On-line Fault Detection Using SVM-based Dynamic MPLS for Batch Processes

In this article, a nonlinear dynamic multiway partial least squares (MPLS) based on support vector machines (SVM) is developed for on-line fault detection in batch processes. The approach, referred to as SVM-based DMPLS, integrates the SVM with the MPLS model. Process data from normal historical batches are used to develop the MPLS model, and a series of single-input-single-output SVM networks are adopted to approximate nonlinear inner relationship between input and output variables. In addition, the application of a time-lagged window technique not only makes the complementarities of unmeasured data of the monitored batch unnecessary, but also significantly reduces the computation and storage requirements in comparison with the traditional MPLS. The proposed approach is validated by a simulation study of on-line fault detection for a fed-batch penicillin production.

On-line Fault Detection Using SVM-based Dynamic MPLS for Batch Processes

In this article, a nonlinear dynamic multiway partial least squares (MPLS) based on support vector ma- chines (SVM) is developed for on-line fault detection in batch processes. The approach, referred to as SVM-based DMPLS, integrates the SVM with the MPLS model. Process data from normal historical batches are used to de- velop the MPLS model, and a series of single-input-single-output SVM networks are adopted to approximate nonlinear inner relationship between input and output variables. In addition, the application of a time-lagged win- dow technique not only makes the complementarities of unmeasured data of the monitored batch unnecessary, but also significantly reduces the computation and storage requirements in comparison with the traditional MPLS. The proposed approach is validated by a simulation study of on-line fault detection for a fed-batch penicillin production.

An Insight Survey on Sensor Errors and Fault Detection Techniques in Smart Spaces

The widespread adoption of the Internet of Things (IoT) has transformed various sectors globally, making themmore intelligent and connected. However, this advancement comes with challenges related to the effectiveness ofIoT devices. These devices, present in offices, homes, industries, and more, need constant monitoring to ensuretheir proper functionality. The success of smart systems relies on their seamless operation and ability to handlefaults. Sensors, crucial components of these systems, gather data and contribute to their functionality. Therefore,sensor faults can compromise the system’s reliability and undermine the trustworthiness of smart environments.To address these concerns, various techniques and algorithms can be employed to enhance the performance ofIoT devices through effective fault detection. This paper conducted a thorough review of the existing literature andconducted a detailed analysis.This analysis effectively links sensor errors with a prominent fault detection techniquecapable of addressing them. This study is innovative because it paves theway for future researchers to explore errorsthat have not yet been tackled by existing fault detection methods. Significant, the paper, also highlights essentialfactors for selecting and adopting fault detection techniques, as well as the characteristics of datasets and theircorresponding recommended techniques. Additionally, the paper presents amethodical overview of fault detectiontechniques employed in smart devices, including themetrics used for evaluation. Furthermore, the paper examinesthe body of academic work related to sensor faults and fault detection techniques within the domain. This reflectsthe growing inclination and scholarly attention of researchers and academicians toward strategies for fault detectionwithin the realm of the Internet of Things.

Infrared Fault Detection Method for Dense Electrolytic Bath Polar Plate Based on YOLOv5s

Electrolysis tanks are used to smeltmetals based on electrochemical principles,and the short-circuiting of the pole plates in the tanks in the production process will lead to high temperatures,thus affecting normal production.Aiming at the problems of time-consuming and poor accuracy of existing infrared methods for high-temperature detection of dense pole plates in electrolysis tanks,an infrared dense pole plate anomalous target detection network YOLOv5-RMF based on You Only Look Once version 5(YOLOv5)is proposed.Firstly,we modified the Real-Time Enhanced Super-Resolution Generative Adversarial Network(Real-ESRGAN)by changing the U-shaped network(U-Net)to Attention U-Net,to preprocess the images;secondly,we propose a new Focus module that introduces the Marr operator,which can provide more boundary information for the network;again,because Complete Intersection over Union(CIOU)cannot accommodate target borders that are increasing and decreasing,replace CIOU with Extended Intersection over Union(EIOU),while the loss function is changed to Focal and Efficient IOU(Focal-EIOU)due to the different difficulty of sample detection.On the homemade dataset,the precision of our method is 94%,the recall is 70.8%,and the map@.5 is 83.6%,which is an improvement of 1.3%in precision,9.7%in recall,and 7%in map@.5 over the original network.The algorithm can meet the needs of electrolysis tank pole plate abnormal temperature detection,which can lay a technical foundation for improving production efficiency and reducing production waste.

Optimizing Optical Fiber Faults Detection:A Comparative Analysis of Advanced Machine Learning Approaches

Efficient optical network management poses significant importance in backhaul and access network communicationfor preventing service disruptions and ensuring Quality of Service(QoS)satisfaction.The emerging faultsin optical networks introduce challenges that can jeopardize the network with a variety of faults.The existingliterature witnessed various partial or inadequate solutions.On the other hand,Machine Learning(ML)hasrevolutionized as a promising technique for fault detection and prevention.Unlike traditional fault managementsystems,this research has three-fold contributions.First,this research leverages the ML and Deep Learning(DL)multi-classification system and evaluates their accuracy in detecting six distinct fault types,including fiber cut,fibereavesdropping,splicing,bad connector,bending,and PC connector.Secondly,this paper assesses the classificationdelay of each classification algorithm.Finally,this work proposes a fiber optics fault prevention algorithm thatdetermines to mitigate the faults accordingly.This work utilized a publicly available fiber optics dataset namedOTDR_Data and applied different ML classifiers,such as Gaussian Naive Bayes(GNB),Logistic Regression(LR),Support Vector Machine(SVM),K-Nearest Neighbor(KNN),Random Forest(RF),and Decision Tree(DT).Moreover,Ensemble Learning(EL)techniques are applied to evaluate the accuracy of various classifiers.In addition,this work evaluated the performance of DL-based Convolutional Neural Network and Long-Short Term Memory(CNN-LSTM)hybrid classifier.The findings reveal that the CNN-LSTM hybrid technique achieved the highestaccuracy of 99%with a delay of 360 s.On the other hand,EL techniques improved the accuracy in detecting fiberoptic faults.Thus,this research comprehensively assesses accuracy and delay metrics for various classifiers andproposes the most efficient attack detection system in fiber optics.

Evolutionary Variational YOLOv8 Network for Fault Detection in Wind Turbines

Deep learning has emerged in many practical applications,such as image classification,fault diagnosis,and object detection.More recently,convolutional neural networks(CNNs),representative models of deep learning,have been used to solve fault detection.However,the current design of CNNs for fault detection of wind turbine blades is highly dependent on domain knowledge and requires a large amount of trial and error.For this reason,an evolutionary YOLOv8 network has been developed to automatically find the network architecture for wind turbine blade-based fault detection.YOLOv8 is a CNN-backed object detection model.Specifically,to reduce the parameter count,we first design an improved FasterNet module based on the Partial Convolution(PConv)operator.Then,to enhance convergence performance,we improve the loss function based on the efficient complete intersection over the union.Based on this,a flexible variable-length encoding is proposed,and the corresponding reproduction operators are designed.Related experimental results confirmthat the proposed approach can achieve better fault detection results and improve by 2.6%in mean precision at 50(mAP50)compared to the existing methods.Additionally,compared to training with the YOLOv8n model,the YOLOBFE model reduces the training parameters by 933,937 and decreases the GFLOPS(Giga Floating Point Operations Per Second)by 1.1.

A Fault Detection Method for Electric Vehicle Battery System Based on Bayesian Optimization SVDD Considering a Few Faulty Samples

Accurate and reliable fault detection is essential for the safe operation of electric vehicles.Support vector data description(SVDD)has been widely used in the field of fault detection.However,constructing the hypersphere boundary only describes the distribution of unlabeled samples,while the distribution of faulty samples cannot be effectively described and easilymisses detecting faulty data due to the imbalance of sample distribution.Meanwhile,selecting parameters is critical to the detection performance,and empirical parameterization is generally timeconsuming and laborious and may not result in finding the optimal parameters.Therefore,this paper proposes a semi-supervised data-driven method based on which the SVDD algorithm is improved and achieves excellent fault detection performance.By incorporating faulty samples into the underlying SVDD model,training deals better with the problem of missing detection of faulty samples caused by the imbalance in the distribution of abnormal samples,and the hypersphere boundary ismodified to classify the samplesmore accurately.The Bayesian Optimization NSVDD(BO-NSVDD)model was constructed to quickly and accurately optimize hyperparameter combinations.In the experiments,electric vehicle operation data with four common fault types are used to evaluate the performance with other five models,and the results show that the BO-NSVDD model presents superior detection performance for each type of fault data,especially in the imperceptible early and minor faults,which has seen very obvious advantages.Finally,the strong robustness of the proposed method is verified by adding different intensities of noise in the dataset.

Fault diagnosis method of link control system for gravitational wave detection

To maintain the stability of the inter-satellite link for gravitational wave detection,an intelligent learning monitoring and fast warning method of the inter-satellite link control system failure is proposed.Different from the traditional fault diagnosis optimization algorithms,the fault intelligent learning method pro-posed in this paper is able to quickly identify the faults of inter-satellite link control system despite the existence of strong cou-pling nonlinearity.By constructing a two-layer learning network,the method enables efficient joint diagnosis of fault areas and fault parameters.The simulation results show that the average identification time of the system fault area and fault parameters is 0.27 s,and the fault diagnosis efficiency is improved by 99.8%compared with the traditional algorithm.

Generalized autoencoder-based fault detection method for traction systems with performance degradation

Fault diagnosis of traction systems is important for the safety operation of high-speed trains.Long-term operation of the trains will degrade the performance of systems,which decreases the fault detection accuracy.To solve this problem,this paper proposes a fault detection method developed by a Generalized Autoencoder(GAE)for systems with performance degradation.The advantage of this method is that it can accurately detect faults when the traction system of high-speed trains is affected by performance degradation.Regardless of the probability distribution,it can handle any data,and the GAE has extremely high sensitivity in anomaly detection.Finally,the effectiveness of this method is verified through the Traction Drive Control System(TDCS)platform.At different performance degradation levels,our method’s experimental results are superior to traditional methods.

Advancements in Photovoltaic Panel Fault Detection Techniques

This paper examines the progression and advancements in fault detection techniques for photovoltaic (PV) panels, a target for optimizing the efficiency and longevity of solar energy systems. As the adoption of PV technology grows, the need for effective fault detection strategies becomes increasingly paramount to maximize energy output and minimize operational downtimes of solar power systems. These approaches include the use of machine learning and deep learning methodologies to be able to detect the identified faults in PV technology. Here, we delve into how machine learning models, specifically kernel-based extreme learning machines and support vector machines, trained on current-voltage characteristic (I-V curve) data, provide information on fault identification. We explore deep learning approaches by taking models like EfficientNet-B0, which looks at infrared images of solar panels to detect subtle defects not visible to the human eye. We highlight the utilization of advanced image processing techniques and algorithms to exploit aerial imagery data, from Unmanned Aerial Vehicles (UAVs), for inspecting large solar installations. Some other techniques like DeepLabV3 , Feature Pyramid Networks (FPN), and U-Net will be detailed as such tools enable effective segmentation and anomaly detection in aerial panel images. Finally, we discuss implications of these technologies on labor costs, fault detection precision, and sustainability of PV installations.

Online Fault Monitoring of On-Load Tap-Changer Based on Voiceprint Detection

The continuous operation of On-Load Tap-Changers (OLTC) is essential for maintaining stable voltage levels in power transmission and distribution systems. Timely fault detection in OLTC is essential for preventing major failures and ensuring the reliability of the electrical grid. This research paper proposes an innovative approach that combines voiceprint detection using MATLAB analysis for online fault monitoring of OLTC. By leveraging advanced signal processing techniques and machine learning algorithms in MATLAB, the proposed method accurately detects faults in OLTC, providing real-time monitoring and proactive maintenance strategies.

A New UKF Based Fault Detection Method in Non-linear Systems

To detect the bias fault in stochastic non-linear dynamic systems, a new Unscented Kalman Filtering（UKF） based real-time recursion detection method is brought forward with the consideration of the flaws of traditional Extended Kalman Filtering（ EKF）. It uses the UKF as the residual generation method and the Weighted-Sum Squared Residual （WSSR） as the fault detection strategy. The simulation results are provided which demonstrate better effectiveness and a higher detection ratio of the developed methods.

Gradient Optimizer Algorithm with Hybrid Deep Learning Based Failure Detection and Classification in the Industrial Environment

Failure detection is an essential task in industrial systems for preventing costly downtime and ensuring the seamlessoperation of the system. Current industrial processes are getting smarter with the emergence of Industry 4.0.Specifically, various modernized industrial processes have been equipped with quite a few sensors to collectprocess-based data to find faults arising or prevailing in processes along with monitoring the status of processes.Fault diagnosis of rotating machines serves a main role in the engineering field and industrial production. Dueto the disadvantages of existing fault, diagnosis approaches, which greatly depend on professional experienceand human knowledge, intellectual fault diagnosis based on deep learning (DL) has attracted the researcher’sinterest. DL reaches the desired fault classification and automatic feature learning. Therefore, this article designs a Gradient Optimizer Algorithm with Hybrid Deep Learning-based Failure Detection and Classification (GOAHDLFDC)in the industrial environment. The presented GOAHDL-FDC technique initially applies continuous wavelettransform (CWT) for preprocessing the actual vibrational signals of the rotating machinery. Next, the residualnetwork (ResNet18) model was exploited for the extraction of features from the vibration signals which are thenfed into theHDLmodel for automated fault detection. Finally, theGOA-based hyperparameter tuning is performedtoadjust the parameter valuesof theHDLmodel accurately.The experimental result analysis of the GOAHDL-FD Calgorithm takes place using a series of simulations and the experimentation outcomes highlight the better resultsof the GOAHDL-FDC technique under different aspects.

Twin model-based fault detection and tolerance approach for in-core self-powered neutron detectors

The in-core self-powered neutron detector(SPND)acts as a key measuring device for the monitoring of parameters and evaluation of the operating conditions of nuclear reactors.Prompt detection and tolerance of faulty SPNDs are indispensable for reliable reactor management.To completely extract the correlated state information of SPNDs,we constructed a twin model based on a generalized regression neural network(GRNN)that represents the common relationships among overall signals.Faulty SPNDs were determined because of the functional concordance of the twin model and real monitoring sys-tems,which calculated the error probability distribution between the model outputs and real values.Fault detection follows a tolerance phase to reinforce the stability of the twin model in the case of massive failures.A weighted K-nearest neighbor model was employed to reasonably reconstruct the values of the faulty signals and guarantee data purity.The experimental evaluation of the proposed method showed promising results,with excellent output consistency and high detection accuracy for both single-and multiple-point faulty SPNDs.For unexpected excessive failures,the proposed tolerance approach can efficiently repair fault behaviors and enhance the prediction performance of the twin model.

Milling Fault Detection Method Based on Fault Tree Analysis and Hierarchical Belief Rule Base

Expert knowledge is the key to modeling milling fault detection systems based on the belief rule base.The construction of an initial expert knowledge base seriously affects the accuracy and interpretability of the milling fault detection model.However,due to the complexity of the milling system structure and the uncertainty of the milling failure index,it is often impossible to construct model expert knowledge effectively.Therefore,a milling system fault detection method based on fault tree analysis and hierarchical BRB(FTBRB)is proposed.Firstly,the proposed method uses a fault tree and hierarchical BRB modeling.Through fault tree analysis(FTA),the logical correspondence between FTA and BRB is sorted out.This can effectively embed the FTA mechanism into the BRB expert knowledge base.The hierarchical BRB model is used to solve the problem of excessive indexes and avoid combinatorial explosion.Secondly,evidence reasoning(ER)is used to ensure the transparency of the model reasoning process.Thirdly,the projection covariance matrix adaptation evolutionary strategies(P-CMA-ES)is used to optimize the model.Finally,this paper verifies the validity model and the method’s feasibility techniques for milling data sets.

Fault Detection for Motor Drive Control System of Industrial Robots Using CNN-LSTM-based Observers

The complex working conditions and nonlinear characteristics of the motor drive control system of industrial robots make it difficult to detect faults.In this paper,a deep learning-based observer,which combines the convolutional neural network(CNN)and the long short-term memory network(LSTM),is employed to approximate the nonlinear driving control system.CNN layers are introduced to extract dynamic features of the data,whereas LSTM layers perform time-sequential prediction of the target system.In terms of application,normal samples are fed into the observer to build an offline prediction model for the target system.The trained CNN-LSTM-based observer is then deployed along with the target system to estimate the system outputs.Online fault detection can be realized by analyzing the residuals.Finally,an application of the proposed fault detection method to a brushless DC motor drive system is given to verify the effectiveness of the proposed scheme.Simulation results indicate the impressive fault detection capability of the presented method for driving control systems of industrial robots.

APPROACH TO FAULT ON-LINE DETECTION AND DIAGNOSIS BASED ON NEURAL NETWORKS FOR ROBOT IN FMS

Based on radial basis function (RBF) neural networks, the healthy working model of each sub system of robot in FMS is established. A new approach to fault on line detection and diagnosis according to neural networks model is presented. Fault double detection based on neural network model and threshold judgement and quick fault identification based on multi layer feedforward neural networks are applied, which can meet quickness and reliability of fault detection and diagnosis for robot in FMS.

Wavelet Denoising Applied to Hardware Redundant Systems for Rolling Element Bearing Fault Detection

This work presents a novel wavelet-based denoising technique for improving the signal-to-noise ratio(SNR)of nonsteady vibration signals in hardware redundant systems.The proposed method utilizes the relationship between redundant hardware components to effectively separate fault-related components from the vibration signature,thus enhancing fault detection accuracy.The study evaluates the proposed technique on two mechanically identical subsystems that are simultaneously controlled under the same speed and load inputs,with and without the proposed denoising step.The results demonstrate an increase in detection accuracy when incorporating the proposed denoising method into a fault detection system designed for hardware redundant machinery.This work is original in its application of a new method for improving performance when using residual analysis for fault detection in hardware redundant machinery configurations.Moreover,the proposed methodology is applicable to nonstationary equipment that experiences changes in both speed and load.

A Convolutional Autoencoder Based Fault Detection Method for Metro Railway Turnout

Railway turnout is one of the critical equipment of Switch&Crossing(S&C)Systems in railway,related to the train’s safety and operation efficiency.With the advancement of intelligent sensors,data-driven fault detection technology for railway turnout has become an important research topic.However,little research in the literature has investigated the capability of data-driven fault detection technology for metro railway turnout.This paper presents a convolutional autoencoder-based fault detection method for the metro railway turnout considering human field inspection scenarios.First,the one-dimensional original time-series signal is converted into a twodimensional image by data pre-processing and 2D representation.Next,a binary classification model based on the convolutional autoencoder is developed to implement fault detection.The profile and structure information can be captured by processing data as images.The performance of our method is evaluated and tested on real-world operational current data in themetro stations.Experimental results show that the proposedmethod achieves better performance,especially in terms of error rate and specificity,and is robust in practical engineering applications.

Billiards Optimization with Modified Deep Learning for Fault Detection in Wireless Sensor Network

Wireless Sensor Networks(WSNs)gather data in physical environments,which is some type.These ubiquitous sensors face several challenges responsible for corrupting them(mostly sensor failure and intrusions in external agents).WSNs were disposed to error,and effectual fault detection techniques are utilized for detecting faults from WSNs in a timely approach.Machine learning(ML)was extremely utilized for detecting faults in WSNs.Therefore,this study proposes a billiards optimization algorithm with modified deep learning for fault detection(BIOMDL-FD)in WSN.The BIOMDLFD technique mainly concentrates on identifying sensor faults to enhance network efficiency.To do so,the presented BIOMDL-FD technique uses the attention-based bidirectional long short-term memory(ABLSTM)method for fault detection.In the ABLSTM model,the attention mechanism enables us to learn the relationships between the inputs and modify the probability to give more attention to essential features.At the same time,the BIO algorithm is employed for optimal hyperparameter tuning of the ABLSTM model,which is stimulated by billiard games,showing the novelty of the work.Experimental analyses are made to affirm the enhanced fault detection outcomes of the BIOMDL-FD technique.Detailed simulation results demonstrate the improvement of the BIOMDL-FD technique over other models with a maximum classification accuracy of 99.37%.