The accurate detection and diagnosis of faults are critical for maintaining optimal operation and ensuring the reliability of industrial processes. Notably, the topic of online fault detection and diagnosis has recently presented a significant challenge. This work mainly deploys a neural network technique for the comprehensive detection and diagnosis of faults within the Tennessee Eastman Process (TEP) on a low-computational power system, the Raspberry Pi board. The devolved methodology showcases a remarkable level of accuracy (94.50%) in diagnosing the various TEP faults, affirming its robustness and effectiveness. To elevate the practical applicability of the proposed approach, a meticulous investigation into the implementation of the suggested approach on a Raspberry Pi 4 card was undertaken. The successful realization of this implementation not only highlights the adaptability of the approach but also paves the way for its seamless integration into practical industrial applications.

The key idea behind this study is to integrate a moving window dynamic PCA (MW-DPCA) methodology for fault detection within the Tennessee Eastman process (TEP) into a low-computational power system, the Raspberry Pi 4 card, for real-time application. Indeed, the paramount importance of real-time fault detection (FD) in intricate industrial processes presents a critical challenge. Various data-driven techniques have been developed to ensure safety, maintain operational stability, and optimize productivity in such processes. Principal Component Analysis (PCA) is a fundamental data-driven technique that utilizes dimensionality reduction to extract the most informative features from high-dimensional data, simplifying analysis and potentially revealing underlying fault patterns. However, PCA primarily focuses on static relationships and may miss crucial temporal dynamics for fault identification. This is where dynamic PCA (DPCA) excels. By incorporating lagged values of variables, DPCA captures the temporal evolution of features, enabling a more comprehensive understanding of process behavior and improving the detection of faults involving dynamic changes. In order to address the stochastic measurements, a moving average filter tool is also employed. The results obtained and the successful realization of this implementation demonstrate the adaptability of the approach and pave the way for its seamless integration into practical industrial applications.

This study presents an embedded system (ES) designed for fault detection and diagnosis in grid-connected photovoltaic (GCPV) systems using transient regime analysis. The primary aim of transient regime analysis is to facilitate real-time decision-making, especially during critical faults. A neural network classifier, incorporating a Genetic Algorithm for automated hyperparameter optimization, is developed for GCPV fault classification. These classifiers are seamlessly integrated into a Raspberry Pi 4 platform for fault diagnosis in GCPV systems. Both simulation and experimental results substantiate the ES's viability for fault diagnosis in the examined GCPV system, achieving high accuracy and enabling prompt decision-making to enhance the reliability and safety of GCPV systems.

The evolution of embedded systems has demonstrated their reliability as a solution for monitoring and controlling industrial systems, particularly in renewable energy conversion systems like photovoltaic (PV) energy. The increasing adoption of PV systems highlights the critical need for effective fault diagnosis to ensure their reliable operation. In this paper, we present a novel fault diagnosis approach utilizing Long Short-Term Memory (LSTM) networks optimized through Bayesian optimization techniques. Our methodology is implemented on a Raspberry Pi platform, demonstrating the feasibility of deploying sophisticated fault diagnosis algorithms in resource-constrained environments. Through extensive experiments, we demonstrate the effectiveness of our approach to accurately diagnose faults in grid-connected photovoltaic systems, thereby improving the reliability and efficiency of integrated environmental monitoring systems.The obtained results highlight the potential of combining advanced deep learning techniques with embedded systems to address complex diagnostic challenges, as demonstrated by achieving a 100% accuracy rate.

Grid Connected Photovoltaic (GCPV) sys-tems has been a rising research area in the industry fields. Therefore the high reliability, performance and safety operation of GCPV systems has become a high priority. Thus, it is important of developing an intel-ligent fault detection and diagnosis method that aims at increasing the efficiency of these systems. Therefore, the present study proposes an enhanced intelligent fault diagnosis approach. In the developed procedure, the measured normal and faulty data are applied together to extract the relevant feature, reduce the impact of noise, and then special features are fed to a neural network classifiers. To do that, a Multiscale Principal Component Analysis (MSPCA)-based Artificial Neural Network (ANN) method is proposed to provide the relia-bility and safety of the GCPV systems. From the GCPV measurements, features are appropriately extracted and scaled through multiscale principal component analysis. The ANN classifier is used in classifying twenty-one faults that can occur in GCPV systems operating under different working conditions. The diagnosis results show that the developed MSPCA-based ANN method not only able to detect faults, but also can effectively distinguish between different kinds of faults.

PV systems are prone to failure owing to aging and external/environmental factors. These failures can affect a range of system components, such as PV modules, connecting lines, and converters/inverters, re-sulting in decreased efficiency, performance, and even system failure. As a result, problem detection and diag-nosis (FDD) is an important issue in high-efficiency grid-connected PV systems. Deep learning techniques are the most well-known data-driven methodologies. The biggest advantage of deep learning algorithms, in diagnosis, are learning effectiveness, intelligent FDD becomes more effective. This paper therefore presents a comparative study of FDD based deep learning. The techniques include the Convolutional Neural Network (CNN) and Long Short Time Memory (LSTM). Finally, the FDD based frameworks are implemented using simulated PV data. The diagnosis results show that the CNN and LSTM-based fault diagnosis methods are able to detect and diagnose faults under different operating modes.

PV systems are subject to failures during their operation due to the aging effects and exter-nal/environmental conditions. These faults may affect the different system components such as PV modules, connection lines, converters/inverters, which can lead to a decrease in the efficiency, performance, and fur-ther system collapse. Thus, a key factor to be taken into consideration in high-efficiency grid-connected PV systems is the fault detection and diagnosis (FDD). The most well-known data-driven methods are Deep Learning (DL) approaches. The biggest advantage of DL algorithms, in diagnosis, are that they try to learn high- level features from PV data in a high-order, non-linear and adaptive manners. Then, the fault is classified using soft-max activation function. This work therefore presents a comparative study of FDD based DL techniques. These techniques include Artificial Neural Network (ANN), Recurrent Neural Network (RNN) and Long-Short Term Memory (LSTM). The DL techniques-based fault diagnosis are implemented using an emulated Grid-Connected PV (GCPV) system. The classification results for the pretrained DL models is exhibited and performance of the models are evaluated.

This paper proposes an effective fault detection and diagnosis (FDD) paradigm in Wind Energy Converter (WEC) Systems. The developed FDD frame-work merges the benefits of kernel principal component analysis (KPCA) model and bidirectional long short-term memory (BiLSTM) feature classifier. KPCA is used to extract and select the most effective features. While, BiLSTM is used for classification purposes. The proposed KPCA-based BiLSTM approach involves two main steps; feature extraction and selection and fault classification. It is tackled in such a way that KPCA model is developed in order to select and extract the more efficient features where the final features are fed to BiLSTM to distinguish between different working modes. Different simulation scenarios are considered in this study in order to show the robustness and performances of the developed technique when compared to the conventional FDD methods.