Faulty Modules Research Articles

A Fault‐Tolerant Approach for Modular Robots through Self‐Reconfiguration

Modular robots have unique advantages in handling faults and improving their robustness due to their self‐reconfiguration capacity and homogeneous interchangeability. When locked joint failure occurs in modular robots, the distribution of the failed modules will affect the manipulation capacity of the robot. In this article, a novel self‐reconfiguration method that utilizes only the remaining resources available to mitigate the damage caused by module joint failures is proposed. The key node configuration is searched by the particle swarm optimization (PSO) algorithm, and then the collision‐free reconfiguration path is planned by the rapidly exploring random trees (RRT) algorithm. The proposed method not only handles single‐module lockup failure but can also be expanded to multi‐module lockup failures, fully improving the fault tolerance of the modular robot. The method is deployed on the hardware, and the feasibility of the algorithm is verified by self‐reconfiguration experiments containing faulty modules.

A software defect prediction method using binary gray wolf optimizer and machine learning algorithms

ContextSoftware defect prediction means finding defect-prone modules before the testing process which will reduce testing cost and time. Machine learning methods can provide valuable models for developers to classify software faulty modules. ProblemThe inherent problem of the classification is the large volume of the training dataset's features, which reduces the accuracy and precision of the classification results. The selection of the effective features of the training dataset for classification is an NP-hard problem that can be solved using heuristic algorithms. MethodIn this study, a binary version of the Gray Wolf optimizer (bGWO) was developed to select the most effective features of the training dataset. By selecting the most influential features in the classification, the precision and accuracy of the software module classifiers can be increased. ContributionDeveloping a binary version of the gray wolf optimization algorithm to optimally select the effective features and creating an effective defect predictor are the main contributions of this study. To evaluate the effectiveness of the proposed method, five real-world and standard datasets have been used for the training and testing stages of the classifier. ResultsThe results indicate that among the 21 features of the train datasets, the basic complexity, sum of operators and operands, lines of codes, number of lines containing code and comments, and sum of operands have the greatest effect in predicting software defects. In this research, by combining the bGWO method and machine learning algorithms, accuracy, precision, recall, and F1 criteria have been considerably increased.

Fault-Tolerant Control of a Triple Redundant PMA-SynRM for Minimum Torque Ripple

In this paper, a new fault-tolerant control (FTC) strategy based on minimum torque ripple is proposed for a triple redundant permanent magnet assisted synchronous reluctance motor under single-phase and double-phase faults. Different from removing the faulty modules (single six-phase (S6) operation or single three-phase (S3) operation), the proposed strategy makes full use of the healthy phase of the faulty modules. By precisely controlling the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d-q</i> axes currents of each module, the ripple of permanent magnet torque and reluctance torque can be suppressed simultaneously, thus achieving the minimum output torque ripple. Moreover, the ratio ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> ) of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d-q</i> axes current to the stator current can be determined under the constraint of minimum copper loss. In addition, by changing the ratio <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> , the proposed strategy can be combined with S6 or S3 strategy to achieve the minimum copper loss in a wide torque range. In summary, the proposed FTC strategy not only achieves the minimum torque ripple but also ensures the minimum copper loss. Finally, experiments are carried out to verify the accuracy and validity of the theory.

A trustworthy hybrid model for transparent software defect prediction: SPAM-XAI.

Maintaining quality in software development projects is becoming very difficult because the complexity of modules in the software is growing exponentially. Software defects are the primary concern, and software defect prediction (SDP) plays a crucial role in detecting faulty modules early and planning effective testing to reduce maintenance costs. However, SDP faces challenges like imbalanced data, high-dimensional features, model overfitting, and outliers. Moreover, traditional SDP models lack transparency and interpretability, which impacts stakeholder confidence in the Software Development Life Cycle (SDLC). We propose SPAM-XAI, a hybrid model integrating novel sampling, feature selection, and eXplainable-AI (XAI) algorithms to address these challenges. The SPAM-XAI model reduces features, optimizes the model, and reduces time and space complexity, enhancing its robustness. The SPAM-XAI model exhibited improved performance after experimenting with the NASA PROMISE repository's datasets. It achieved an accuracy of 98.13% on CM1, 96.00% on PC1, and 98.65% on PC2, surpassing previous state-of-the-art and baseline models with other evaluation matrices enhancement compared to existing methods. The SPAM-XAI model increases transparency and facilitates understanding of the interaction between features and error status, enabling coherent and comprehensible predictions. This enhancement optimizes the decision-making process and enhances the model's trustworthiness in the SDLC.

A New Improved Prediction of Software Defects Using Machine Learning-based Boosting Techniques with NASA Dataset

Predicting when and where bugs will appear in software may assist improve quality and save on software testing expenses. Predicting bugs in individual modules of software by utilizing machine learning methods. There are, however, two major problems with the software defect prediction dataset: Social stratification (there are many fewer faulty modules than non-defective ones), and noisy characteristics (a result of irrelevant features) that make accurate predictions difficult. The performance of the machine learning model will suffer greatly if these two issues arise. Overfitting will occur, and biassed classification findings will be the end consequence. In this research, we suggest using machine learning approaches to enhance the usefulness of the CatBoost and Gradient Boost classifiers while predicting software flaws. Both the Random Over Sampler and Mutual info classification methods address the class imbalance and feature selection issues inherent in software fault prediction. Eleven datasets from NASA's data repository, "Promise," were utilised in this study. Using 10-fold cross-validation, we classified these 11 datasets and found that our suggested technique outperformed the baseline by a significant margin. The proposed methods have been evaluated based on their abilities to anticipate software defects using the most important indices available: Accuracy, Precision, Recall, F1 score, ROC values, RMSE, MSE, and MAE parameters. For all 11 datasets evaluated, the suggested methods outperform baseline classifiers by a significant margin. We tested our model to other methods of flaw identification and found that it outperformed them all. The computational detection rate of the suggested model is higher than that of conventional models, as shown by the experiments..

A generalized gradual fault diagnosis using photovoltaic plant reflectometry profile

This paper presents a generalized gradual fault diagnosis technique for photovoltaic (PV) plants using photovoltaic plant reflectometry profile (PPRP). PV plants can suffer from many types of defects; the most critical are the electrical ones that may appear either after a long or a short period of time. Many fault detection techniques are used depending on fault types, and reflectometry is one of the most efficient. Nevertheless, it has its limitations, such as location detection of the first fault only within a PV string, in addition to not being able to identify the fault type. This paper presents a mathematical model of the reflectometry technique, named hereafter the photovoltaic plant reflectometry profile (PPRP), that enhances its performance by enabling it to detect and locate multiple faults on a N-PV string, in addition to detecting any impedance mismatches, even the smallest ones, between a good PV string and one with faulty modules. Simulations were conducted to evaluate the accuracy of the PPRP analytical model considering a 7-PV string and its effectiveness to detect the expected locations of the faulty modules, and results show that the simulated reflected signal matches exactly the expected PPRP using the developed mathematical model.

Intelligent Software Bug Prediction Framework with Parameter-Tuned LSTM with Attention Mechanism Using Adaptive Target-Based Pooling Deep Features

In recent years, various researchers have designed a software bug prediction model for classifying the nonfaulty and faulty modules in software that are correlated with software constraints. Software bug or defect prediction helps programmers or developers discover the possibilities of bugs and minimize maintenance costs. However, most approaches do not solve the class-imbalance issue regarding the software bug prediction model. To solve these issues, the latest software bug prediction model using enhanced deep-structured architecture is developed. Here, the software modules are obtained from online sources, which undergo pre-processing to remove unnecessary data. These pre-processed texts are considered for deep feature extraction, performed using a Convolutional Neural Network (CNN) with an adaptive target-based pooling method to get effective deep features. Here, the parameter tuning in CNN is performed using Hybrid Rat-Barnacle Mating Swarm Optimization (HR-BMSO) to enhance the prediction performance. These deep features are inserted into Adaptive Features-based Parameter-Tuned Attention Long Short Term Memory (AF-PTALSTM) for predicting the software bugs, in which the optimization of certain parameters takes place with the same HR-BMSO to get accurate predicted results. The accuracy and [Formula: see text]1-score of the designed AF-PTALSTM method attain 97% and 94% through analysis. Thus, the experimental analysis of the designed software bug prediction model depicts higher efficiency while estimating with traditional approaches.

Multi-objective control strategy for multilevel converter based battery D-STATCOM with power quality improvement

Differences in battery state-of-charge (SOC) in a cascaded H-bridge multilevel converter (CHB-MLC) based distribution static synchronous compensator (D-STATCOM) in battery storage system (BSS) applications could result in undesirable efficiency and performance reductions, and even system failure. Moreover, faults occurring on batteries and/or H-bridges (modules) of CHB-MLC severely increase the risk of failure. Power quality at the output of CHB-MLC is reduced as results of those faults. Therefore, active power differences that are introduced by SOC balancing among modules and bypassing the faulty modules yield to undesired harmonics at the output of CHB-MLC. This paper proposes a multi-objective control strategy for CHB-MLC based BSS D-STATCOM. The proposed control strategy includes a bidirectional power flow controller with unity power factor operation (UPF) feature, a fault-tolerant (FT) controller, a SOC balancing scheme that is designed to work on fault conditions, and a harmonic minimization strategy to enhance the power quality at the output of the CHB-MLC. Performance of the proposed method is validated through different operating conditions including faults, unbalanced SOCs and dynamic load changes. Results indicate that charging and discharging of the batteries with 5 kW rated power are held successfully along with maintaining UPF operation under both normal and fault conditions. Moreover, grid currents are balanced with the help of the FT controller and SOC balancing is properly operated even in fault conditions. Negative impacts of faults and unbalanced operating conditions on the power quality are also mitigated with the help of the harmonic reduction controller with significant decreases on total harmonic distortion reaching up to 47% and 69% during normal operation and faulty operations, respectively.

Classification framework for faulty-software using enhanced exploratory whale optimizer-based feature selection scheme and random forest ensemble learning.

Software Fault Prediction (SFP) is an important process to detect the faulty components of the software to detect faulty classes or faulty modules early in the software development life cycle. In this paper, a machine learning framework is proposed for SFP. Initially, pre-processing and re-sampling techniques are applied to make the SFP datasets ready to be used by ML techniques. Thereafter seven classifiers are compared, namely K-Nearest Neighbors (KNN), Naive Bayes (NB), Linear Discriminant Analysis (LDA), Linear Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), and Random Forest (RF). The RF classifier outperforms all other classifiers in terms of eliminating irrelevant/redundant features. The performance of RF is improved further using a dimensionality reduction method called binary whale optimization algorithm (BWOA) to eliminate the irrelevant/redundant features. Finally, the performance of BWOA is enhanced by hybridizing the exploration strategies of the grey wolf optimizer (GWO) and harris hawks optimization (HHO) algorithms. The proposed method is called SBEWOA. The SFP datasets utilized are selected from the PROMISE repository using sixteen datasets for software projects with different sizes and complexity. The comparative evaluation against nine well-established feature selection methods proves that the proposed SBEWOA is able to significantly produce competitively superior results for several instances of the evaluated dataset. The algorithms' performance is compared in terms of accuracy, the number of features, and fitness function. This is also proved by the 2-tailed P-values of the Wilcoxon signed ranks statistical test used. In conclusion, the proposed method is an efficient alternative ML method for SFP that can be used for similar problems in the software engineering domain.

Module-Level Performance Evaluation for a Smart PV System Based on Field Conditions

This study presents an approach with a simple structure, low complexity and low costs to evaluate the real-time status and localize the faults of a smart PV system at module level based on field conditions. The performance evaluation approach of a PV system is developed through the defined performance indicators, a complex data matrix to track module locations and a thermal model to determine the module temperature. The generalization potential of the proposed approach has been demonstrated through the successful experiment validation. The results show that the performance indicators are greatly corrected by the estimated module temperature with great linear agreement in R2 of 0.922 compared to actual measured temperature under same conditions. Due to the effective performance indicators capturing more performance differences caused by faults of cracks in 0.22 of PV_ΔV, partial shading in 0.47 of PV_ΔV, broken sensors in 0.17 of PI_ΔI and 1 of PV_ΔV separately, the proposed approach is very effective in evaluating the performance of PV modules at module level. Meanwhile, the faulty modules are diagnosed and located through these findings and the data matrix in the smart PV system. Additionally, the sensitivity of the proposed approach to fault in cracks is much higher than that of monitoring only the power.

ASSESSMENT OF THE EFFICIENCY OF PROTECTION OF THE INFORMATION SYSTEM OF ENTERPRISES

The paper evaluates the effectiveness of the use of functionally stable generalized information system of the enterprise. Diagnosis of the system in general. The following definitionassumption was used in the diagnosis: the amount of diagnostic information is the number of test results accumulated by the module before the execution of the algorithm, recorded during the mathematical modeling of the diagnostic procedure. It is illustrated that the reliability of the diagnosis does not change due to the expansion of the system. This is due to the property of adaptive self-diagnosis - the ability to diagnose with a given reliability. Values of reliability significantly depend on the number of failures. As the number of failures increases, the reliability of diagnosis decreases, due to the peculiarities of the algorithm of analysis of test results, in which the whole set of modules is divided into two subsets - possible good and possible bad modules. It is the power of many possible faulty modules that influences decisionmaking, i.e. the reliability of diagnosis. Dependence of reliability on a priori probability of working condition of modules shows insignificant increase of reliability with increase of value of probability of working capacity that is also caused by features of algorithm of analysis of diagnostic information in which the decision on failures of modules is made directly taking into account value of probability of working capacity. The amount of diagnostic information is the number of test results accumulated by the module before executing the algorithm. The property of adaptive self-diagnosis, in contrast to the existing ones, is considered in the work due to the increase in the time of diagnosis and the acceptable decrease in the reliability of the diagnosis. At the same time, the time of diagnosis is predictable and is within acceptable limits, which allows to detect failures in time, roll back the computational process and ensure the insensitivity of the computational process to failures of system modules.

Novel Double Modular Redundancy Based Fault-Tolerant FIR Filter for Image Denoising

In signal processing and communication systems, digital filters are widely employed. In some circumstances, the reliability of those systems is crucial, necessitating the use of fault tolerant filter implementations. Many strategies have been presented throughout the years to achieve fault tolerance by utilising the structure and properties of the filters. As technology advances, more complicated systems with several filters become possible. Some of the filters in those complicated systems frequently function in parallel, for example, by applying the same filter to various input signals. Recently, a simple strategy for achieving fault tolerance that takes advantage of the availability of parallel filters was given. Many fault-tolerant ways that take advantage of the filter’s structure and properties have been proposed throughout the years. The primary idea is to use structured authentication scan chains to study the internal states of finite impulse response (FIR) components in order to detect and recover the exact state of faulty modules through the state of non-faulty modules. Finally, a simple solution of Double modular redundancy (DMR) based fault tolerance was developed that takes advantage of the availability of parallel filters for image denoising. This approach is expanded in this short to display how parallel filters can be protected using error correction codes (ECCs) in which each filter is comparable to a bit in a standard ECC. “Advanced error recovery for parallel systems,” the suggested technique, can find and eliminate hidden defects in FIR modules, and also restore the system from multiple failures impacting two FIR modules. From the implementation, Xilinx ISE 14.7 was found to have given significant error reduction capability in the fault calculations and reduction in the area which reduces the cost of implementation. Faults were introduced in all the outputs of the functional filters and found that the fault in every output is corrected.

Data and Ensemble Machine Learning Fusion Based Intelligent Software Defect Prediction System

The software engineering field has long focused on creating high-quality software despite limited resources. Detecting defects before the testing stage of software development can enable quality assurance engineers to concentrate on problematic modules rather than all the modules. This approach can enhance the quality of the final product while lowering development costs. Identifying defective modules early on can allow for early corrections and ensure the timely delivery of a high-quality product that satisfies customers and instills greater confidence in the development team. This process is known as software defect prediction, and it can improve end-product quality while reducing the cost of testing and maintenance. This study proposes a software defect prediction system that utilizes data fusion, feature selection, and ensemble machine learning fusion techniques. A novel filter-based metric selection technique is proposed in the framework to select the optimum features. A three-step nested approach is presented for predicting defective modules to achieve high accuracy. In the first step, three supervised machine learning techniques, including Decision Tree, Support Vector Machines, and Naïve Bayes, are used to detect faulty modules. The second step involves integrating the predictive accuracy of these classification techniques through three ensemble machine-learning methods: Bagging, Voting, and Stacking. Finally, in the third step, a fuzzy logic technique is employed to integrate the predictive accuracy of the ensemble machine learning techniques. The experiments are performed on a fused software defect dataset to ensure that the developed fused ensemble model can perform effectively on diverse datasets. Five NASA datasets are integrated to create the fused dataset: MW1, PC1, PC3, PC4, and CM1. According to the results, the proposed system exhibited superior performance to other advanced techniques for predicting software defects, achieving a remarkable accuracy rate of 92.08%.

Feature Extraction and Classification of Photovoltaic Panels Based on Convolutional Neural Network

Photovoltaic (PV) boards are a perfect way to create eco-friendly power from daylight. The defects in the PV panels are caused by various conditions; such defective PV panels need continuous monitoring. The recent development of PV panel monitoring systems provides a modest and viable approach to monitoring and managing the condition of the PV plants. In general, conventional procedures are used to identify the faulty modules earlier and to avoid declines in power generation. The existing deep learning architectures provide the required output to predict the faulty PV panels with less accuracy and a more time-consuming process. To increase the accuracy and to reduce the processing time, a new Convolutional Neural Network (CNN) architecture is required. Hence, in the present work, a new Real-time Multi Variant Deep learning Model (RMVDM) architecture is proposed, and it extracts the image features and classifies the defects in PV panels quickly with high accuracy. The defects that arise in the PV panels are identified by the CNN based RMVDM using RGB images. The biggest difference between CNN and its predecessors is that CNN automatically extracts the image features without any help from a person. The technique is quantitatively assessed and compared with existing faulty PV board identification approaches on the large real-time dataset. The results show that 98% of the accuracy and recall values in the fault detection and classification process.

Mitigating mismatch power loss of series–parallel and total-cross-tied array configurations using novel enhanced heterogeneous hunger games search optimizer

The location of shaded or faulty Photovoltaic modules in the PV array has a negative impact on the harvested power from the entire array. To overcome this significant limitation, PV reconfiguration is a considerable technique developed via interchanging the PV modules’ location physically or electrically. By this inspiration, in this article, the authors propose a novel enhanced heterogeneous hunger games search optimizer (EHHGS) based PV reconfiguration. The innovated EHHGS introduces a modified variant for the basic hunger game search optimizer (HGS) to achieve a high diversity and robust exploitation of the optimal solutions. The EHHGS is applied to identify the optimal relocation for the shaded or faulty modules in two configurations of PV connected array: total-cross-tied array (TCT) and Series–parallel one (S–P). The proposed approach has applied symmetric and asymmetric connected PV arrays with dimensions of 9 × 9 and 10 × 8 throughout five different shade patterns. Moreover, for providing a flexible tool for the user/researcher to detect and observe the benefits achieved via the PV reconfiguration strategy, a simple graphical user interface (GUI) for the PV reconfiguration strategy of TCT or S–P PV connected array using meta-heuristic algorithms is designed. This implemented GUI can extend for any size of PV arrays, different optimization algorithms, and different connection schemes. The proposed EHHGS, HGS, and set of recent optimizers, including harris hawk optimizer (HHO), marine predators algorithm (MPA), and artificial ecosystem-based optimization (AEO), handle a new simplified objective function to boost the optimizer’s ability in catching the optimal modules’ location to alleviate the mismatched power in the studied arrays. Several statistical metrics are computed for providing an unbiased comparison. Through the comparisons, the proposed EHHGS exhibits superior performance. It achieves the best re-design for the considered arrays that helps in avoiding the mismatch losses in the cases of the partial shaded/faulty modules and enhances the power generated profiles. EHHGS enhances the power by percentages of 44.42%, 11.9%, 33.36%, 20. 86% and 13.17% compared to the TCT-connected system. In the case of the S–P connection, the proposed EHHGS generates 47.2% and 10.45%, 30.75%, 17.25%, and 26.27% higher power.

Online Diagnosis and Ride-Through Operation for Cascaded H-Bridge Converter Based STATCOM With a Single Open-Circuit IGBT

To address the most commonly seen single insulated gate bipolar transistor (IGBT) open-circuit fault problems in cascaded H-bridge (CHB) converter based static synchronous compensator, a simple online fault diagnosis method and the corresponding ride-through operation approach are proposed. The method can quickly determine the exact location of the open-circuit IGBT through three steps, via analyzing the relationships between the dc bias in the three-phase output current of CHB converter, the transient dc rail voltage variations with respect to the averaged dc rail voltage, and the response of dc rail voltage and output current when the modulation approach of the faulty power module is adjusted. Afterwards, a fault ride-through approach is adopted to seamlessly transfer the modulation approach of faulty H-bridge module and it behaves as a half-bridge to provide a half of the rated output ac voltage. Meanwhile, the corresponding dc offset voltage from this faulty module is compensated by producing the same dc voltage in the healthy phase legs to avoid the dc bias in the three-phase output current. It has been demonstrated that the proposed fault diagnosis approach and the fault ride-through approach are effective in various adverse situations including grid voltage sudden disturbances and the load power sudden changes. Comprehensive simulated and experimental results have been provided to validate the effectiveness of the proposed approach.

Tiny Phase-Error Monitor for Fault and Soft-Error-Tolerant DLL to Support Graceful Degradation and Module-Level Testing

In an IC used for safety-critical applications, the Fault and soft-error tolerance (or FET) is often desirable. In this work, we consider a graceful degradation scheme, as the second line of defense, for a FET delay-locked loop (DLL) we have recently developed. By doing so, a FET DLL will not operate blindly when its tolerance to faults or soft errors has been degraded. This is achieved by incorporating a novel low-cost excessive phase-error monitor. Any excessive phase error beyond a prelearned phase-error tolerance range will trigger an alarm of failure. This monitor can also be used to support an online test for deciding whether there is a faulty module in our TMR-based FET DLL at any given time. We have implemented the proposed scheme in a 90-nm CMOS process. The results show that the area of this excessive phase error monitor is as small as 60 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}\,\,\times $ </tex-math></inline-formula> 60 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}\,\,=$ </tex-math></inline-formula> 0.0036 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , or only 4.12% of the entire FET DLL.

Generative Oversampling Methods for Handling Imbalanced Data in Software Fault Prediction

Imbalanced software fault datasets, having fewer faulty modules than the nonfaulty modules, make accurate fault prediction difficult. It is challenging for software practitioners to handle imbalanced fault data during software fault prediction (SFP). Earlier, several researchers have applied oversampling techniques such as synthetic minority oversampling techniques and others for imbalanced learning in SFP. However, most of these techniques resulted in overfitted prediction models. This article presents generative oversampling methods to handle imbalanced data problems in the SFP. Using the generative adversarial network (GAN) based approach, the presented methods generate synthetic samples of the faulty modules to balance the proportion of faulty and nonfaulty modules in the fault datasets. Further, SFP models are built on the processed fault datasets using different machine learning techniques. Experimental validation of the presented oversampling methods is done on 18 fault datasets gathered from PROMISE, JIRA, Eclipse data repositories, and precision, recall, f1-score, and AUC are used as evaluation measures. We extensively compared presented oversampling methods with various state-of-the-art class imbalance techniques and baseline models. The experimental results evidenced that the presented methods improved fault prediction performance and yielded better performance than the state-of-the-art class imbalance techniques.

Self-Assembly and Self-Repair during Motion with Modular Robots

Self-reconfigurable modular robots consist of multiple modular elements and have the potential to enable future autonomous systems to adapt themselves to handle unstructured environments, novel tasks, or damage to their constituent elements. This paper considers methods of self-assembly, bringing together robotic modules to form larger organism-like structures, and self-repair, removing and replacing faulty modules damaged by internal events or environmental phenomena, which allow group tasks for the multi-robot organism to continue to progress while assembly and repair take place. We show that such “in motion” strategies can successfully assemble and repair a range of structures. Previously developed self-assembly and self-repair strategies have required group tasks to be halted before they could begin. This paper finds that self-assembly and self-repair methods able to operate during group tasks can enable faster completion of the task than previous strategies, and provide reliability benefits in some circumstances. The practicality of these new methods is shown with physical hardware demonstrations. These results show the feasibility of assembling and repairing modular robots whilst other tasks are in progress.

Detection Criterion for Progressive Faults in Photovoltaic Modules Based on Differential Voltage Measurements

PV modules may experience degradation conditions that affect their power efficiency and affect the rest of the PV array. Based on the literature review, this paper links the parameter variation on a PV module with the six most common degradation faults, namely, series resistance degradation, optical homogeneous degradation, optical heterogeneous degradation, potential induced degradation, micro-cracks, and light-induced degradation. A Monte Carlo-based numerical simulation was used to study the effect of the faults mentioned above in the voltage of the modules in a PV array with one faulty module. A simple expression to identify faults was derived based on the obtained results. The simplicity of this expression allows integrating the fault detection technique in low-cost electronic circuits embedded in a PV module, optimizer, or microinverter.