Search results for "Encoder"
showing 10 items of 61 documents
Data-driven Fault Diagnosis of Induction Motors Using a Stacked Autoencoder Network
2019
Current signatures from an induction motor are normally used to detect anomalies in the condition of the motor based on signal processing techniques. However, false alarms might occur if using signal processing analysis alone since missing frequencies associated with faults in spectral analyses does not guarantee that a motor is fully healthy. To enhance fault diagnosis performance, this paper proposes a machinelearning based method using in-built motor currents to detect common faults in induction motors, namely inter-turn stator winding-, bearing- and broken rotor bar faults. This approach utilizes single-phase current data, being pre-processed using Welch’s method for spectral density es…
Multi-Component Fault Detection in Wind Turbine Pitch Systems Using Extended Park's Vector and Deep Autoencoder Feature Learning
2018
Pitch systems are among the wind turbine components with most frequent failures. This article presents a multicomponent fault detection for induction motors and planetary gearboxes of the electric pitch drives using only the three-phase motor line currents. A deep autoencoder is used to extract features from the extended Park's vector modulus of the motor three-phase currents and a support vector machine to classify faults. The methodology is validated in a laboratory setup of a scaled pitch drive, with four commonly occurring faults, namely, the motor stator turns fault, broken rotor bars fault, planetary gearbox bearing fault and planet gear faults, under varying load and speed conditions.
Model-Free Sliding-Mode-Based Detection and Estimation of Backlash in Drives With Single Encoder
2021
Backlash is a frequently encountered problem for various drives, especially those equipped with a single encoder onside of the controlled actuator. This brief proposes a sliding-mode differentiator-based estimation of unknown backlash size while measuring the actuator displacement only. Neither actuator nor load dynamics are explicitly known, while a principal second-order actuator behavior is assumed. We make use of the different perturbation dynamics distinctive for different backlash modes and an unbounded impulse-type perturbation at impact. The latter leads to transient loss of the sliding-mode and allows for detecting an isolated time instant of the backlash occurrence. The proposed m…
On Stability of Virtual Torsion Sensor for Control of Flexible Robotic Joints with Hysteresis
2019
Author's accepted manuscript (postprint). This article has been published in a revised form in Robotica, http://doi.org/10.1017/S0263574719001358. This version is free to view and download for private research and study only. Not for re-distribution or re-use. © 2019 Cambridge University Press. Available from 25/03/2020. Aim of the virtual torsion sensor (VTS) is in observing the nonlinear deflection in the flexible joints of robotic manipulators and, by its use, improving positioning control of the joint load. This model-based approach utilizes the motor-side sensing only and, therefore, replaces the load-side encoders at nearly zero hardware costs. For being applied in the closed control …
Health Indicator for Low-Speed Axial Bearings Using Variational Autoencoders
2020
This paper proposes a method for calculating a health indicator (HI) for low-speed axial rolling element bearing (REB) health assessment by utilizing the latent representation obtained by variational inference using Variational Autoencoders (VAEs), trained on each speed reference in the dataset. Further, versatility is added by conditioning on the speed, extending the VAE to a conditional VAE (CVAE), thereby incorporating all speeds in a single model. Within the framework, the coefficients of autoregressive (AR) models are used as features. The dimensionality reduction inherent in the proposed method lowers the need of expert knowledge to design good condition indicators. Moreover, the sugg…
Combining Auto-Encoder with LSTM for WiFi-Based Fingerprint Positioning
2021
Although indoor positioning has long been investigated by various means, its accuracy remains concern. Several recent studies have applied machine learning algorithms to explore wireless fidelity (WiFi)-based positioning. In this paper, we propose a novel deep learning model which concatenates an auto-encoder with a long short term memory (LSTM) network for the purpose of WiFi fingerprint positioning. We first employ an auto-encoder to extract representative latent codes of fingerprints. Such an extraction is proven to be more reliable than simply using a deep neural network to extract representative features since a latent code can be reverted back to its original input. Then, a sequence o…
An Encrypted Traffic Classification Framework Based on Convolutional Neural Networks and Stacked Autoencoders
2020
In recent years, deep learning-based encrypted traffic classification has proven to be effective; especially, using neural networks to extract features from raw traffic to classify encrypted traffic. However, most of the neural networks need a fixed-sized input, so that the raw traffic need to be trimmed. This will cause the loss of some information; for example, we do not know the number of packets in a session. To solve these problems, a framework, which implements both a convolutional neural network (CNN) and a stacked autoencoder (SAE), is proposed in this paper. This framework uses a CNN to extract high-level features from raw network traffic and uses an SAE to encode the 26 statistica…
Cloud-based elastic architecture for distributed video encoding: Evaluating H.265, VP9, and AV1
2020
Abstract Areas with social and business impact such as entertainment, healthcare, surveillance, and e-learning would benefit from improvements in video coding and transcoding services. New codecs, such as AV1, are being developed to deal with new demands for high video resolutions with bandwidth constraints and quality requirements. However, these new codecs have high computational requirements and new strategies are needed to accelerate their processing. Cloud computing offers interesting features such as on-demand resource allocation, multitenancy, elasticity, and resiliency among others. Deploying video coding and transcoding services on these infrastructures is suitable because it allow…
A GPU-Based DVC to H.264/AVC Transcoder
2010
Mobile to mobile video conferencing is one of the services that the newest mobile network operators can offer to users With the apparition of the distributed video coding paradigm which moves the majority of complexity from the encoder to the decoder, this offering can be achieved by introducing a transcoder This device has to convert from the distributed video coding paradigm to traditional video coding such as H.264/AVC which is formed by simpler decoders and more complex encoders, and allows to the users to execute only the low complex algorithms In order to deal with this high complex video transcoder, this paper introduces a graphics processing unit based transcoder as base station The…
«Motion Estimation Accelerator with User Search Strategy in an RVC Context»
2009
Motion estimation represents a key module in video compression. The RVC context requires proposing a flexible solution for motion estimation. According to the nature of the application, a full search is sometimes not suitable, hence, alternative fast/reduced solutions should be considered. This paper proposes a model and implementation of a flexible motion estimation engine, which can be configured to support any user-defined search strategy. Typically, the computational requirements of the search strategy can be traded with the RD-performance of the obtained video encoder. A CAL dataflow description of the accelerator is proposed so that it can be easily handled in the RVC context. An auto…