scholarly journals Modal Experiment on Structural Condition Monitoring Using Cumulative Harmonic Analysis

2012 ◽  
Vol 2012 ◽  
pp. 1-6 ◽  
Author(s):  
Yoshinori Takahashi ◽  
Satoru Goto ◽  
Mikio Tohyama
Keyword(s):  
Harmonic Analysis ◽  
Condition Monitoring ◽  
Spectral Distribution ◽  
Time Window ◽  
Short Interval ◽  
Structural Condition ◽  
Scale Model ◽  
Transient Vibration ◽  
Monitoring Method ◽  
Spectral Peaks

This paper describes a cumulative harmonic analysis (CHA) that replaces the stepwise time window function of Berman and Fincham's cumulative spectral analysis with a spectral accumulation function, thereby enabling a new structural health monitoring method. CHA estimates and visualizes system damping conditions without the need of transient-vibration records. The damping conditions are closely related to the spectral distribution around the dominant spectral peaks due to structural resonance. This type of spectral distribution can be visualized with CHA even within a short interval of random vibration samples. The effect of CHA on monitoring the damping conditions was reported by the authors in a previous study. In the current study, the authors confirmed the usefulness of CHA for structural condition monitoring by conducting scale-model experiments.

scholarly journals Structural Condition Monitoring by Cumulative Harmonic Analysis of Random Vibration

2008 ◽  
Vol 2008 ◽  
pp. 1-8 ◽  
Author(s):  
Yoshinori Takahashi ◽  
Toru Taniguchi ◽  
Mikio Tohyama
Keyword(s):  
Harmonic Analysis ◽  
Condition Monitoring ◽  
Spectral Distribution ◽  
Random Vibration ◽  
Structural Condition ◽  
Transient Vibration ◽  
Short Intervals ◽  
Spectral Peaks ◽  
Accumulation Function

Analysis of signals based on spectral accumulation has great potential for enabling the condition of structures excited by natural forces to be monitored using random vibration records. This article describes cumulative harmonic analysis (CHA) that was achieved by introducing a spectral accumulation function into Berman and Fincham's conventional cumulative analysis, thus enabling potential new areas in cumulative analysis to be explored. CHA effectively enables system damping and modal overlap conditions to be visualized without the need for transient-vibration records. The damping and modal overlap conditions lead to a spectral distribution around dominant spectral peaks due to structural resonance. This distribution can be revealed and emphasized by CHA records of magnitude observed even within short intervals in stationary random vibration samples.

Time Window from Visual Images to Visual Short-Term Memory: Consolidation or Integration?

2004 ◽  
Vol 51 (1) ◽  
pp. 45-51 ◽  
Author(s):  
Yuhong Jiang
Keyword(s):  
Short Term Memory ◽  
Time Window ◽  
Short Interval ◽  
Visual Images ◽  
Short Term ◽  
Temporal Window ◽  
Term Memory ◽  
Visual Short Term Memory ◽  
Probe Task ◽  
Long Time

Abstract. When two dot arrays are briefly presented, separated by a short interval of time, visual short-term memory of the first array is disrupted if the interval between arrays is shorter than 1300-1500 ms ( Brockmole, Wang, & Irwin, 2002 ). Here we investigated whether such a time window was triggered by the necessity to integrate arrays. Using a probe task we removed the need for integration but retained the requirement to represent the images. We found that a long time window was needed for performance to reach asymptote even when integration across images was not required. Furthermore, such window was lengthened if subjects had to remember the locations of the second array, but not if they only conducted a visual search among it. We suggest that a temporal window is required for consolidation of the first array, which is vulnerable to disruption by subsequent images that also need to be memorized.

scholarly journals Power-Based Non-Intrusive Condition Monitoring for Terminal Device in Smart Grid

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3635 ◽  
Author(s):  
Guoming Zhang ◽  
Xiaoyu Ji ◽  
Yanjie Li ◽  
Wenyuan Xu
Keyword(s):  
Smart Grid ◽  
Power Consumption ◽  
Real Time ◽  
Condition Monitoring ◽  
Resource Constraints ◽  
Detection System ◽  
Stable Operation ◽  
Detection Accuracy ◽  
Monitoring Method ◽  
Dsp Processors

As a critical component in the smart grid, the Distribution Terminal Unit (DTU) dynamically adjusts the running status of the entire smart grid based on the collected electrical parameters to ensure the safe and stable operation of the smart grid. However, as a real-time embedded device, DTU has not only resource constraints but also specific requirements on real-time performance, thus, the traditional anomaly detection method cannot be deployed. To detect the tamper of the program running on DTU, we proposed a power-based non-intrusive condition monitoring method that collects and analyzes the power consumption of DTU using power sensors and machine learning (ML) techniques, the feasibility of this approach is that the power consumption is closely related to the executing code in CPUs, that is when the execution code is tampered with, the power consumption changes accordingly. To validate this idea, we set up a testbed based on DTU and simulated four types of imperceptible attacks that change the code running in ARM and DSP processors, respectively. We generate representative features and select lightweight ML algorithms to detect these attacks. We finally implemented the detection system on the windows and ubuntu platform and validated its effectiveness. The results show that the detection accuracy is up to 99.98% in a non-intrusive and lightweight way.

Condition Monitoring of Tank Bore Surface Characteristics by an Integrated Measurement Technique

2021 ◽  
Vol 13 (2) ◽  
Author(s):  
Ramesh Shanmugam ◽  
D. Dinakaran ◽  
D.G. Harris Samuel
Keyword(s):  
Real Time ◽  
Condition Monitoring ◽  
Measurement Technique ◽  
Great Degree ◽  
Surface Characteristics ◽  
Integrated Approach ◽  
Thermal Camera ◽  
Monitoring Method ◽  
Measuring Surface ◽  
Combat Effectiveness

Accuracy and safety of tank guns are dependent a great degree on the condition of its gun bore. Many parameters affect accuracy and safety and have strong and complex interdependencies. While it is extremely difficult to monitor all these parameters during battle conditions, it is also essential to enhance the accuracy of the gun by measuring and compensating these parameters. Among all, bore wear and bore centreline are predominant factors. The surface characteristics of the bore also are indicative of potential accidents/deterioration, which should be monitored continuously. Hence, condition monitoring of tank gun bore characteristics in near real-time is an impending need with huge potential for enhancing the combat effectiveness of tank formations. This paper analyses various bore parameters affecting accuracy and safety and proposes a comprehensive condition monitoring method that uses vision camera, thermal camera and mechanical profiler. This integrated approach provides enhanced accuracy in measuring surface characteristics of tank bore that has been partially validated.

scholarly journals Condition Monitoring of Wind Turbine Based on Copula Function and Autoregressive Neural Network

2018 ◽  
Vol 198 ◽  
pp. 04008
Author(s):  
Zhongshan Huang ◽  
Ling Tian ◽  
Dong Xiang ◽  
Sichao Liu ◽  
Yaozhong Wei
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Wind Turbine ◽  
Condition Monitoring ◽  
Copula Function ◽  
Single Step ◽  
Fusion Model ◽  
Monitoring Method ◽  
Monitoring Model ◽  
Autoregressive Neural Network

The traditional wind turbine fault monitoring is often based on a single monitoring signal without considering the overall correlation between signals. A global condition monitoring method based on Copula function and autoregressive neural network is proposed for this problem. Firstly, the Copula function was used to construct the binary joint probability density function of the power and wind speed in the fault-free state of the wind turbine. The function was used as the data fusion model to output the fusion data, and a fault-free condition monitoring model based on the auto-regressive neural network in the faultless state was established. The monitoring model makes a single-step prediction of wind speed and power, and statistical analysis of the residual values of the prediction determines whether the value is abnormal, and then establishes a fault warning mechanism. The experimental results show that this method can provide early warning and effectively realize the monitoring of wind turbine condition.

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