The Bayesian network inference algorithms likelihood weighting and junction tree propagation are successfully applied and changed mid-simulation to investigate how inference computation time changes in an unpredictable operating system, as well as how the controller reacts to inference algorithm changes.
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