Augmented-Reality-Based Mechanical Fault Diagnosis Method

2014 ◽  
Vol 1044-1045 ◽  
pp. 720-722
Author(s):  
Hong Lei Jing ◽  
Jing Nie ◽  
Nian Zhang
Keyword(s):  
Fault Diagnosis ◽  
Augmented Reality ◽  
Rapid Development ◽  
Modern Society ◽  
Economic Losses ◽  
Stable Operation ◽  
Wear And Tear ◽  
Diagnosis Method ◽  
High Degree

With the rapid development of modern society, the industrial mechanized production reached unprecedented climax in this era. Science and technology advance increasingly, modern equipment from structure to function tends to be complex and improved, and gradually achieve a high degree of automation. However, due to the inevitable factors such as wear and tear, abrasion and chemicals infection, machinery equipment will inevitably appear unforeseen fault, causing the machine to detract from the performance, or even causing serious economic losses. Therefore, mechanical fault diagnosis can reduce equipment accident rate and ensure the long-term stable operation of the device. And applying the augmented reality to machinery fault diagnosis method research can maximize the efficiency of mechanical fault diagnosis and equipment efficiency. This article explores the prospects for the development of mechanical fault diagnosis methods based on the theoretical basis and application value of augmented reality.

scholarly journals An Intelligent Fault Diagnosis Method for Transformer Based on IPSO-gcForest

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Kezhen Liu ◽  
Shizhe Wu ◽  
Zhao Luo ◽  
Zeweiyi Gongze ◽  
Xianlong Ma ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Power System ◽  
Characteristic Parameter ◽  
Economic Losses ◽  
Stable Operation ◽  
Diagnosis Method ◽  
Transformer Fault ◽  
Ipso Algorithm ◽  
Better Than ◽  
Fault Characteristic

Transformers are the main equipment for power system operation. Undiagnosed faults in the internal components of the transformer will increase the downtime during operation and cause significant economic losses. Efficient and accurate transformer fault diagnosis is an important part of power grid research, which plays a key role in the safe and stable operation of the power system. Existing traditional transformer fault diagnosis methods have the problems of low accuracy, difficulty in effectively processing fault characteristic information, and superparameters that adversely affect transformer fault diagnosis. In this paper, we propose a transformer fault diagnosis method based on improved particle swarm optimization (IPSO) and multigrained cascade forest (gcForest). Considering the correlation between the characteristic gas dissolved in oil and the type of fault, firstly, the noncode ratios of the characteristic gas dissolved in the oil are determined as the characteristic parameter of the model. Then, the IPSO algorithm is used to iteratively optimize the parameters of the gcForest model and obtain the optimal parameters with the highest diagnostic accuracy. Finally, the diagnosis effect of IPSO-gcForest model under different characteristic parameters and size samples is analyzed by identification experiments and compared with that of various methods. The results show that the diagnostic effect of the model with noncode ratios as the characteristic parameter is better than DGA data, IEC ratios, and Rogers ratios. And the IPSO-gcForest model can effectively improve the accuracy of transformer fault diagnosis, thus verifying the feasibility and effectiveness of the method.

Hierarchical Information Fault Diagnosis Method for Power System Based on Fireworks Algorithm

2021 ◽  
Author(s):  
Feng Haixun ◽  
Yi Kenan ◽  
Jia Zihang ◽  
Bi Huijing
Keyword(s):  
Fault Diagnosis ◽  
Power System ◽  
Fault Location ◽  
Stable Operation ◽  
Specific Situation ◽  
Fireworks Algorithm ◽  
Protection Information ◽  
Diagnosis Method ◽  
Important Means ◽  
Fault Recording

Power system fault diagnosis is an important means to ensure the safe and stable operation of power system. According to the specific situation of China’s current power grid automation level, a hierarchical fault diagnosis method based on switch trip signal, protection information and fault recording information is proposed. This method can not only diagnose simple fault and complex fault, but also judge fault type and phase, and complete fault location, which provides reliable guarantee for operators to quickly remove fault and resume operation. The diagnosis method based on this principle has good application effect in simulation test.

scholarly journals Italian Catacombs and Their Digital Presence for Underground Heritage Sustainability

2021 ◽  
Vol 13 (21) ◽  
pp. 12010
Author(s):  
Olga Lo Presti ◽  
Maria Rosaria Carli
Keyword(s):  
Augmented Reality ◽  
Digital Technologies ◽  
Video Sharing ◽  
Built Heritage ◽  
Vatican City ◽  
Wear And Tear ◽  
Art And Culture ◽  
Virtual And Augmented Reality

The Italian catacombs represent one of the most interesting examples of the country’s underground built heritage. A strategic use of digital technologies can foster their sustainability by providing virtual access to local communities and tourists, as well as by transferring their value to future generations. Referring to a classification of the catacombs of Italy carried out by the Pontifical Commission for Sacred Archaeology of Vatican City, this paper analyzes the digital presence of this heritage within the contexts of social media, video sharing and navigation platforms in institutional and touristic areas. The emerging results show a good digital presence of this cultural heritage on these platforms. At the same time, they reflect an almost total absence of 3D technologies, virtual reconstructions or augmented reality. Only 2 out of 63 catacombs analyzed offer a photographic overview of the sites through the online Google Art and Culture platform, but this is only a small example of what a virtual visit would offer. The following work is based on this notion, as it aims to demonstrate that this type of underground built heritage still has great potential for the valorization and sustainability of these sites through the use of digital technologies. The use of virtual and augmented reality, enhanced by immersive storytelling, would limit the physical wear and tear on the site, making its conservation sustainable in the long term.

scholarly journals A narrative of virtual and augmented reality in the forest sector

2019 ◽  
Vol 70 (1) ◽  
pp. 88-96
Author(s):  
Laura Peedosaar ◽  
Eneli Põldveer ◽  
Joonas Kollo ◽  
Ahto Kangur
Keyword(s):  
Augmented Reality ◽  
Rapid Development ◽  
Forest Sector ◽  
Management Options ◽  
Forest Owners ◽  
Good Opportunity ◽  
Natural Processes ◽  
Virtual And Augmented Reality ◽  
Near Future

Abstract With the rapid development in data acquisition and presentation, there is a growing interest in virtual forests and computer visualization tools. Forest owners have become more aware about their property and are interested in applying different forest management methods and silvicultural techniques. The tools are also applicable in assessment of the changes to the landscape as a result of natural and anthropogenic disturbances. Virtual reality offers a good opportunity to test and compare different management options before implementing decisions which can lead to irreversible consequences. Advances in spatial and temporal data collection enable new and practical solutions for analysis and visualization of long-term natural processes with new forestry applications. In the near future, forest owners and managers will have the possibility to make management decisions without the direct need to exit the office. Furthermore, the learning process is more enthralling and also more profound through augmented reality, helping to foster better working practices even before starting a job in the forest sector.

Effect of scale-varying fractional-order stochastic resonance by simulation and its application in bearing diagnosis

2019 ◽  
Vol 10 (01) ◽  
pp. 1941003 ◽  
Author(s):  
Kuo Chi ◽  
Jianshe Kang ◽  
Xinghui Zhang ◽  
Fei Zhao
Keyword(s):  
Fault Diagnosis ◽  
Stochastic Resonance ◽  
Fractional Order ◽  
Signal To Noise Ratio ◽  
Economic Losses ◽  
Integration Step ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method ◽  
Weak Fault

Bearing is among the most widely used components in rotating machinery. Its failure can cause serious economic losses or even disasters. However, the fault-induced impulses are weak especially for the early failure. As to the bearing fault diagnosis, a novel bearing diagnosis method based on scale-varying fractional-order stochastic resonance (SFrSR) is proposed. Signal-to-noise ratio of the SFrSR output is regarded as the criterion for evaluating the stochastic resonance (SR) output. In the proposed method, by selecting the proper parameters (integration step [Formula: see text], amplitude gain [Formula: see text] and fractional-order [Formula: see text]) of SFrSR, the weak fault-induced impulses, the noise and the potential can be matched with each other. An optimal fractional-order dynamic system can be generated. To verify the proposed SFrSR, numerical tests and application verification are conducted in comparison with the traditional scale-varying first-order SR (SFiSR). The results prove that the parameters [Formula: see text] and [Formula: see text] affect the SFrSR effect seriously and the proposed SFrSR can enhance the weak signal while suppressing the noise. The SFrSR is more effective for bearing fault diagnosis than SFiSR.

scholarly journals Multi-Sensor Fault Diagnosis of Underwater Thruster Propeller Based on Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7187
Author(s):  
Chia-Ming Tsai ◽  
Chiao-Sheng Wang ◽  
Yu-Jen Chung ◽  
Yung-Da Sun ◽  
Jau-Woei Perng
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Rapid Development ◽  
Sensor Fault ◽  
The Neural Network ◽  
Signal Input ◽  
Diagnosis Method ◽  
The One ◽  
The Time Domain ◽  
Single Neural Network

With the rapid development of unmanned surfaces and underwater vehicles, fault diagnoses for underwater thrusters are important to prevent sudden damage, which can cause huge losses. The propeller causes the most common type of thruster damage. Thus, it is important to monitor the propeller’s health reliably. This study proposes a fault diagnosis method for underwater thruster propellers. A deep convolutional neural network was proposed to monitor propeller conditions. A Hall element and hydrophone were used to obtain the current signal from the thruster and the sound signal in water, respectively. These raw data were fast Fourier transformed from the time domain to the frequency domain and used as the input to the neural network. The output of the neural network indicated the propeller’s health conditions. This study demonstrated the results of a single signal and the fusion of multiple signals in a neural network. The results showed that the multi-signal input had a higher accuracy than the one-signal input. With multi-signal inputs, training two types of signals with a separated neural network and then merging them at the end yielded the best results (99.88%), as compared to training two types of signals with a single neural network.

Data-driven fault diagnosis method for the safe and stable operation of solid oxide fuel cells system

2021 ◽  
Vol 490 ◽  
pp. 229561
Author(s):  
Yi Zheng ◽  
Xiao-long Wu ◽  
Dongqi Zhao ◽  
Yuan-wu Xu ◽  
Beibei Wang ◽  
...  
Keyword(s):  
Fuel Cells ◽  
Fault Diagnosis ◽  
Solid Oxide Fuel Cells ◽  
Solid Oxide ◽  
Data Driven ◽  
Stable Operation ◽  
Oxide Fuel ◽  
Diagnosis Method

scholarly journals An Online Data-Driven Fault Diagnosis Method for Air Handling Units by Rule and Convolutional Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4358
Author(s):  
Huanyue Liao ◽  
Wenjian Cai ◽  
Fanyong Cheng ◽  
Swapnil Dubey ◽  
Pudupadi Balachander Rajesh
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Convolutional Neural Networks ◽  
Hvac Systems ◽  
Data Driven ◽  
Stable Operation ◽  
Online Data ◽  
Rule Based ◽  
Air Handling Units ◽  
Diagnosis Method

The stable operation of air handling units (AHU) is critical to ensure high efficiency and to extend the lifetime of the heating, ventilation, and air conditioning (HVAC) systems of buildings. In this paper, an online data-driven diagnosis method for AHU in an HVAC system is proposed and elaborated. The rule-based method can roughly detect the sensor condition by setting threshold values according to prior experience. Then, an efficient feature selection method using 1D convolutional neural networks (CNNs) is proposed for fault diagnosis of AHU in HVAC systems according to the system’s historical data obtained from the building management system. The new framework combines the rule-based method and CNNs-based method (RACNN) for sensor fault and complicated fault. The fault type of AHU can be accurately identified via the offline test results with an accuracy of 99.15% and fast online detection within 2 min. In the lab, the proposed RACNN method was validated on a real AHU system. The experimental results show that the proposed RACNN improves the performance of fault diagnosis.

Helicopter main reduction planetary gear fault diagnosis method based on SVDD

2020 ◽  
Vol 64 (1-4) ◽  
pp. 137-145
Author(s):  
Yubin Xia ◽  
Dakai Liang ◽  
Guo Zheng ◽  
Jingling Wang ◽  
Jie Zeng
Keyword(s):  
Fault Diagnosis ◽  
Planetary Gear ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Energy Characteristics ◽  
Gear Fault ◽  
Channel Information ◽  
Diagnosis Method

Aiming at the irregularity of the fault characteristics of the helicopter main reducer planetary gear, a fault diagnosis method based on support vector data description (SVDD) is proposed. The working condition of the helicopter is complex and changeable, and the fault characteristics of the planetary gear also show irregularity with the change of working conditions. It is impossible to diagnose the fault by the regularity of a single fault feature; so a method of SVDD based on Gaussian kernel function is used. By connecting the energy characteristics and fault characteristics of the helicopter main reducer running state signal and performing vector quantization, the planetary gear of the helicopter main reducer is characterized, and simultaneously couple the multi-channel information, which can accurately characterize the operational state of the planetary gear’s state.

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