Explore the vast range of titles available.

With the enhanced industrial and domestic energy needs, there is a great urge for renewable energy sources because of their eco-friendly nature. Solar energy is crucial among renewable energy sources and there is a great need to optimize and enhance the performance of solar energy usage that is mainly dependent on the system components. The current work has been aimed to discuss the fault detection of photovoltaic (PV) modules by evaluating an efficient, facile inspection algorithm electrical analysis for real-time applications. The paper presents a real-time experimental model for infrared thermography using a thermal imager mounted on a tripod at a suitable distance from the PV modules to capture the images in the best possible way. A novel hybrid algorithm has been proposed and the fault detection along with the electrical parameter analysis has been accurately performed on the PV modules to analyze and process various externally induced faults in the PV systems.

A simplified approach to calculate the daylight glare comfort class (imperceptible, perceptible, disturbing, or intolerable glare) on annual basis and for a grid of points in a space is presented. This method relies on the calculation of the vertical illuminance (Ev) for each grid point only, which is compared to an Ev threshold value for each daylight glare comfort class. These Ev threshold values are determined through a comparison with the Daylight Glare Probability (DGP) values on an annual basis through a fault-detection technique, for a reduced number of points. Compared to an annual calculation of exact DGP values on a certain grid, this approach is able to evaluate the daylight glare comfort classes only, but it is less time consuming. The paper presents and critically discusses this simplified method by means of its application to different case-studies: south and west oriented office in Turin (Lat 45.1° N), in which the DGP is assessed for three points in the space, considering glazing with different transmission properties (specular or scattering) and visible transmittances, as well as three operable internal shading systems (one venetian blinds and two roller blinds, for solar or glare control). For the presented case studies, the average error in the classification of the space according to daylight glare comfort classes is below 5% when comparing this simplified approach to related DGP values.

The development of information technology and process technology have been enhanced the rapid changes in high-tech products and smart manufacturing, specifications become more sophisticated. Large amount of sensors are installed to record equipment condition during the manufacturing process. In particular, the characteristics of sensor data are temporal. Most the existing approaches for time series classification are not applicable to adaptively extract the effective feature from a large number of sensor data, accurately detect the fault, and provide the assignable cause for fault diagnosis. This study aims to propose a multiple time-series convolutional neural network (MTS-CNN) model for fault detection and diagnosis in semiconductor manufacturing. This study incorporates data augmentation with sliding window to generate amounts of subsequences and thus to enhance the diversity and avoid over-fitting. The key features of equipment sensor can be learned automatically through stacked convolution-pooling layers. The importance of each sensor is also identified through the diagnostic layer in the proposed MTS-CNN. An empirical study from a wafer fabrication was conducted to validate the proposed MTS-CNN and compare the performance among the other multivariate time series classification methods. The experimental results demonstrate that the MTS-CNN can accurately detect the fault wafers with high accuracy, recall and precision, and outperforms than other existing multivariate time series classification methods. Through the output value of the diagnostic layer in MTS-CNN, we can identify the relationship between each fault and different sensors and provider valuable information to associate the excursion for fault diagnosis.

Sensor fault detection and isolation (SFDI) is a fundamental topic in unmanned aerial vehicle (UAV) development, where attitude estimation plays a key role in flight control systems and its accuracy is crucial for UAV reliability. In commercial drones with low maximum take-off weights, typical redundant architectures, based on triplex, can represent a strong limitation in UAV payload capabilities. This paper proposes an FDI algorithm for low-cost multi-rotor drones equipped with duplex sensor architecture. Here, attitude estimation involves two 9-DoF inertial measurement units (IMUs) including 3-axis accelerometers, gyroscopes and magnetometers. The SFDI algorithm is based on a particle filter approach to promptly detect and isolate IMU faulted sensors. The algorithm has been implemented on a low-cost embedded platform based on a Raspberry Pi board. Its effectiveness and robustness were proved through experimental tests involving realistic faults on a real tri-rotor aircraft. A sensitivity analysis was carried out on the main algorithm parameters in order to find a trade-off between performance, computational burden and reliability.

Battery sensor data collection and transmission are essential for battery management systems (BMS). Since inaccurate battery data brought on by sensor faults, communication issues, or even cyber-attacks can impose serious harm on BMS and adversely impact the overall dependability of BMS-based applications, such as electric vehicles, it is critical to assess the durability of battery sensor and communication data in BMS. Sensor data are necessary for a BMS to perform every operation. Effective sensor fault detection is crucial for the sustainability and security of electric vehicle battery systems. This research suggests a system for battery data, especially lithium ion batteries, that allows deep learning-based detection and the classification of faulty battery sensor and transmission information. Initially, we collected the sensor data, and preprocessing was carried out using z-score normalization. The features were extracted using sparse principal component analysis (SPCA), and enhanced marine predators algorithm (EMPA) was used for feature selection. The BMS’s safety and dependability may be enhanced by the suggested incipient bat-optimized deep residual network (IB-DRN)-based false battery data identification and classification system. Simulations using MATLAB (2021a), along with statistics, machine learning, and a deep learning toolbox, along with experimental research, were used to show and assess how well the suggested strategy performs. It is shown to be superior to traditional approaches.

The research and application of bearing fault diagnosis techniques are crucial for enhancing equipment reliability, extending bearing lifespan, and reducing maintenance expenses. Nevertheless, most existing methods encounter challenges in discriminating between signals from machines operating under normal and faulty conditions, leading to unstable detection results. To tackle this issue, the present study proposes a novel approach for bearing fault detection based on graph neural networks and ensemble learning. Our key contribution is a novel stochasticity-based compositional method that transforms Euclidean-structured data into a graph format for processing by graph neural networks, with feature fusion and a newly proposed ensemble learning strategy for outlier detection specifically designed for bearing fault diagnosis. This approach marks a significant advancement in accurately identifying bearing faults, highlighting our study's pivotal role in enhancing diagnostic methodologies.

Safe and stable operation plays an important role in the chemical industry. Fault detection and diagnosis (FDD) make it possible to identify abnormal process deviations early and assist operators in taking proper action against fault propagation. After decades of development, data-driven process monitoring technologies have gradually attracted attention from process industries. Although many promising FDD methods have been proposed from both academia and industry, challenges remain due to the complex characteristics of industrial data. In this work, classical and recent research on data-driven process monitoring methods is reviewed from the perspective of characterizing and mining industrial data. The implementation framework of data-driven process monitoring methods is first introduced. State of art of process monitoring methods corresponding to common industrial data characteristics are then reviewed. Finally, the challenges and possible solutions for actual industrial applications are discussed.

Abstract Recently, prognostic and health management (PHM) has become a prominent field in modern industry. The rotate vector (RV) reducer is one of the widely used mechanical components in industrial systems, specifically in robots. The RV reducer is known for its unique characteristics of small size, efficient speed transmission, and high torsion. The RV reducer is prone to several kinds of faults, due to its continuous operation in an industrial robot. To keep the operation smooth and steady, timely PHM of the RV reducer has become essential. Previously, the RV reducer fault was diagnosed via various techniques, such as ferrography analysis, vibration analysis, and acoustic emission analysis. However, these conventional techniques have various issues. To resolve those issues, we introduce a novel approach to use the embedded electrical current system for the fault detection of the RV reducer. However, this is quite complicated to investigate mechanical fault using an electrical current signature, since the RV reducer is not an integral part of the electric motor, and finding a fault pattern in faulty components needs thorough examination. We therefore focus on the application of machine learning (ML) for fault classifications. We present an approach for feature extraction, feature selection, and feature reduction using the information obtained from the motor current signature analysis to create an ML-based fault classification system with distinguishable prominent features. Finally, the authenticity of the presented approach is justified via the improved values of evaluating parameters, such as accuracy, specificity, and sensitivity, for ML classifiers. Graphical Abstract Graphical Abstract

Nowadays, millions of photovoltaic (PV) plants are installed around the world. Given the widespread use of PV supply systems and in order to keep these PV plants safe and to avoid power losses, they should be carefully protected, and eventual faults should be detected, classified and isolated. In this paper, different machine learning (ML) and deep learning (DL) techniques were assessed for fault detection and diagnosis of PV modules. First, a dataset of infrared thermography images of normal and failure PV modules was collected. Second, two sub-datasets were built from the original one: The first sub-dataset contained normal and faulty IRT images, while the second one comprised only faulty IRT images. The first sub-dataset was used to develop fault detection models referred to as binary classification, for which an image was classified as representing a faulty PV panel or a normal one. The second one was used to design fault diagnosis models, referred to as multi-classification, where four classes (Fault1, Fault2, Fault3 and Fault4) were examined. The investigated faults were, respectively, failure bypass diode, shading effect, short-circuited PV module and soil accumulated on the PV module. To evaluate the efficiency of the investigated models, convolution matrix including precision, recall, F1-score and accuracy were used. The results showed that the methods based on deep learning exhibited better accuracy for both binary and multiclass classification while solving the fault detection and diagnosis problem in PV modules/arrays. In fact, deep learning techniques were found to be efficient for the detection and classification of different kinds of defects with good accuracy (98.71%). Through a comparative study, it was confirmed that the DL-based approaches have outperformed those based on ML-based algorithms.

Safety and reliability are absolutely important for modern sophisticated systems and technologies. Therefore, malfunction monitoring capabilities are instilled in the system for detection of the incipient faults and anticipation of their impact on the future behavior of the system using fault diagnosis techniques. In particular, state-of-the-art applications rely on the quick and efficient treatment of malfunctions within the equipment/system, resulting in increased production and reduced downtimes. This paper presents developments within Fault Detection and Diagnosis (FDD) methods and reviews of research work in this area. The review presents both traditional model-based and relatively new signal processing-based FDD approaches, with a special consideration paid to artificial intelligence-based FDD methods. Typical steps involved in the design and development of automatic FDD system, including system knowledge representation, data-acquisition and signal processing, fault classification, and maintenance related decision actions, are systematically presented to outline the present status of FDD. Future research trends, challenges and prospective solutions are also highlighted.