Recent Applications of Artificial Intelligence in Fault Diagnosis of Photovoltaic Systems

2020 ◽  
pp. 257-271 ◽  
Author(s):  
A. Mellit
Keyword(s):  
Artificial Intelligence ◽  
Fault Diagnosis ◽  
Photovoltaic Systems

Deep learning strategies for automatic fault diagnosis in photovoltaic systems by thermographic images

2021 ◽  
Vol 241 ◽  
pp. 114315
Author(s):  
D. Manno ◽  
G. Cipriani ◽  
G. Ciulla ◽  
V. Di Dio ◽  
S. Guarino ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Learning Strategies ◽  
Photovoltaic Systems

Civil Aeroengine Fault Diagnosis Based on Fuzzy Least Square Support Vector Machine

2011 ◽  
Vol 130-134 ◽  
pp. 2047-2050 ◽  
Author(s):  
Hong Chun Qu ◽  
Xie Bin Ding
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Coefficient Matrix ◽  
Least Square ◽  
Support Vector ◽  
Influence Coefficient ◽  
Structural Risk ◽  
Better Than

SVM(Support Vector Machine) is a new artificial intelligence methodolgy, basing on structural risk mininization principle, which has better generalization than the traditional machine learning and SVM shows powerfulability in learning with limited samples. To solve the problem of lack of engine fault samples, FLS-SVM theory, an improved SVM, which is a method is applied. 10 common engine faults are trained and recognized in the paper.The simulated datas are generated from PW4000-94 engine influence coefficient matrix at cruise, and the results show that the diagnostic accuracy of FLS-SVM is better than LS-SVM.

scholarly journals Fault Detection and Severity Level Identification of Spiral Bevel Gears under Different Operating Conditions Using Artificial Intelligence Techniques

Machines ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 173
Author(s):  
Syed Muhammad Tayyab ◽  
Steven Chatterton ◽  
Paolo Pennacchi
Keyword(s):  
Artificial Intelligence ◽  
Fault Diagnosis ◽  
Fault Detection ◽  
Research Study ◽  
High Load ◽  
Operating Conditions ◽  
Spiral Bevel Gears ◽  
Bevel Gears ◽  
Spiral Bevel ◽  
Level Identification

Spiral bevel gears are known for their smooth operation and high load carrying capability; therefore, they are an important part of many transmission systems that are designed for high speed and high load applications. Due to high contact ratio and complex vibration signal, their fault detection is really challenging even in the case of serious defects. Therefore, spiral bevel gears have rarely been used as benchmarking for gears’ fault diagnosis. In this research study, Artificial Intelligence (AI) techniques have been used for fault detection and fault severity level identification of spiral bevel gears under different operating conditions. Although AI techniques have gained much success in this field, it is mostly assumed that the operating conditions under which the trained AI model is deployed for fault diagnosis are same compared to those under which the AI model was trained. If they differ, the performance of AI model may degrade significantly. In order to overcome this limitation, in this research study, an effort has been made to find few robust features that show minimal change due to changing operating conditions; however, they are fault discriminating. Artificial neural network (ANN) and K-nearest neighbors (KNN) are used as classifiers and both models are trained and tested by using the selected robust features for fault detection and severity assessment of spiral bevel gears under different operating conditions. A performance comparison between both classifiers is also carried out.

Satellite based fault diagnosis of photovoltaic systems using recurrent neural networks

2022 ◽  
Vol 305 ◽  
pp. 117874
Author(s):  
Jonas Van Gompel ◽  
Domenico Spina ◽  
Chris Develder
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Recurrent Neural Networks ◽  
Photovoltaic Systems

Application of Artificial Intelligence to Gearbox Fault Diagnosis

2016 ◽  
pp. 536-562
Author(s):  
Anand Parey ◽  
Amandeep Singh Ahuja
Keyword(s):  
Artificial Intelligence ◽  
Fault Diagnosis ◽  
Power Plants ◽  
Human Life ◽  
Propulsion Systems ◽  
Marine Propulsion ◽  
Rotary Machines ◽  
Domestic Appliances ◽  
Financial Losses ◽  
Significant Milestone

Gearboxes are employed in a wide variety of applications, ranging from small domestic appliances to the rather gigantic power plants and marine propulsion systems. Gearbox failure may not only result in significant financial losses resulting from downtime of machinery but may also place human life at risk. Gearbox failure in transmission systems of warships and single engine aircraft, beside other military applications, is unacceptable. The criticality of the gearbox in rotary machines has resulted in enormous effort on the part of researchers to develop new and efficient methods of diagnosing faults in gearboxes so that timely rectification can be undertaken before catastrophic failure occurs. Artificial intelligence (AI) has been a significant milestone in automated gearbox fault diagnosis (GFD). This chapter reviews over a decade of research efforts on fault diagnosis of gearboxes with AI techniques. Some of areas of AI in GFD which still merit attention have been identified and discussed at the end of the chapter.

scholarly journals A Novel Fault Diagnosis System on Polymer Insulation of Power Transformers Based on 3-stage GA–SA–SVM OFC Selection and ABC–SVM Classifier

Polymers ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1096 ◽  
Author(s):  
Xiaoge Huang ◽  
Yiyi Zhang ◽  
Jiefeng Liu ◽  
Hanbo Zheng ◽  
Ke Wang
Keyword(s):  
Artificial Intelligence ◽  
Fault Diagnosis ◽  
Diagnostic Accuracy ◽  
Diagnostic System ◽  
Gas Analysis ◽  
Diagnostic Methods ◽  
Power Transformers ◽  
Svm Classifier ◽  
Diagnosis System ◽  
Optimal Feature

Dissolved gas analysis (DGA) has been widely used in various scenarios of power transformers’ online monitoring and diagnoses. However, the diagnostic accuracy of traditional DGA methods still leaves much room for improvement. In this context, numerous new DGA diagnostic models that combine artificial intelligence with traditional methods have emerged. In this paper, a new DGA artificial intelligent diagnostic system is proposed. There are two modules that make up the diagnosis system. The two modules are the optimal feature combination (OFC) selection module based on 3-stage GA–SA–SVM and the ABC–SVM fault diagnosis module. The diagnosis system has been completely realized and embodied in its outstanding performances in diagnostic accuracy, reliability, and efficiency. Comparing the result with other artificial intelligence diagnostic methods, the new diagnostic system proposed in this paper performed superiorly.

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