The fast and robust automated quality visual inspection has received increasing attention in the product quality control for production efficiency. To effectively detect defects in products, many methods focus on the hand-crafted optical features. However, these methods tend to only work well under specified conditions and have many requirements for the input. So the work in this paper targets on building a deep model to solve this problem. The elaborately designed deep convolutional neural networks (CNN) proposed by us can automatically extract powerful features with less prior knowledge about the images for defect detection, while at the same time is robust to noise. We experimentally evaluate this CNN model on a benchmark dataset and achieve a fast detection result with a high accuracy, surpassing the state-of-the-art methods.

A fast and robust convolutional neural network-based defect detection model in product quality control

Real-time weld geometry prediction based on multi-information using neural network optimized by PCA and GA during thin-plate laser welding

Challenges and opportunities of deep learning-based process fault detection and diagnosis: a review

Simple Summary Timely and accurate detection of cardiovascular diseases is critical to reduce the risk of myocardial infarction. This article proposes a methodology using machine learning, neuro-fuzzy and statistical methods to predict cardiovascular diseases. Our results show that the proposed methodology outperformed well known approaches, reaching a high prediction accuracy greater than 90%. Our methodology helps medical doctors to enhance diagnosis, quality of healthcare and efficacious prescriptions, decreasing the time for exams and minimizing expenses in clinical practice. Abstract Timely and accurate detection of cardiovascular diseases (CVDs) is critically important to minimize the risk of a myocardial infarction. Relations between factors of CVDs are complex, ill-defined and nonlinear, justifying the use of artificial intelligence tools. These tools aid in predicting and classifying CVDs. In this article, we propose a methodology using machine learning (ML) approaches to predict, classify and improve the diagnostic accuracy of CVDs, including support vector regression (SVR), multivariate adaptive regression splines, the M5Tree model and neural networks for the training process. Moreover, adaptive neuro-fuzzy and statistical approaches, nearest neighbor/naive Bayes classifiers and adaptive neuro-fuzzy inference system (ANFIS) are used to predict seventeen CVD risk factors. Mixed-data transformation and classification methods are employed for categorical and continuous variables predicting CVD risk. We compare our hybrid models and existing ML techniques on a CVD real dataset collected from a hospital. A sensitivity analysis is performed to determine the influence and exhibit the essential variables with regard to CVDs, such as the patient’s age, cholesterol level and glucose level. Our results report that the proposed methodology outperformed well known statistical and ML approaches, showing their versatility and utility in CVD classification. Our investigation indicates that the prediction accuracy of ANFIS for the training process is 96.56%, followed by SVR with 91.95% prediction accuracy. Our study includes a comprehensive comparison of results obtained for the mentioned methods.

/pdf/early-prediction-in-classification-of-cardiovascular-15t7nrq5.pdf

Early Prediction in Classification of Cardiovascular Diseases with Machine Learning, Neuro-Fuzzy and Statistical Methods

Automatic defect classification in photovoltaic (PV) modules is gaining significant attention due to the limited application of manual/visual inspection. However, the automatic classification of defects in crystalline silicon solar cells is a challenging task due to the inhomogeneous intensity of cell cracks and complex background. The present study is carried out for automatic defects classification of PV cells in electroluminescence images. Two machine learning approaches, features extraction-based support vector machine (SVM) and convolutional neural network (CNN) are used for the solar cell defect classifications. Suitable hyperparameters, algorithm optimisers, and loss functions are used to achieve the best performance. Solar cell defects are divided into seven classes such as one non-defective and six defective classes. Feature extraction algorithms such as histograms of oriented gradients (HOG), KAZE, Scale-Invariant Feature Transform (SIFT) and speeded-up-robust features (SURF) are used to train the SVM classifier. Finally, the performance results are compared. It is concluded that CNN's accuracy for solar cell defect classification is 91.58% which outperforms the state-of-the-art methods. With features extraction-based SVM, accuracies of 69.95, 71.04, 68.90, and 72.74% are obtained for HOG, KAZE, SIFT, and SURF, respectively. The present study may contribute to making a PV system more efficient for classifying defects to improve the power system efficiency.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-rpg.2019.1342

Photovoltaic cell defect classification using convolutional neural network and support vector machine

In this study, a multiscale monitoring method for nonlinear processes was developed. We introduced a machine learning tool for fault detection and isolation based on the kernel principal component analysis (PCA) and discrete wavelet transform. The principle of our proposal involved decomposing multivariate data into wavelet coefficients by employing the discrete wavelet transform. Then, the kernel PCA was applied on every matrix of coefficients to detect defects. Only those scales that manifest overruns of the squared prediction errors in control limits were considered in the data reconstruction phase. Thus, the kernel PCA was approached on the reconstructed matrix for detecting defects and isolation. This approach exploits the kernel PCA performance for nonlinear process monitoring in combination with multiscale analysis when processing time-frequency scales. The proposed method was validated on a photovoltaic system related to a complex industrial process. A data matrix was determined from the variables that characterize this process corresponding to motor current, angular speed, convertor output voltage, and power voltage system output. We tested the developed methodology on 1000 observations of photovoltaic variables. A comparison with monitoring methods based on neural PCA was established, proving the efficiency of the developed methodology.

/pdf/multiscale-monitoring-using-machine-learning-methods-new-1psrcfjw.pdf

Multiscale Monitoring Using Machine Learning Methods: New Methodology and an Industrial Application to a Photovoltaic System

This paper is mainly aimed at developing an off-line supervision approach geared to complex processes. This approach consists of two parts: the first part is the fault detection and isolation and the second one is the process control. The first part is devoted to the implementation of the multilayer neural PCA which combines the advantage of data reduction provided by the principal component analysis and the power of neural network linearization. The transition to control is conditioned by the absence of faults in the process; if there is a defect, it must be isolated by identifying the defected variables. The second part rests on the combination of two control tools: both the gain scheduling and the feedback linearization yield a new approach called nonlinear gain scheduling. To have our work validated, we applied it to a photovoltaic system and it gave effective results.

Intelligent supervision approach based on multilayer neural PCA and nonlinear gain scheduling

This paper characterizes an approach to data compression and defect isolation for the multi-variable process by introducing the wavelet principal component analysis (PCA) and the genetic algorithms. In the defect analysis process, data compression is a critical phase. In our case of study, this can be achieved through the wavelet PCA. We can, through the use of such a technique, filter the data noise and the measurement errors by combining the PCA and the wavelet analysis. The genetic algorithm optimization feature is then employed in structuring the residues of the defect isolation. We present the principle of the PCA as well as the wavelet compression property. Then, we describe both the defect isolation classical method by structuring the residues and the principle of optimizing a problem by the genetic algorithms. The proposed approach is implemented on a statistic system and the Tennessee Eastman process. In comparison with other previous methods, the obtained results mirror the performance of the approach to defect isolation.

Multi-variable process data compression and defect isolation using wavelet PCA and genetic algorithm

In this paper, we propose an approach to study the biomedical signal of the electro-cardiographic ECG. Our contribution consists in applying the principal component analysis (PCA) to help diagnose the cardiovascular system. Many researches have recently approached the same theme by integrating many tools of signal processing, but the most interesting method is the PCA.Starting from the ECG signal, we define the characteristic parameters of the electrical activity of the heart. The PCA allows detecting and then localizing the defective parameters of the ECG and facilitating the diagnosis of the existing heart disorders. The results we get at the end of this paper show that this approach is reliable compared with data given by the medical expert.

/pdf/ecg-signal-monitoring-using-linear-pca-1vrg698ewv.pdf

ECG signal monitoring using linear PCA

The major goal of this paper is the description of a fault detection and isolation system. Such a system is geared to the complex processes through the combination of the neural networks, Fisher discriminate analysis and the principal component analysis. The corner stone of this work is the application of a self-associative neural network to the nonlinear PCA for fault detection by starting with a various measurement multivariable matrix stemming from a complex industrial process. FDA and PLS are, then, used to identify the directions of the detected faults through the classification of the groups with and without faults. To isolate these faults is to recognize the variables that are the cause of these faults. This is attained by calculating the contribution; in other words, the variables having the largest contribution with regard to the others are considered defective. This statistical approach is authenticated on a pastry production process and gives good results. By comparing FDI with other methods, we can perceive that our approach gives more reliable results.

Exploiting neural PCA and Fisher discriminate analysis for FDI system

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