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

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.

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

Sensors ◽

10.3390/s20133635 ◽

2020 ◽

Vol 20 (13) ◽

pp. 3635 ◽

Cited By ~ 1

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.

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Power Loss Prediction for Aging Characteristics and Condition Monitoring for Parallel-Connected Power Modules Using Nonlinear Autoregressive Neural Network

Complexity ◽

10.1155/2019/8759873 ◽

2019 ◽

Vol 2019 ◽

pp. 1-10

Author(s):

Shengyou Xu ◽

Xin Yang ◽

Li Ran ◽

Minyou Chen ◽

Wei Lai

Keyword(s):

Neural Network ◽

Real Time ◽

Condition Monitoring ◽

Power Loss ◽

Junction Temperature ◽

Monitoring Method ◽

Current Sharing ◽

Power Modules ◽

The Impact ◽

Nonlinear Autoregressive

Power modules connected in parallel may have different electrothermal performance variances resulting from aging because of the nonuniform rate of degradation; different electrothermal performance variances mean different current sharing, different junction temperature, and power losses, which will directly influence the overall characteristics of them. Thus, it is essential to monitor the condition and evaluate the degradation grade to improve the reliability of large-scale power modules. In this paper, the impact of thermal resistance difference on current sharing, junction temperature, and power loss of parallel-connected power modules has been discussed and analyzed. Additionally, a methodology is proposed for condition monitoring and evaluation of the power modules without intruding them by recognizing the increase in external power loss due to internal degradation from aging. In this method, power modules are deemed as a whole system considering only external factors associated with them, all important electrical and thermal parameters are classified as the inputs, and power loss is considered as the output. Firstly, power dissipation is predicted by models using NARX (nonlinear autoregressive with exogenous input) neural network. Then, a monitoring method is illustrated based on the prediction model; a reasonable criterion for the error between the normal and the predicted real-time power loss is established. Finally, the real-time condition and the degradation grade of aging can be evaluated so that the operator can take suitable operating measures by means of this approach. Experimental results validated the effectiveness of the proposed methodology.

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Machine cutting tool condition monitoring method of aeroplane composite material processing based on artificial neural networks

Advanced Computer Control ◽

10.2495/icacc130991 ◽

2014 ◽

Author(s):

Xiaolin Liu ◽

Chunyu Mu ◽

Jianting Wang ◽

Kun Yuan

Keyword(s):

Neural Networks ◽

Material Processing ◽

Composite Material ◽

Artificial Neural Networks ◽

Condition Monitoring ◽

Cutting Tool ◽

Tool Condition Monitoring ◽

Monitoring Method ◽

Tool Condition ◽

Artificial Neural

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A Multi-parameter Joint Warning Mechanism for Physical Condition Monitoring System in Physical Education

Recent Patents on Engineering ◽

10.2174/1872212113666190911115748 ◽

2020 ◽

Vol 14 (1) ◽

pp. 113-119

Author(s):

Zhang Su

Keyword(s):

Physical Education ◽

False Alarm ◽

Real Time ◽

Monitoring System ◽

Condition Monitoring ◽

Early Warning ◽

Physical Condition ◽

School Students ◽

Early Warning Mechanism ◽

Number Of Segments

Background: In recent years, sudden deaths of primary and secondary school students caused by sports activities have drawn great attention in education and medical circles. It is necessary for schools to monitor the physical condition of the students in order to reasonably set the duration of their physical activity. At present, the physical condition monitoring instruments used in various hospitals are expensive, bulky, and difficult to operate, and the detection process is complicated. Therefore, existing approaches cannot meet the needs of physical education teachers on campus for detecting the physical condition of students. Methods: This study designs a portable human-physiological-state monitoring and analysis system. Real-time communication between a wearable measurement device and a monitoring device can be ensured by real-time detection of the environment and power control of the transmitted signal. Results: From a theoretical point of view, the larger the number of segments M, the more significantly the reduction of false alarm probability. The simulation results also show this fact. Compared with the conventional early warning mechanism, the probability of a false alarm for the proposed system is lower, and the greater the number of segments, the faster its reaction speed. Conclusion: The portable monitoring system of student physical condition for use in physical education of primary and middle school students proposed in this paper ensures real-time monitoring of the members within the system in an open environment, and further proposes an early warning mechanism for combining multiple vital sign parameters. In addition, the proposed system functions faster; the average early warning time required is only one-quarter of that of the conventional system.

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Online Real-Time Monitoring System Through Using Adaptive Angular-Velocity VKF Order Tracking

Volume 1: 24th Conference on Mechanical Vibration and Noise, Parts A and B ◽

10.1115/detc2012-70506 ◽

2012 ◽

Cited By ~ 1

Author(s):

Ting-Chi Yeh ◽

Min-Chun Pan

Keyword(s):

Angular Velocity ◽

Real Time ◽

Monitoring System ◽

Condition Monitoring ◽

Theoretical Derivation ◽

Periodic Loading ◽

Order Tracking ◽

Computation Scheme ◽

Condition Monitoring System ◽

Operation Status

When rotary machines are running, acousto-mechanical signals acquired from the machines are able to reveal their operation status and machine conditions. Mechanical systems under periodic loading due to rotary operation usually respond in measurements with a superposition of sinusoids whose frequencies are integer (or fractional integer) multiples of the reference shaft speed. In this study we built an online real-time machine condition monitoring system based on the adaptive angular-velocity Vold-Kalman filtering order tracking (AV2KF_OT) algorithm, which was implemented through a DSP chip module and a user interface coded by the LabVIEW®. This paper briefly introduces the theoretical derivation and numerical implementation of computation scheme. Experimental works justify the effectiveness of applying the developed online real-time condition monitoring system. They are the detection of startup on the fluid-induced instability, whirl, performed by using a journal-bearing rotor test rig.

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