Long distance wireless fault diagnosis for photovoltaic modules based on back propagation neural network

Chen, Ling; Han, Wei; Li, Haitao; Xu, Zikun; Zhang, Jingwei; Cao, Xiang

doi:10.1177/0020720920940601

Cited by 7 publications

(4 citation statements)

References 23 publications

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“…To improve product quality, artificial intelligence (AI) solutions have been used to automate defect detection processes by automatically verifying product quality and providing insight into quality issues, hence reducing waste and enabling production improvements. Many ML-based fault/defect detection [ 333 , 334 , 335 , 336 , 337 , 338 ] and quality monitoring approaches [ 336 ] have been proposed in the literature. Several examples of fault diagnostics [ 335 , 339 , 340 , 341 , 342 , 343 , 344 , 345 , 346 , 347 , 348 , 349 ] are also available in the literature.…”

Section: Key Building Blocks Technology Enablers and Innovation Accel...mentioning

confidence: 99%

“…Many ML-based fault/defect detection [ 333 , 334 , 335 , 336 , 337 , 338 ] and quality monitoring approaches [ 336 ] have been proposed in the literature. Several examples of fault diagnostics [ 335 , 339 , 340 , 341 , 342 , 343 , 344 , 345 , 346 , 347 , 348 , 349 ] are also available in the literature. Bayesian approaches that enable root cause analysis of quality issues [ 350 , 351 ] have also been proposed.…”

Section: Key Building Blocks Technology Enablers and Innovation Accel...mentioning

confidence: 99%

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A Primer on the Factories of the Future

Anumbe

Saidy

Harik

2022

Sensors

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In a dynamic and rapidly changing world, customers’ often conflicting demands have continued to evolve, outstripping the ability of the traditional factory to address modern-day production challenges. To fix these challenges, several manufacturing paradigms have been proposed. Some of these have monikers such as the smart factory, intelligent factory, digital factory, and cloud-based factory. Due to a lack of consensus on general nomenclature, the term Factory of the Future (or Future Factory) has been used in this paper as a collective euphemism for these paradigms. The Factory of the Future constitutes a creative convergence of multiple technologies, techniques, and capabilities that represent a significant change in current production capabilities, models, and practices. Using the semi-narrative research methodology in concert with the snowballing approach, the authors reviewed the open literature to understand the organizing principles behind the most common smart manufacturing paradigms with a view to developing a creative reference that articulates their shared characteristics and features under a collective lingua franca, viz., Factory of the Future. Serving as a review article and a reference monograph, the paper details the meanings, characteristics, technological framework, and applications of the modern factory and its various connotations. Amongst other objectives, it characterizes the next-generation factory and provides an overview of reference architectures/models that guide their structured development and deployment. Three advanced communication technologies capable of advancing the goals of the Factory of the Future and rapidly scaling advancements in the field are discussed. It was established that next-generation factories would be data rich environments. The realization of their ultimate value would depend on the ability of stakeholders to develop the appropriate infrastructure to extract, store, and process data to support decision making and process optimization.

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Section: Key Building Blocks Technology Enablers and Innovation Accel...mentioning

confidence: 99%

Section: Key Building Blocks Technology Enablers and Innovation Accel...mentioning

confidence: 99%

A Primer on the Factories of the Future

Anumbe

Saidy

Harik

2022

Sensors

View full text Add to dashboard Cite

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“…The cost function in equation ( 8) is shown in Figure 1(c). It can be found that there are two sparse solutions due to the constraint in equation (7). The L-BFGS algorithm would search for the sparse solutions from an initial solution along the direction of gradient descent.…”

Section: Variant Sparse Filteringmentioning

confidence: 99%

“…There are various machine learning models that can learn useful features in an unsupervised or supervised way, 6 such as back-propagation neural networks (BPNNs), 7 autoencoders, 8 restricted Boltzmann machine (RBM), 9 and convolutional neural networks (CNNs). 10 As a traditional machine learning model, the core idea of BPNN has been widely applied in other models.…”

Section: Introductionmentioning

confidence: 99%

A novel intelligent fault diagnosis method based on variant sparse filtering and back-propagation

Chan

Cheng

2021

Noise & Vibration Worldwide

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Detecting the mechanical faults of rotating machinery in time plays a key role in avoiding accidents. With the coming of the big data era, intelligent fault diagnosis methods based on machine learning models have become promising tools. To improve the feature learning ability, an unsupervised sparse feature learning method called variant sparse filtering is developed. Then, a fault diagnosis method combining variant sparse filtering with a back-propagation algorithm is presented. The involvement of the back-propagation algorithm can further optimize the weight matrix of variant sparse filtering using label data. At last, the developed diagnosis method is validated by rolling bearing and planetary gearbox experiments. The experiment results indicate that the developed method can achieve high accuracy and good stability in rotating machinery fault diagnosis.

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Fault diagnosis for PV system using a deep learning optimized via PSO heuristic combination technique

2023

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A heuristic particle swarm optimization combined with Back Propagation Neural Network (BPNN-PSO) technique is proposed in this paper to improve the convergence and the accuracy of prediction for fault diagnosis of Photovoltaic (PV) array system. This technique works by applying the ability of deep learning for classification and prediction combined with the particle swarm optimization ability to find the best solution in the search space. Some parameters are extracted from the output of the PV array to be used for identification purpose for the fault diagnosis of the system. The results using the back propagation neural network method only and the method of the back propagation heuristic combination technique are compared. The back propagation algorithm converges after 350 steps while the proposed BP-PSO algorithm converges only after 250 steps in the training phase. The accuracy of prediction using the BP algorithms is about 87.8% while the proposed BP-PSO algorithm achieved 95% of right predictions. It was clearly shown that the results of the back propagation heuristic combination technique had better results in the convergence of the simulation as well as in the accuracy of the prediction of the fault diagnosis in the PV system.

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Long distance wireless fault diagnosis for photovoltaic modules based on back propagation neural network

Cited by 7 publications

References 23 publications

A Primer on the Factories of the Future

A Primer on the Factories of the Future

A novel intelligent fault diagnosis method based on variant sparse filtering and back-propagation

Fault diagnosis for PV system using a deep learning optimized via PSO heuristic combination technique

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