Fault Detection Technique Research Articles

A Systematic Review of Lithium Battery Defect Detection Techniques and Technologies

This systematic review aims to explore and synthesize the existing literature on defect detection methods in lithium batteries. With the increasing demand for reliable and efficient lithium batteries in various applications, ensuring their safety and performance through effective defect detection is critical. This review categorizes and evaluates different detection techniques, including electrochemical, non-destructive testing (NDT), electrical, acoustic emission, optical methods, and machine learning. The primary objective is to provide a comprehensive understanding of the current state of defect detection technologies, assess their effectiveness, and identify key challenges and future research directions. The review covers various defect types, including manufacturing, operational, and environmental defects, and discusses the methodologies used for defect detection, including their sensitivity, accuracy, speed, cost, and practicality. Additionally, the review highlights real-world applications, case studies, and the integration challenges of these technologies with Battery Management Systems (BMS). By examining these aspects, the review aims to offer valuable insights for researchers, manufacturers, and practitioners in the field of lithium battery technology. Key findings suggest that while significant advancements have been made, there remain substantial challenges, particularly in the areas of data acquisition, standardization, and integration with existing battery management systems. Future research should focus on improving the robustness, scalability, and cost-effectiveness of defect detection methods, as well as developing comprehensive regulatory frameworks to ensure the safe deployment of lithium batteries.

Novel Reference Method for the Characterization of PD Measuring Systems Using HFCT Sensors.

During their lifespan, high-voltage (HV) electrical systems are subjected to operating conditions in which electrical, mechanical, thermal and environmental-related stresses occur. These conditions over time lead to unforeseen failures caused by various types of defects. For this reason, there are several technologies for measuring and monitoring the electrical systems, with the aim of minimizing the number of faults. The early detection of defects, preferably in their incipient state, will enable the necessary corrective actions to be taken in order to avoid unforeseen failures. These failures generally lead to human risks and material damage, lack of power supply and significant economic losses. An efficient maintenance technique for the early detection of defects consists of the supervision of the dielectrics status in the installations by means of on-line partial discharge (PD) measurement. Nowadays, there are numerous systems in the market for the measurement of PD in HV installations. The most efficient with a reasonable cost will be those that offer greater security guarantees and the best positioned in the market. Currently, technology developers and users of PD measuring systems face difficulties related to the lack of reference procedures for their complete characterization and to the technical and economic drawback of performing the characterization tests on site or in laboratory installations. To deal with the previous difficulties, in this paper a novel method for the complete and standardized characterization of PD measuring systems is presented. The applicability of this method is mainly adapted for the characterization of systems operating in on-line applications using high-frequency current transformer (HFCT) sensors. For the appropriate application of the method, an associated and necessary scale modular test platform is used. In the test platform, the real on-site measuring conditions of an HV insulated distribution line are simulated in a controlled way. Practical characterizations, showing the convenience and advantages of applying the method using the modular test platform, are also presented.

An Efficient Double Deep Q Learning Network-Based Soft Faults Detection and Localization in Analog Circuits

To identify whether the circuit under test is fault-free or faulty, fault diagnosis is conducted in an analog circuit. However, in the case of fault detection (FD) techniques, the size of FD is the main challenge in testing, especially for complex analog circuits. Hence to reduce the size of FD, specific test nodes are chosen to perform fault diagnosis from the available accessible test nodes. Specific test nodes are selected based on the faults classification efficiency of fault diagnosis. Therefore, this paper proposes an approach for single and multiple soft fault diagnosis using minimum test nodes in analog electronic circuits. The proposed double deep Q-learning-based hybrid Simulated annealing–Tabu search (DDQN-Hybrid SATS) technique determines minimum test nodes with the utilization of distance metric for fault classification. The DDQN-Hybrid SATS technique is used to identify minimum nodes for testing. In this, the double deep Q learning network (DDQN) ensures better reliability and faster convergence in learning but suffers from catastrophic forgetting issues. To prevent such issues, the DDQN approach is optimized using a hybrid SATS algorithm. The hybrid optimization of simulated annealing (SA) and Tabu search (TS) coalesce the advantages of individual optimization procedures to provide the optimum solution in a fast and effective way. The results for a fourth-order low pass filter are presented (i.e., first CUT) and an eight-bit digital to analog converter (i.e., second CUT). Moreover, the simulation experiment reveals that the proposed DDQN-Hybrid SATS technique achieves greater overall fault classification accuracy of about 96.25% than other compared techniques with minimum computation time.

USE OF GPR TECHNOLOGIES IN THE ROAD CONSTRUCTION INDUSTRY

The article continues the study on determining the possibility of applying ground penetrating radar (GPR) technologies in the road construction industry. The authors conduct research using an antenna unit that registers the cross-polarisation component of the reflected signal. In this case, processing the obtained radar images comes down to subtracting two mutually orthogonal components of the signal, which allows the determination of areas of the environment with anisotropic properties. Identifying and positioning extraneous inclusions, finding communications, and determining the location of reinforcement in roadway and bridge structures are relevant tasks. Their solution lies in studying the phenomenon of wave diffraction on extraneous inclusions of the investigated environment. Metal inclusions of different diameters are distinctly visible on radar images obtained during laboratory research. The laboratory studies conducted by the authors showed that the shape and nature of the hyperbola of the diffracted wave significantly depend on the object’s depth, diameter, and material. Therefore, further research in this direction should aim at studying the shape of the diffracted wave, which ultimately allows for judging the depth of occurrence and the diameter of a local extraneous inclusion. When finding and identifying reinforcement in structural layers of road surfaces and elements of bridges, difficulties arise due to the spacing of the reinforcing mesh. With a small reinforcing mesh spacing, when the wavelength is longer than the spacing, the reflections from the adjacent reinforcement bars merge into a common continuous boundary and make the lower layers of the structure almost indistinguishable. Solving the mentioned difficulties is possible by improving the equipment base and the algorithms for processing and interpreting the received radar images. Completed theoretical studies and computational experiments allow us to state that the development of road surface thickness measurement and defect detection techniques will become the basis of the road surface monitoring system using the subsurface geolocation method. Keywords: ground penetrating radar, radar image, road surface, dielectric constant, defect detection, thickness measurement, local inclusions.

Cases of sensor fault assessment

Abstract The field of structural health monitoring (SHM) is based on collecting data, and according to that, useful information about the monitored structures is obtained. Therefore, it is essential that the signals from the sensors used in the monitoring campaign can be trusted. However, some of these sensors will sometimes fail or will, at least, pick up excessive noise that might influence the quality of the information extracted from the data. In this paper, a new technique of sensor fault detection is illustrated that is based on constraints between multiple output signals from the monitored structures.

Fault Detection Techniques for Variable Frequency Drives and its Structured Components

Abstract: Variable Frequency Drives (VFDs) are essential instruments in modern industrial systems, providing precise control over motor speed in various industrial applications and improving energy efficiency. Faults in VFD’s can lead to costly downtime and reduced productivity. This paper presents precise fault detection technique for VFDs and its technical components providing flawless step by step method for quick access of faulty parts / items and time saving in testing methods. Experimental results demonstrate the effectiveness of the proposed approach in accurately detecting faults in IGBT, SCR, choke, capacitor, power card, control card, firing card, CT card and its causes like temperature over voltage and current, aging, atmosphere and bearing wear etc. The proposed methodology offers Cold test and hot test methods applied on common technical machines using oscilloscope and multi-meter.

Enhancing Power System Reliability Through Advanced Fault Diagnosis Methods: A Deep Learning Approach

In order to attain high reliability in power systems, effective fault detection techniques that can quickly identify and mitigate defects must be developed. In this article, we suggest a novel method for improving power system dependability by using deep learning techniques to identify faults. In order to achieve precise fault identification and classification, our technology uses deep neural networks to automatically learn and extract features from power system data. We thoroughly test our method on a range of fault scenarios and real-world datasets to determine its efficacy. The outcomes show how well our approach performs in comparison to conventional fault identification methods, underscoring the possibility of a major increase in power system reliability. By providing useful insights for improving problem diagnosis techniques in power engineering, this research advances system resilience and minimizing downtime.

FFF print defect characterization through in-situ electrical resistance monitoring

Fused filament fabrication is a popular fabrication technique. Currently there is a need for in-situ monitoring modalities to gather real-time information on prints, both for quality control and closed-loop control. Despite current advancements, effective and affordable in-situ monitoring techniques for non-destructive defect detection of voids and bonding quality are still limited. This work demonstrates in-situ monitoring of fused filament fabrication through electrical resistance measurements as an alternative to thermal and optical methods. A new, easy-to-implement setup is demonstrated which measures the electrical resistance of a conductively doped filament between the nozzle and single or multi-electrodes on the bed. Defects can be located in an unprecedented way with the use of encoded axes in combination with the observed resistance variations throughout the part. A model of the anisotropic electrical conduction is used to interpret the measurements, which matches well with the data. Warping, inter-layer adhesion, under-extrusion and overhang sagging print defects can be observed in the measurements of parts with a complex geometry, which would be difficult to measure otherwise. Altogether in-situ electrical resistance monitoring offers a tool for optimising prints by online studying the influence of the print parameters for quality assessment and it opens up possibilities for closed-loop control.

A transfer-learning-based robust technique for multi-type fault detection and classification using Hilbert–Huang transform in low-voltage power distribution grids

A transfer-learning-based robust technique for multi-type fault detection and classification using Hilbert–Huang transform in low-voltage power distribution grids

DAT: A robust Discriminant Analysis-based Test of unimodality for unknown input distributions

This letter introduces a Discriminant Analysis-based unimodality Test (DAT) for automatically identifying whether a dataset is unimodal or multimodal, detecting deviations in time series datasets, estimating statistical parameters, and identifying the skewness of the data. DAT is effective in classifying datasets under both unimodal and multimodal conditions and is suitable for bi-classification applications. The performance of DAT was compared to two well-known unimodality tests, namely the dip and folding tests, and is shown to perform better. We then extended DAT to the development of a fault detection technique, which was tested against five different machine learning classifiers using data from the Case Western Reserve University (CWRU) ball bearing database. The results obtained show that the proposed approach is effective, achieving 99.999% accuracy for detecting small ball bearing fault sizes (0.007 inches). Our conclusion indicates a significant potential of the proposed test for improving anomaly detection in industrial and other related fields.

Micro-crack localization for steel strands using nonlinear vibro-acoustic modulation based on modified signal bispectrum analysis

Under continuous environmental erosion and long-time high tension, fatigue micro-cracks of bridge cables appear frequently to challenge the reliability of cables. The vibro-acoustic modulation analysis is adopted because of its sensitivity to contact-type defects, especially fatigue cracks. In this paper, using multiple sets of low-frequency vibration and high-frequency ultrasonic excitation, the propagation of the ultrasonic wave in the steel strands is studied. Then, the modulated signal bispectrum is proposed to improve the detection reliability of nonlinear modulation components in the nonlinear vibro-acoustic modulation signal of the contact-type defect. The numerical simulations and laboratory tests of straight wire, helical wire, and multi-wire strands are carried out for micro-crack detection to verify the robustness of the proposed method. Compared with the traditional method, the modulated signal bispectrum can still produce clearer peak detection under high noise conditions. The results show that it provides a promising, dynamic and rapid cable defect detection technique.

Rotor speed estimation for half-broken bar detection in induction motors using Kalman filtering

This study addresses the early detection of half-broken bars in induction motors (IMs). Most research on fault detection focuses on current signature analysis because it can provide online and remote monitoring at a low cost. However, errors caused by spectral leakage around the fundamental frequency supply reduce its reliability for fault diagnosis at the incipient stages. The proposed detection technique is based on a spectral analysis of the motor speed. To detect a half-broken bar, measurements of stator voltages and currents are processed by an adaptive extended Kalman filter for system state estimation. Then, a spectral analysis is applied to the estimated motor speed to obtain its power spectral distribution. Finally, the fault harmonic amplitude at 2sfs is used as fault indicator for fault diagnosis. The proposed approach has three main advantages: (1) speed estimation is unaltered by spectral leakage around the main frequency supply, (2) the cost of speed sensors is avoided, and (3) the absence of connecting shaft couplings prevents corruption of speed data by mechanical interference. Numerical simulations and experimental results were carried out for the squirrel cage IM to provide encouraging validation of the proposed early fault detection technique.

Recent advances in fault detection techniques for photovoltaic systems: An overview, classification and performance evaluation

Photovoltaic (PV) arrays are typically built outdoors in severe conditions and are vulnerable to a variety of defects, which will negatively impact their efficiency and reduce the system's performance as a whole. Therefore, implementing efficient fault identification and diagnosis is crucial to increase the productivity and efficiency of PV installations, ensure their safe operation and reduce maintenance costs. In this article, the types and causes of numerous faults that arise in PV systems are swiftly examined. Additionally, a number of the most recent methods suggested in the literature for PV fault diagnostics are reviewed and assessed. We only cover methods for locating faults on the DC side of the PV system. Based on the review that was conducted, suggestions for the direction of future studies are given. Finally, this document serves as a significant manual that offers relevant information on methods of detection and diagnosis for PV systems.

Optimal Artificial Neural Network-based Fabric Defect Detection and Classification

Automated Fabric Defect (FD) detection plays a crucial role in industrial automation within fabric production. Traditionally, the identification of FDs heavily relies on manual assessment, facilitating prompt repairs of minor defects. However, the efficiency of manual recognition diminishes significantly as labor working hours increase. Consequently, there is a pressing need to introduce an automated analysis method for FD recognition to reduce labor costs, minimize errors, and improve fabric quality. Many researchers have devised defect detection systems utilizing Machine Learning (ML) approaches, enabling swift, accurate, and efficient identification of defects. This study presents the Optimal Artificial Neural Network-based Fabric Defect Detection and Classification (OANN-FDDC) technique. The OANN-FDDC technique exploits handcrafted features with a parameter-tuning strategy for effectively detecting the FD process. To obtain this, the OANN-FDDC technique employs CLAHE and Bilateral Filtering (BF) model-based contrast augmentation and noise removal. Besides, the OANN-FDDC technique extracts shape, texture, and color features. For FD detection, the ANN method is utilized. To improve the detection results of the ANN method, the Root Mean Square Propagation (RMSProp) optimization technique is used for the parameter selection process. The simulation outputs of the OANN-FDDC technique were examined on an open fabric image database. The experimental results of the OANN-FDDC technique implied a better outcome than the 96.97% of other recent approaches.

Multi-Fault Diagnosis Based on Hybrid Bio-Inspired Algorithm ACO-GA

The fault detection and isolation (FDI) procedure increases the assurance of quality, reliability, and safety of industrial systems. Several faults may appear simultaneously, and the purpose of multi-fault diagnosis is to identify and locate these multiple faults. This work is particularly interested in the diagnosis based on the structural analysis of the system; residuals can be generated and used as fault indicators by model-based fault detection techniques. The isolation is dependent on the structure of the fault signature matrix. A new fault signature that represents the superposition of the fault is produced by simultaneous fault effects, resulting in an additional column in an extended signature matrix. This remedy is rather combinatorial. This research focuses on two methods to isolate multiple faults: (1) A modified enumerative method; (2) A hybrid ant colony optimization algorithm-genetic algorithm (Hybrid ACO-GA) is adapted to the MFD problem which has the advantage of a better research as well as the hybridization with GA.

Unbalanced Distribution Network Cross-Country Fault Diagnosis Method with Emphasis on High-Impedance Fault Syndrome

Unusual fault scenarios can occur on the utility grid in a power system network. Cross-Country Faults (CCFs) connected to the High-Impedance Fault (HIF) syndrome are more prone to occur in forested areas due to thunderstorms, cyclones, and improper vegetation management and tree pruning. Finding and categorizing CCFs associated with HIF syndrome is a great challenge. This study employed the cross-correlation method to reconstruct the signals produced by CCFs with HIF, which were shown to be complicated, aperiodic, asymmetric, and nonlinear. A decreased sensitivity to random noise means that a given modification might not affect equally all component peaks. This allows for more precise signal recovery. The maximum voltage cross-correlation coefficients were carefully evaluated as distinguishing elements in the development of a suggested fault detection technique. The proposed concept was evaluated on a modified imbalanced IEEE 240 bus system under different case studies. These case studies cover a wide range of scenarios, such as the switching of a capacitor bank, feeder energization, and the effects of nonlinear loads under noisy conditions.

Islanding and fault detection of inverter based distributed generations using wavelet packet transform and ensemble

The adoption of Distributed Generation (DG) technology has seen a substantial level of advancement in recent years. This has created potential protection issues in microgrids during faults and islanding. This paper proposes a fault and islanding detection method using wavelet packet transform (WPT) and Ensemble. A new index called Normalized RMS Node power index (NRNPI) is defined based on WPT to differentiate between transient behavior during faults and islanding, and the ensemble acts as a classifier. The ability of the method in detecting islanding and both grounded and non-grounded faults is demonstrated through simulations of a test system in Matlab Simulink. Real time simulation of the proposed method is done in OPAL-RT using UDP-IP communication protocol. The proposed method as an islanding detection technique gives an accuracy of 100% and 99.32 % as a fault detection technique under the test conditions. Results of both simulations and Hardware in Loop (HIL) implementation is promising. The proposed method has the potential of extending the idea for other transient analysis in the microgrid.

Optimized analytical approach for the detection of process-induced defects using acoustic emission during directed energy deposition process

Directed energy deposition (DED), an advanced additive manufacturing (AM) technique, facilitates the production of complex metallic and metal matrix composite structures. The DED process has continuously improved and offers numerous advantages for depositing high-quality coatings with excellent wear and corrosion resistance onto metallic surfaces. The dynamic nature of the process, however, introduces a significant risk of process-induced defects in manufactured parts. This study presents an optimized analytical approach using acoustic emission (AE) based defect detection technique to identify and quantify process-induced cracks during the DED process. The developed signal processing technique enabled the identification of actual AE events associated with cracking, distinguishing them from other disturbances. Experimental validation was conducted using manufactured parts that were longitudinally sectioned to determine the relationship of the crack location with the recorded acoustic signal. The demonstrated correlation of acoustic signatures with cracking illustrates the robustness of the AE technique to identify the process-induced cracking for quality assurance during the DED process.

Image-based novel fault detection with deep learning classifiers using hierarchical labels

One important characteristic of modern fault classification systems is the ability to flag the system when faced with previously unseen fault types. This work considers the unknown fault detection capabilities of deep neural network-based fault classifiers. Specifically, we propose a methodology on how, when available, labels regarding the fault taxonomy can be used to increase unknown fault detection performance without sacrificing model performance. To achieve this, we propose to utilize soft label techniques to improve the state-of-the-art deep novel fault detection techniques during the training process and novel hierarchically consistent detection statistics for online novel fault detection. Finally, we demonstrated increased detection performance on novel fault detection in inspection images from the hot steel rolling process, with results well replicated across multiple scenarios and baseline detection methods.

Research Status and Progress on Non-Destructive Testing Methods for Defect Inspection of Micro-Electronic Packaging

Abstract In a highly competitive and demanding micro-electronics market, non-destructive testing (NDT) technology has been widely applied to defect detection and evaluation of micro-electronic packaging. However, the trend of micro-electronic packaging toward miniaturization, high-density, ultrathin, ultralight, and with small chip footprint, poses an urgent demand for novel NDT methods with high-resolution and large penetration depth, which is utilized for internal defect detection and identification of advanced complicated packages. The conventional NDT methods for micro-electronic packaging mainly include optical visual inspection, X-ray inspection, active infrared thermography, scanning acoustic microscopy (SAM), atomic force microscopy (AFM), laser Doppler vibration measuring technique, scanning SQUID microscopy (SSM), electrical impedance spectroscopy, scanning electron microscope (SEM), and so on. This paper aims to provide a review of addressing their basic principles, advantages, limitations, and application researches in the field of defect inspection of micro-electronic packaging. Moreover, in order to overcome the shortcomings of the existing NDT methods, this paper emphasizes a novel NDT approach, called hybrid ultrasonic-laser digital holographic microscopy (DHM) imaging inspection method, and discusses its basic principle, merits, key techniques, system construction, and experimental results in detail. When some key technical problems can be solved in further research, this method will become a potentially promising technique for defect detection and evaluation of advanced complicated packages.