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107 results for “Actuation”
Combining Model-Based and Feature-Driven Diagnosis Approaches – A Case Study on Electromechanical Actuators
Model-based diagnosis typically uses analytical redundancy to compare predictions from a model against observations from the system being diagnosed. However this approach does not work very well when it is not feasible to create analytic relations describing all the observed data, e.g., for vibration data which is usually sampled at very high rates and requires very detailed finite element models to describe its behavior. In such cases, features (in time and frequency domains) that contain diagnostic information are extracted from the data. Since this is a computationally intensive process, it is not efficient to extract all the features all the time. In this paper we present an approach that combines the analytic model-based and feature-driven diagnosis approaches. The analytic approach is used to reduce the set of possible faults and then features are chosen to best distinguish among the remaining faults. We describe an implementation of this approach on the Flyable Electro-mechanical Actuator (FLEA) test bed.
Experimental Data Collection and Modeling for Nominal and Fault Conditions on Electro-Mechanical Actuators
Being relatively new to the field, electromechanical actuators in aerospace applications lack the knowledge base compared to ones accumulated for the other actuator types, especially when it comes to fault detection and characterization. Lack of health monitoring data from fielded systems and prohibitive costs of carrying out real flight tests push for the need of building system models and designing affordable but realistic experimental setups. This paper presents our approach to accomplish a comprehensive test environment equipped with fault injection and data collection capabilities. Efforts also include development of multiple models for EMA operations, both in nominal and fault conditions that can be used along with measurement data to generate effective diagnostic and prognostic estimates. A detailed description has been provided about how various failure modes are inserted in the test environment and corresponding data is collected to verify the physics based models under these failure modes that have been developed in parallel. A design of experiment study has been included to outline the details of experimental data collection. Furthermore, some ideas about how experimental results can be extended to real flight environments through actual flight tests and using real flight data have been presented. Finally, the roadmap leading from this effort towards developing successful prognostic algorithms for electromechanical actuators is discussed.*
New Sensing Application to Diagnose Power Semiconductor Aging in Actuator Power Drive Systems
This paper presents a new sensing application to diagnose power semiconductor aging in power drive systems. It has been shown previously that device parasitic characteristics change during the aging process which results in detectable changes in their frequency response. This change is manifested in the current signal at very high frequencies. Therefore, using a wideband AC current sensor, high frequency components of the current can be acquired, providing a way to detect device aging.
Experimental Validation of a Prognostic Health Management System for Electro-Mechanical Actuators
The work described herein is aimed to advance prognostic health management solutions for electro-mechanical actuators and, thus, increase their reliability and attractiveness to designers of the next generation aircraft and spacecraft. In pursuit of this goal the team adopted a systematic approach by starting with EMA FMECA reviews, consultations with EMA manufacturers, and extensive literature reviews of previous efforts. Based on the acquired knowledge, nominal/off-nominal physics models and prognostic health management algorithms were developed. In order to aid with development of the algorithms and validate them on realistic data, a testbed capable of supporting experiments in both laboratory and flight environment was developed. Test actuators with architectures similar to potential flight-certified units were obtained for the purposes of testing and realistic fault injection methods were designed. Several hundred fault scenarios were created, using permutations of position and load profiles, as well as fault severity levels. The diagnostic system was tested extensively on these scenarios, with the test results demonstrating high accuracy and low numbers of false positive and false negative diagnoses. The prognostic system was utilized to track fault progression in some of the fault scenarios, predicting the remaining useful life of the actuator. A series of run-to-failure experiments were conducted to validate its performance, with the resulting error in predicting time to failure generally lesser than 10% error. While a more robust validation procedure would require dozens more experiments executed under the same conditions (and, consequently, more test articles destroyed), the current results already demonstrate the potential for predicting fault progression in this type of devices. More prognostic experiments are planned for the next phase of this work, including investigation and comparison of other prognostic algorithms (such as various types of Particle Filter and GPR), addition of new fault types, and execution of prognostic experiments in flight environment.
A Diagnostic Approach for Electro-Mechanical Actuators in Aerospace Systems
Electro-mechanical actuators (EMA) are finding increasing use in aerospace applications, especially with the trend towards all all-electric aircraft and spacecraft designs. However, electro-mechanical actuators still lack the knowledge base accumulated for other fielded actuator types, particularly with regard to fault detection and characterization. This paper presents a thorough analysis of some of the critical failure modes documented for EMAs and describes experiments conducted on detecting and isolating a subset of them. The list of failures has been prepared through an extensive Failure Modes and Criticality Analysis (FMECA) reference, literature review, and accessible industry experience. Methods for data acquisition and validation of algorithms on EMA test stands are described. A variety of condition indicators were developed that enabled detection, identification, and isolation among the various fault modes. A diagnostic algorithm based on an artificial neural network is shown to operate successfully using these condition indicators and furthermore, robustness of these diagnostic routines to sensor faults is demonstrated by showing their ability to distinguish between them and component failures. The paper concludes with a roadmap leading from this effort towards developing successful prognostic algorithms for electromechanical actuators.
Dataset for the publication "Deployment of an electrocorticography system assisted with a soft robotic actuation"
<p>Electrocorticography (ECoG) is a minimally invasive approach frequently used clinically to map epileptogenic regions of the brain and facilitate lesion resection surgery, and increasingly explored in brain-machine interface applications. Current devices display limitations that require trade-offs between cortical surface coverage, spatial electrode resolution, aesthetic, and risk consequences, and often limit the use of the mapping technology to the operating room. In this work, we report on a scalable technique for the fabrication of large-area soft robotic electrode arrays and their deployment on the cortex through a square centimeter burr hole using a pressure-driven actuation mechanism called eversion. The deployable system consists of up to six pre-folded soft legs and it is placed subdurally on the cortex using an aqueous pressurized solution and secured to the pedestal on the rim of the small craniotomy. Each leg contains soft, microfabricated electrodes and strain sensors for real-time deployment monitoring. In a proof-of-concept acute surgery, a soft robotic electrode array was successfully deployed on the cortex of a minipig to record sensory cortical activity. This soft robotic neurotechnology opens promising avenues for minimally invasive cortical surgery and applications related to neurological disorders such as motor and sensory deficits.</p>
Supplementary Materials for Sensuator: A Hybrid Sensor-Actuator Approach to Soft Robotic Proprioception Using Recurrent Neural Networks
<p>Videos accompanying the paper Sensuator: A Hybrid Sensor-Actuator Approach to Soft Robotic Proprioception Using Recurrent Neural Networks</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.