Time domain optimization of fault detection systems

2002 ◽  
Author(s):  
X. Ding ◽  
L. Guo
Keyword(s):  
Fault Detection ◽  
Time Domain ◽  
Domain Optimization ◽  
Detection Systems

scholarly journals Fast Time-Domain Simulation for Reliable Fault Detection

2016 ◽  
Author(s):  
Bratislav Tasić ◽  
Jos J. Dohmen ◽  
Rick Janssen ◽  
E. Jan W. ter Maten ◽  
Roland Pulch ◽  
...  
Keyword(s):  
Fault Detection ◽  
Time Domain ◽  
Fast Time ◽  
Time Domain Simulation

Design and multi-objective optimization of magnetorheological damper considering vehicle riding comfort and operation stability

2021 ◽  
pp. 1045389X2110482
Author(s):  
Zhaoxue Deng ◽  
Xinxin Wei ◽  
Xingquan Li ◽  
Shuen Zhao ◽  
Sunke Zhu
Keyword(s):  
Time Domain ◽  
Vehicle Dynamics ◽  
Mr Damper ◽  
Structure Parameters ◽  
Multi Objective Optimization ◽  
Dynamics Model ◽  
Damping Force ◽  
Domain Optimization ◽  
Operation Stability ◽  
The Time Domain

Mostly, magnetorheological (MR) dampers were optimized based on individual performance, without considering the influence of structure parameters change on vehicle performance. Therefore, a multi-objective optimization scheme of MR damper based on vehicle dynamics model was proposed. The finite element method was used to analyze magnetic flux density distribution in tapered damping channel under different structure parameters. Furthermore, the damping force expression of the tapered flow mode MR damper was derived, and the damping force was introduced into the vehicle dynamics model. In order to improve the ride comfort and operation stability of the vehicle, a collaborative optimization platform combining magnetic circuit finite element analysis and vehicle dynamics model was established. Based on this platform, the optimal design variables were determined by comfort and stability sensitivity analysis. The time domain optimization objective and frequency domain optimization objective are proposed simultaneously to overcome the lack of time domain optimization objective. The results show that compared with the time domain optimization and the initial design, the suspension dynamic deflection, tire dynamic load and vehicle body vertical acceleration are decreased after the time-frequency optimization. At the same time, in the frequency domain, the amplitude of vibration acceleration in each working condition is significantly reduced.

Edge-based Explainable Fault Detection Systems for photovoltaic panels on edge nodes

2021 ◽  
Author(s):  
Seshapalli Sairam ◽  
Subathra Seshadhri ◽  
Giancarlo Marafioti ◽  
Seshadhri Srinivasan ◽  
Geir Mathisen ◽  
...  
Keyword(s):  
Fault Detection ◽  
Photovoltaic Panels ◽  
Detection Systems ◽  
Edge Based

An Ensemble Dynamic Time Domain Averaging Method for Gear Fault Detection without Tachometer

2013 ◽  
Vol 572 ◽  
pp. 439-442
Author(s):  
Hui Fang Xiao ◽  
Xiao Jun Zhou ◽  
Yi Min Shao
Keyword(s):  
Fault Detection ◽  
Time Domain ◽  
Time Warping ◽  
Mode Decomposition ◽  
Signal Characteristics ◽  
Gear Fault ◽  
Speed Fluctuation ◽  
Signal Simulation ◽  
Time Domain Averaging ◽  
Dynamic Time

Time Domain Averaging (TDA) has been widely used for fault detection. However, it cannot reveal signal characteristics accurately in conditions of speed fluctuation and no tachometer. Empirical mode decomposition (EMD) helps to extract physically meaningful components from the singles. Dynamic Time Warping (DTW) can solve inconsistence in signal lengths per rotation due to speed fluctuation. Utilizing the advantages of EMD, DTW and TDA, an ensemble dynamic-time domain averaging (ED-TDA) algorithm is proposed for gear fault detection without tachometer. First, the selected intrinsic mode function (IMF) and the envelop signals are equal-spaced intercepted. Then, the phase accumulation error among the envelop signal segments are estimated by the DTW, which are further used to compensate the IMF segments. Finally, the compensated IMF segments are averaged to obtain the feature signal. Simulation and experimental results validate the efficiency of the algorithm in extracting feature signal from collected speed fluctuating signal without tachometer.

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