Fault Detection Technique Research Articles

Fault detection through discrete wavelet transform in overhead power transmission lines

AbstractTransmission lines are a very important and vulnerable part of the power system. Power supply to the consumers depends on the fault‐free status of transmission lines. If the normal working condition of the power system is disturbed due to faults, the persisting fault of long duration results in financial and economic losses. The fault analysis has an important association with the selection of protective devices and reliability assessment of high‐voltage transmission lines. It is imperative to devise a suitable feature extraction tool for accurate fault detection and classification in transmission lines. Several feature extraction techniques have been used in the past but due to their limitations, that is, for use in stationary signals, limited space in localizing nonstationary signals, and less robustness in case of variations in normal operation conditions. Not suitable for real‐time applications and large calculation time and memory requirements. This research presents a discrete wavelet transform (DWT)‐based novel fault detection technique at different parameters, that is, fault inception and fault resistance with proper selection of mother wavelet. In this study, the feasibility of DWT using MATLAB software has been investigated. It has been concluded from the simulated data that wavelet transform together with an effective classification algorithm can be implemented as an effective tool for real‐time monitoring and accurate fault detection and classification in the transmission lines.

Deep Learning and Long-Duration PRPD Analysis to Uncover Weak Partial Discharge Signals for Defect Identification

This study focuses on improving the defect recognition accuracy under weak partial discharges (PDs) in epoxy resin through phase-resolved partial discharge (PRPD) analysis. The method is to refine the data rather than enhance the algorithm. Two measurement conditions are compared until PRPD pattern saturation is achieved: one-minute and one-hour durations. The PD data specifically target three void types located at different positions within the epoxy material. The aim is to evaluate how the presence of weak PDs at the PD extinction voltage (PDEV) influences defect recognition accuracy. This research sheds light on the potential implications of neglecting the significance of weak PD signals in defect detection. A convolutional neural network (CNN) model is trained using PRPD data recorded at the repetitive PD inception voltage (RPDIV) and tested using the new PRPD data from both conditions recorded from a lower PDIV to a PDEV. The trained CNN model achieves a defect recognition accuracy of 100% for a one-hour duration, highlighting the importance of not neglecting weak PD signals. This emphasizes the significance of extended measurement duration and pattern saturation in capturing and analyzing weak PD signals for an improved defect recognition. This study contributes to the advancement of practical applications by understanding the behavior of the epoxy material and enhancing defect detection techniques.

Improving kernel PCA-based algorithm for fault detection in nonlinear industrial process through fractal dimension

Principal Component Analysis (PCA) is a widely used technique for fault detection and diagnosis. PCA works well when the data set has linear characteristics. However, most industrial processes have nonlinear characteristics in their data. Kernel PCA (KPCA) is an alternative solution for such types of data sets. This solution doesn’t come without a cost since one of KPCA’s disadvantages is a large number of observations which results in more occupied storage space and more execution time than the PCA technique. Furthermore, if the data is too large it may minimize the monitoring performance of the KPCA model. Reduced KPCA (RKPCA) is a solution for the conventional KPCA limitations. Firstly, RKPCA can deal with nonlinear characteristics without crucial problems because it is based on the KPCA algorithm with a data reduction part where it keeps most of the data’s information. Thus, by reducing the number of observations RKPCA reduces the occupied storage space and execution time while preserving tolerable monitoring performance. The proposed RKPCA algorithm consists of two parts. First, the large-sized training data set is reduced using the fractal dimension technique (correlation dimension). Afterward, the KPCA model is developed through the obtained reduced training data set. The proposed scheme is applied to the Tennessee Eastman Process and the Cement Plant Rotary Kiln data sets to evaluate its performance in comparison with other algorithms.

Online fault detection for industrial processes through Kalman filter

Industrial processes suffer from a wide range of damages including normal wear, environmental changes, physical structural defects and so on. This paper describes the possibility of system health management based on a prediction model, i.e., state space model realized by Kalman filter. The categorical target was mapped to numerical values in advance for this purpose. To deal with the time-varying and streaming characteristics of the industrial process, the model is applied in an online fashion. Comparing with conventional fault detection techniques, this model has the advantages of monitoring not only the production process of interests through observation equation, but also the structural anomalies described via unseen states estimation. In addition, the process and measurement noises provide valuable information about the unstructured uncertainties caused by other reasons. Experiments have been conducted to valid the effectiveness of the proposed method.

Analysis and Detection of Power System Network Faults with Wavelet Transform

Fault detection technique is important for enhancing protection, stability, reliability and continuity of supply. There are many fault detection techniques implemented in power system such as Fourier transform, wavelet transform, neural networks, etc. This research presents the fault identification and analysis using wavelet transform method as well as estimation of circuit breaker rating. The discrete wavelet transform is implemented for the fault identification in five bus system. Various types of line and ground faults have been considered and studied using Daubechies wavelet function. The first level decomposition is used for the fault analysis. The maximum detailed coefficients for various faults currents are analysed for the fault identification. The wavelet transform algorithm is implemented using MATLAB programming. The five-bus system is modelled in MATLAB Simulink and various faults are simulated. The switching time of fault is taken as 0.05 to 0.1 seconds. The faults have been studied at different points in the system. The fault identification method gives accurate results for different types of faults. The load flow analysis is also done for five bus system and appropriate rating of circuit breaker is estimated.

State-of-the-Art Techniques for Fault Diagnosis in Electrical Machines: Advancements and Future Directions

Electrical machines are prone to various faults and require constant monitoring to ensure safe and dependable functioning. A potential fault in electrical machinery results in unscheduled downtime, necessitating the prompt assessment of any abnormal circumstances in rotating electrical machines. This paper provides an in-depth analysis as well as the most recent trends in the application of condition monitoring and fault detection techniques in the disciplines of electrical machinery. It first investigates the evolution of traditional monitoring techniques, followed by signal-based techniques such as spectrum, vibration, and temperature analysis, and the most recent trends in its signal processing techniques for assessing faults. Then, it investigates and details the implementation and evolution of modern approaches that employ intelligence-based techniques such as neural networks and support vector machines. All these applicable and state-of-art techniques in condition monitoring and fault diagnosis aid in predictive maintenance and identification and have the highly reliable operation of a motor drive system. Furthermore, this paper focuses on the possible transformational impact of electrical machine condition monitoring by thoroughly analyzing each of the monitoring techniques, their corresponding pros and cons, their approaches, and their applicability. It offers strong and useful insights into proactive maintenance measures, improved operating efficiency, and specific recommendations for future applications in the field of diagnostics.

An Improved Algorithm for Topology Identification of Distribution Networks Using Smart Meter Data and Its Application for Fault Detection

This paper proposes a new method for estimating the topology of radial distribution networks and a novel data-driven fault detection technique using smart meter data. An improved method to estimate voltage sensitivity coefficients from smart meter data is proposed that takes into account the variability of the transformer voltage. These coefficients are used in both the topology estimation and the fault detection algorithms. In the topology estimation algorithm, an improved graph learning algorithm, which iteratively constructs the network graph, is devised. In the fault detection algorithm, the objective is to look for sudden changes in the estimated voltage sensitivity coefficients. The performance of the proposed methods is first assessed on randomly generated topologies under various noise conditions. Then, the performance of the algorithms is validated using real smart meter measurements obtained from Australian distribution networks. The results show a significant improvement in the accuracy of estimated topologies compared to the state-of-the-art methods while the fault detection technique is successful in detecting faults using real smart meter data.

Time-reversal technique for pipeline defect detection.

In this paper, time-reversal (TR) technique is proposed for the detection of discrete defects (e.g., leaks and blockages) in pressurized fluid-filled pipelines using active transient waves. The proposed technique is based on the one-dimensional wave equation and decouples the search for the location from that of the size of the defect. Another key feature of this method is that the measured pressure head signal can be truncated so as to eliminate undesirable reflections from unknown or uncertain parts of the pipe system so as not to compromise the accuracy and robustness of the detection. The proposed method is tested through numerical simulations for a single leak, multiple leaks, and multiple mixed-type of defects (i.e., leaks and blockages) in elastic as well as visco-elastic pipe systems. The proposed method is also validated through laboratory experiments wherein leaks and blockages of various sizes are considered. The sensitivity analysis of the method with respect to wave speed and signal-to-noise ratio (SNR) is also performed. In all cases, the method has shown accurate results in terms of localization, classification, and size estimation of the defects even for SNR as low as 0 dB.

Digital twins based fault diagnosis and reconfiguration of high gain topology for utility level PV system

ABSTRACT This paper proposes a reliable fault diagnosis and reconfiguration strategy based on digital twins that is implemented in an enhanced Z-source-based high-gain dc-dc converter for interfacing utility-level photovoltaic (PV) systems. One of the major failure source in a PV system, however, is the interfacing of DC-DC converters. A switch fault detection technique (FDT) is essential to ensure the reliable operation of these converters, as semiconductor switches are among the most important components. However, the literature does not adequately address switch fault detection and reconfiguration strategies in high-gain DC-DC converters. Open-circuit and short-circuit faults in the MOSFET switch can be detected with a simple FDT that measures diode voltage. The presented fault diagnosis scheme relies solely on diode voltage as its signature for detection. FDT and reconfiguration logic isolate switch faults and reconfigure converters to achieve the desired output voltage without introducing any additional components. A simple logic circuit processes the diode voltage and gate driver signal to generate switch and diode fault indicators (FI). The Z-source-based converter’s FDT makes it fault-tolerant. Experimental validation confirms the excellent performance of the proposed fault-tolerant high-gain converter under healthy and faulty conditions.

Design and Development of Experimental Test Rig for Fault Diagnosis of Ball Bearing Using Fuzzy Logic Concept

Rolling element bearings are frequently employed in industry. Many machine-related issues are linked to bearing failures. To minimize downtime and preserve product quality in a highly automated production, an online detection system is required. Condition-based monitoring for deep groove ball bearings is becoming more common. All rotating machinery uses these bearings extensively to accommodate both static and dynamic loads. Techniques for condition-based monitoring can be utilized to diagnose bearing defects to prevent this failure. So, it is important to study these faults present in the machines. The techniques for fault detection will be described in this paper. We are discussing Fast Fourier Transform (FFT) technique. In FFT, we obtain frequency-relationship graphs, and based on peak frequencies, we predict faults. A detailed analysis using the FFT Methodology is done to find out the possible faults, and finally validate with MATLAB software. For the aim of bearing diagnostics, the system performs vibration analysis. More advanced diagnostic systems use fuzzy logic and classification methods to identify the state of the machinery. These techniques enable the creation of more automatic and trustworthy diagnostic systems.

Detection and classification of power system fault in IEEE 30 bus network using wavelet transform and novel hybrid Bees Bayesian Optimization algorithm based Improved convolution Neural network (ICNN)

This work suggests the fault detection and classification technique in an IEEE standard 30 bus network using Wavelet Transform (WT) and Bees Optimization Algorithm (BOA) based Improved Convolution Neural Network (ICNN). The post-fault the voltage and current signals were collected from the Bus via WT, and the proposed model initially identifies important statistical parameters from one cycle of those signals. The WT is a signal analysis algorithm which decomposes power signals into multiple frequency ranges using a series of high-pass and low-pass filters. The extracted features are normalized and supplied as input to ICNN which is used for classification of fault in the system. The ICNN procedure begins with convolution, max poolingand feature analysis, and the final step is feeding the results to the fully connected (FC) layer. A novel hybrid Bees Bayesian Optimization Algorithm (BA-BO) is employed for the tuning of parameters in ICNN. The simulation of IEEE 30 bus system is done by using MATLAB Simulink and then the results were discussed. The proposed technique provides an overall testing accuracy of 99.67% compared to existing methods.

A Novel Hybrid Optimization Approach for Fault Detection in Photovoltaic Arrays and Inverters Using AI and Statistical Learning Techniques: A Focus on Sustainable Environment

Fault detection in PV arrays and inverters is critical for ensuring maximum efficiency and performance. Artificial intelligence (AI) learning can be used to quickly identify issues, resulting in a sustainable environment with reduced downtime and maintenance costs. As the use of solar energy systems continues to grow, the need for reliable and efficient fault detection and diagnosis techniques becomes more critical. This paper presents a novel approach for fault detection in photovoltaic (PV) arrays and inverters, combining AI techniques. It integrates Elman neural network (ENN), boosted tree algorithms (BTA), multi-layer perceptron (MLP), and Gaussian processes regression (GPR) for enhanced accuracy and reliability in fault diagnosis. It leverages its strengths for the accuracy and reliability of fault diagnosis. Feature engineering-based sensitivity analysis was utilized for feature extraction. The fault detection and diagnosis were assessed using several statistical criteria including PBAIS, MAE, NSE, RMSE, and MAPE. Two intelligent learning scenarios are carried out. The first scenario is conducted for PV array fault detection with DC power (DCP) as output. The second scenario is conducted for inverter fault detection with AC power (ACP) as the output. The proposed technique is capable of detecting faults in PV arrays and inverters, providing a reliable solution for enhancing the performance and reliability of solar energy systems. A real-world solar energy dataset is used to evaluate the proposed technique with results compared to existing detection techniques and obtained results showing that it outperforms existing fault detection techniques, achieving higher accuracy and better performance. The GPR-M4 optimization justified its reliably among all the models with MAPE = 0.0393 and MAE = 0.002 for inverter fault detection, and MAPE = 0.091 and MAE = 0.000 for PV array fault detection.

A novel enhanced deep learning-based fault diagnosis approach for cascaded multilevel inverter

With the widespread use of power electronic converters like Multilevel Inverters (MLI) in various applications, several challenges arise due to increased switch count, capacitor surge current, and reverse current from inductive loads. These factors lead to the onset of faults, which negatively affect the reliability of MLI. Hence, an efficient fault detection and fault tolerant technique are necessary to ensure the reliability of the inverter. The deep learning-based fault detection approach is proposed for a 15-level Cascaded H-Bridge (CHB) MLI to address this issue. The proposed technique involves pre-processing with an Adaptive Hilbert-Huang filter, segmentation using Partial Differential Equation (PDE), feature extraction using Scale-Invariant Feature Transform (SIFT), and classification of fault signals with a Crow Swarm Algorithm (CSA) optimized Convolution Neural Network (CNN) classifier. To evaluate the effectiveness of the proposed approach in fault classification, four complex fault types were considered using MATLAB. The results show that CSA-optimized CNN offers a remarkable classification accuracy of 99.84%.

Reduction of False Positives for Runtime Errors in C/C++ Software: A Comparative Study

In software development, early defect detection using static analysis can be performed without executing the source code. However, defects are detected on a non-execution basis, thus resulting in a higher ratio of false positives. Recently, studies have been conducted to effectively perform static analyses using machine learning (ML) and deep learning (DL) technologies. This study examines the techniques for detecting runtime errors used in existing static analysis tools and the causes and rates of false positives. It analyzes the latest static analysis technologies that apply machine learning/deep learning to decrease false positives and compares them with existing technologies in terms of effectiveness and performance. In addition, machine-learning/deep-learning-based defect detection techniques were implemented in experimental environments and real-world software to determine their effectiveness in real-world software.

Recurrent neural network based sensor fault detection and isolation for nonlinear systems: Application in PWR

Real-time monitoring of sensors is crucial in attaining consistent process performance with strict safety and eco-friendly measures. This article presents a fault detection and isolation (FDI) technique that diagnoses sensor faults regardless of the utilized model structure. A Recurrent Neural Network (RNN) is utilized to estimate state variables with the inputs and outputs of the process. RNNs build predictive models of the process. Then, a bank of residuals of state variables is obtained such that each residual is responsive to a subsection of faults and unresponsive to others. Consequently, an exclusive fault signature is attained for each fault case. One of the merits of the proposed procedure is that it does not necessitate the fault history of the process or first principle models contrasting to other existing outcomes in the literature. The efficacy of the proposed FDI methodology is studied on a highly nonlinear pressurized water nuclear reactor model.

Development of Rolling Element Bearing Test Bench Facility And Application of Wavelet Transform for Fault Diagnosis

The aim of this experimental study is to develop an efficient rolling element bearing fault detection technique which could be effectively utilized for easy interpretation of bearing signal data. This paper deals with a new scheme for the diagnosis of localized defects in rolling element bearing based on Wavelet Transform. Vibration signal for healthy bearing, bearing with outer race fault, inner race fault and ball fault were acquired from a bearing test bench fabricated at Naval Institute of Aeronautical Technology, Kochi. The vibration signal was acquired using an accelerometer, control card and Data acquisition card. The acquired signal was further processed using a time frequency analysis technique known as Wavelet Transform. The experiments were carried out using different mother wavelets. However, defect features were best obtained using Complex Gaussian Wavelet of order 8, thereby rendering easier interpretation of the analysed data.

Enhancing Wind Turbine Performance: Statistical Detection of Sensor Faults Based on Improved Dynamic Independent Component Analysis

Efficient detection of sensor faults in wind turbines is essential to ensure the reliable operation and performance of these renewable energy systems. This paper presents a novel semi-supervised data-based monitoring technique for fault detection in wind turbines using SCADA (supervisory control and data acquisition) data. Unlike supervised methods, the proposed approach does not require labeled data, making it cost-effective and practical for wind turbine monitoring. The technique builds upon the Independent Component Analysis (ICA) approach, effectively capturing non-Gaussian features. Specifically, a dynamic ICA (DICA) model is employed to account for the temporal dynamics and dependencies in the observed signals affected by sensor faults. The fault detection process integrates fault indicators based on I2d, I2e, and squared prediction error (SPE), enabling the identification of different types of sensor faults. The fault indicators are combined with a Double Exponential Weighted Moving Average (DEWMA) chart, known for its superior performance in detecting faults with small magnitudes. Additionally, the approach incorporates kernel density estimation to establish nonparametric thresholds, increasing flexibility and adaptability to different data types. This study considers various types of sensor faults, including bias sensor faults, precision degradation faults, and freezing sensor faults, for evaluation. The results demonstrate that the proposed approach outperforms PCA and traditional ICA-based methods. It achieves a high detection rate, accurately identifying faults while reducing false alarms. It could be a promising technique for proactive maintenance, optimizing the performance and reliability of wind turbine systems.

An innovative deep neural network coordinating with percussion-based technique for automatic detection of concrete cavity defects

Cavity defects are usually invisible in concrete, failure to detect and repair them in time could significantly weaken the load-bearing capacity of engineering structures, potentially threatening the structural safety during the construction and operation periods. Therefore, it is essential to explore efficient approaches to detect concrete cavity defects. In this study, we propose a novel method to detect concrete cavity defects based on percussion and deep learning (DL) techniques. First, the percussion sound dataset was collected from concrete specimens with different types of precast cavity defects. Next, an innovative DL framework named the multi-scale convolutional bidirectional memory network (MCBMNet) was developed for classifying these percussion sound signals associated with specified concrete cavity defects. The MCBMNet utilizes the multi-scale convolutional operation to generate various scale features directly from raw percussion sounds, and then employs the bidirectional memory units to capture intrinsic feature connections and enhance feature separability, thus improving prediction accuracy and robustness. Finally, two experimental cases, including water-free and water-filled conditions, were designed to test the feasibility of the proposed method considering the influences of humidity environment in reality. Experimental results well demonstrate the excellent performances of the proposed MCBMNet for concrete cavity defect detection, with its average accuracy over 98% in both cases. Additionally, the anti-noise capacity and adaptability of MCBMNet were thoroughly investigated, which outperforms conventional learning algorithms.

Investigating Machine Learning and Control Theory Approaches for Process Fault Detection: A Comparative Study of KPCA and the Observer-Based Method.

The paper focuses on the importance of prompt and efficient process fault detection in contemporary manufacturing industries, where product quality and safety protocols are critical. The study compares the efficiencies of two techniques for process fault detection: Kernel Principal Component Analysis (KPCA) and the observer method. Both techniques are applied to observe water volume variation within a hydraulic system comprising three tanks. PCA is an unsupervised learning technique used for dimensionality reduction and pattern recognition. It is an extension of Principal Component Analysis (PCA) that utilizes kernel functions to transform data into higher-dimensional spaces, where it becomes easier to separate classes or identify patterns. In this paper, KPCA is applied to detect faults in the hydraulic system by analyzing the variation in water volume. The observer method originates from control theory and is utilized to estimate the internal states of a system based on its output measurements. It is commonly used in control systems to estimate the unmeasurable or hidden states of a system, which is crucial for ensuring proper control and fault detection. In this study, the observer method is applied to the hydraulic system to estimate the water volume variations within the three tanks. The paper presents a comparative study of these two techniques applied to the hydraulic system. The results show that both KPCA and the observer method perform similarly in detecting faults within the system. This similarity in performance highlights the efficacy of these techniques and their potential adaptability in various fault diagnosis scenarios within modern manufacturing processes.

Autonomous fault detection and diagnosis for permanent magnet synchronous motors using combined variational mode decomposition, the Hilbert-Huang transform, and a convolutional neural network

The continuous and online monitoring of the condition of electrical machines is key to their safe operation. This study introduces a novel fault detection and diagnosis technique for continuous monitoring of faults in permanent magnet synchronous motors (PMSM). The proposed method relies solely on built-in sensors (stator phase currents only) to detect three types of faults: inter-turn short circuit, partial demagnetisation, and static eccentricity. Our fault detection and diagnosis strategy was developed by combining variational mode decomposition (VMD), the Hilbert-Huang transform (HHT) and a convolutional neural network (CNN). The VMD is first applied to the stator phase current signals to analyse the characteristic behaviour of the current signals by decomposing the current signals into several intrinsic mode functions. The intrinsic mode functions of the healthy and faulty signals are compared, and that with the frequency shift characteristics is selected. HHT is then applied to extract the fault feature by calculating the instantaneous frequency. Finally, the instantaneous frequency feature is fed into the CNN, which is designed to detect and classify motor faults. Experimental results clearly show that the variation of the instantaneous frequency of the PMSM, working at different operating states, can be utilised for condition monitoring and fault detection.