40 research outputs found
Evaluation of the Performance/Energy Overhead in DSP Video Decoding and its Implications
Video decoding is considered as one of the most compute and energy intensive
application in energy constrained mobile devices. Some specific processing
units, such as DSPs, are added to those devices in order to optimize the
performance and the energy consumption. However, in DSP video decoding, the
inter-processor communication overhead may have a considerable impact on the
performance and the energy consumption. In this paper, we propose to evaluate
this overhead and analyse its impact on the performance and the energy
consumption as compared to the GPP decoding. Our work revealed that the GPP can
be the best choice in many cases due to the a significant overhead in DSP
decoding which may represents 30% of the total decoding energy
A new method to enhance of fault detection and diagnosis in gearbox systems
The kurtogram analysis presents some limitations when diagnosing gearbox systems, particularly in time domain. Its envelop signal analysis is not able to detect any defects. This paper presents a new approach to enhance the detection and diagnosis in gearbox systems. This new approach is based on Maximum Correlated Kurtosis Deconvolution combined with Spectral Kurtosis fault diagnosis methodology. This technique allows us to obtain better detection in the gearbox system which is not the case of the spectral kurtosis analysis alone. For this purpose, a dynamical model of a simple stage gearbox is proposed. The approach can detect and identify at early stage the gearbox and also the crack tooth defects
A new method based on fast Kurtogram for the identification of pitting fault versus crack fault in gearbox systems
This paper presents a new technique to diagnose differentially two localized gear tooth faults: a pitting and a crack. These faults could have very different prognoses, but existing diagnostic techniques only indicate the presence of local tooth faults without being able to differentiate between a pitting and a crack. In the aim to diagnose differentially these tow faults, a dynamic model of one stage spur gear is proposed witch make it possible to simulate the effect of pitting and crack faults on the vibration signal. Then, simulated vibration signal is analyzed by using a Fast-Kurtogram technique. This method is suitable for differentiate between a pitting and a crack faults
DyPS: Dynamic Processor Switching for Energy-Aware Video Decoding on Multi-core SoCs
In addition to General Purpose Processors (GPP), Multicore SoCs equipping
modern mobile devices contain specialized Digital Signal Processor designed
with the aim to provide better performance and low energy consumption
properties. However, the experimental measurements we have achieved revealed
that system overhead, in case of DSP video decoding, causes drastic
performances drop and energy efficiency as compared to the GPP decoding. This
paper describes DyPS, a new approach for energy-aware processor switching (GPP
or DSP) according to the video quality . We show the pertinence of our solution
in the context of adaptive video decoding and describe an implementation on an
embedded Linux operating system with the help of the GStreamer framework. A
simple case study showed that DyPS achieves 30% energy saving while sustaining
the decoding performanc
Automatic condition monitoring of electromechanical system based on MCSA, spectral kurtosis and SOM neural network
Condition monitoring and fault diagnosis play the most important role in industrial applications. The gearbox system is an essential component of mechanical system in fault identification and classification domains. In this paper, we propose a new technique which is based on the Fast-Kurtogram method and Self Organizing Map (SOM) neural network to automatically diagnose two localized gear tooth faults: a pitting and a crack. These faults could have very different diagnostics; however, the existing diagnostic techniques only indicate the presence of local tooth faults without being able to differentiate between a pitting and a crack. With the aim to automatically diagnose these two faults, a dynamic model of an electromechanical system which is a simple stage gearbox with and without defect driven by a three phase induction machine is proposed, which makes it possible to simulate the effect of pitting and crack faults on the induction stator current signal. The simulated motor current signal is then analyzed by using a Fast-Kurtogram method. Self-organizing map (SOM) neural network is subsequently used to develop an automatic diagnostic system. This method is suitable for differentiating between a pitting and a crack fault
Gear fault feature extraction and classification of singular value decomposition based on Hilbert empirical wavelet transform
Vibration signal of gearbox systems carries the important dynamic information for fault diagnosis. However, vibration signals always show non stationary behavior and overwhelmed by a large amount of noise make this task challenging in many cases. Thus, a new fault diagnosis method combining the Hilbert empirical wavelet transform (HEWT), the singular value decomposition (SVD) and Elman neural network is proposed in this paper. Vibration signals of normal gear, gear with tooth root crack, gear with chipped tooth in width, gear with chipped tooth in length, gear with missing tooth and gear with general surface wear are collected in different speed and load conditions. HEWT, a new self-adaptive time-frequency analysis, was applied to the vibration signals to obtain the instantaneous amplitude matrices. Singular value vectors, as the fault feature vectors were then acquired by applying the SVD. Last, the Elman neural network was used for automatic gearbox fault identification and classification. Through experimental results, it was concluded that the proposed method can accurately extract and classify the gear fault features under variable conditions. Moreover, the performance of the proposed HEWT-SVD method has an advantage over that of Hilbert-Huang transform (HHT)-SVD, local mean decomposition (LMD)-SVD or wavelet packet transform (WPT)-PCA for feature extraction
A microprocessor-based crane load state monitoring system
122 p. : ill. ; 30 cmThe objective of this research work is to develop a microprocessor-based system to monitor the load stat and insure the safety of a mobile telescopic crane. The need for an automatic and intelligent system that determines the lifted lead, in replacement to the exiting design based on analog electronics, is to serve the national crane manufacturing company ENMTP-CPG, Ain-Smara, Algeria. The conventional microprocessor-based system use the searching technique( look-up tables) to read the allowed load stored in memory