Early Fault Detection Research Articles

Hybrid DGA Method for Power Transformer Faults Diagnosis Based on Evolutionary k-Means Clustering and Dissolved Gas Subsets Analysis

Considered as the heart of electrical power transmission and distribution networks, power transformers are essential part of the electricity transmission grid. Among the condition monitoring and fault diagnosis tools for these machines, dissolved gas analysis (DGA) has proven its effectiveness in their early detection and classification of faults. Up to date, many methods have been proposed in the literature for the interpretation of DGA data, classified into traditional and intelligent methods. This paper proposes a two-steps hybrid method, which uses the strengths of both methods. The approach uses the evolutionary k-means clustering algorithm based on the genetic algorithm for subset formation and subset analysis by human expertise. In the diagnostic procedure, to determine the condition of a sample, the subset to which it belongs is first identified and then the corresponding diagnostic sub-model is applied. The proposed method has been implemented with 595 DGA data, tested on 254 DGA data and validated on the International Electrotechnical Commission (IEC) TC10 database. Their performances were evaluated and compared with existing traditional, intelligent and hybrid methods. From the results obtained with the IEC TC10 database, the newly proposed approach depicts the best overall diagnosis accuracies. Indeed, the best performance is achieved with the proposed method compared to other models in the literature, with diagnostic accuracy of 98.29% compared to 88.89% of the Gouda triangle method, to 88.03% of the Hyosun Corporation gas ratio method or to 86.32% of the three ratios technique.

Energy-aware and Context-aware Fault Detection Framework for Wireless Sensor Networks

Wireless sensor networks (WSNs) consist of many sensor nodes that are densely deployed throughout a randomized geographical area to monitor, detect, and analyze various physical phenomena. The primary obstacle encountered in WSNs pertains to the significant reliance of sensor nodes on finite battery power for wireless data transfer. Sensors as a crucial element inside Cyber-Physical Systems (CPS) renders them vulnerable to failures arising from intricate surroundings, substandard manufacturing, and the passage of time. Various anomalies can appear within WSNs, mostly attributed to defects such as hardware and software malfunctions and anomalies and assaults initiated by unauthorized individuals. These anomalies significantly impact the overall integrity and completeness of the data gathered by the networks. Therefore, it is imperative to provide a critical mechanism for the early detection of faults, even in the presence of constraints imposed by the sensor nodes. Machine Learning (ML) techniques encompass a range of approaches that may be employed to identify and diagnose sensor node faults inside a network. This paper presents a novel Energy-aware and Context-aware fault detection framework (ECFDF) that utilizes the Extra-Trees algorithm (ETA) for fault detection in WSNs. To assess the effectiveness of the suggested methodology for identifying context-aware faults (CAF), a simulated WSN scenario is created. This scenario consists of data from humidity and temperature sensors and is designed to emulate severe low-intensity problems. This study examines six often-seen categories of sensor fault, including drift, hard-over/bias, spike, erratic/precision, stuck, and data loss. The ECFDF approach utilizes an Energy-Efficient Fuzzy Logic Adaptive Clustering Hierarchy (EE-FLACH) algorithm to select a Super Cluster Head (SCH) within WSNs. The SCH is responsible for achieving optimal energy consumption within the network, and this selection process facilitates the early detection of faults. The results of the simulation indicate that the ECFDF technique has superior performance in terms of Fault Detection Accuracy (FDA), False-Positive Rate (FPR), and Mean Residual Energy (MRE) when compared to other detection and classification methods.

Incipient Fault Detection in a Hydraulic System Using Canonical Variable Analysis Combined with Adaptive Kernel Density Estimation.

Incipient fault detection in a hydraulic system is a challenge in the condition monitoring community. Existing research mainly monitors abnormal working conditions in hydraulic systems by separately detecting the key working parameter, which often causes a high miss warning rate for incipient faults due to the oversight of parameter dependence. A principal component analysis provides an effective method for incipient fault detection by taking the correlation of multiple parameters into consideration, but this technique assumes the systems are Gaussian-distributed, making it invalid for a dynamic non-Gaussian system. In this paper, we combine a canonical variable analysis (CVA) and adaptive kernel density estimation (AKDE) for the early fault detection of nonlinear dynamic hydraulic systems. The collected hydraulic system data set was used to construct the typical variable space, and the state space and residual space are divided to represent the characteristics of different correlations between the two variables, which are quantitatively described using Hotelling's T2 and Q. In order to investigate the proper upper control limits, AKDE was utilised to estimate the underlying probability density functions of T2 and Q by taking the nonlinearity of the hydraulic system variables into consideration. The advantages of the proposed approach for incipient fault detection are illustrated via a marine power plant lubrication system.

The early detection of short-circuit fault in the active low-voltage AC system

The distributed power supply decentralized access to the low-voltage side of the distribution network transforms the existing low-voltage AC system into an active low-voltage AC system. The original low-voltage AC short-circuit detection methods are no longer applicable. In this paper, a short-circuit early detection method suitable for different branch types in the active low-voltage AC system is proposed. Based on the analysis of the transient characteristics of the active low-voltage AC short- circuit, a short-circuit time-varying vector theory and a fast method for solving its modulus length are proposed. Then, we construct a branch state variable containing time-varying vector modulus length difference information of current and voltage. The constructed branch state variable can reflect the characteristics of short-circuit state in real-time, which greatly improving the reliability of fast short-circuit detection. Finally, the early detection of short-circuit fault can be realized by comparing the branch state variable with the set detection threshold. The physical experiment system experiments show that the proposed short-circuit early detection method is effective and reliable. Compared with the existing short-circuit fast detection method, the detection time is reduced by 3–5 ms. Moreover, the short-circuit breaking and current limiting can be achieved by combining with the fast protection mechanism, and the current limiting ratio is as low as 0.33.

Anomaly Detection in Air Handling Units using Motor Current Signal Imaging for Belt Looseness Detection

An air handling unit (AHU) is a critical component of heating, ventilation, and air conditioning (HVAC) systems. Slip of AHU is an intuitive key feature for monitoring a belt looseness fault of an AHU. However, fluctuating rotation speed of the motor and fan makes slip hard to monitor. Since the role of the belt is to deliver torque between the motor and fan, this leads to change of the motor current signal. This paper suggests a normal data-based anomaly detection that utilizes motor current signal imaging to identify belt looseness in AHUs. The overall process proceeds as followings: (1) converting 1-dimensional motor current signal into 2-dimentional image in the amplitude domain, (2) extracting features of normal data by applying convolutional neural networks, (3) calculating health index to detect the belt looseness fault. The technique to transform time-series current data to an image is based on its histogram. The image is obtained by the inner product of the histogram obtained from a current signal and its transpose. The effect of torque load on a motor induces an amplitude modulation of the current signal. Current signal imaging based on histogram provides the fault features in a robust way. To validate the proposed method, a case study using an AHU testbed is conducted. The results demonstrate that the proposed method can detect belt looseness faults in AHU using only normal data, providing an approach for early fault detection in HVAC systems.

Generalizability of machine learning-based fault classification for residential air-conditioners

Residential air-conditioning systems are subject to soft faults, which can reduce their efficiency, capacity, and life expectancy, without being detected by the occupants. Early detection and diagnosis of faults could potentially be done by using machine learning (ML) on measurable features of the air-conditioner, such as refrigerant temperatures and pressures. This paper describes a novel approach to applying ML, which is to train a fault classifier using data from one system, and apply it to different systems. If this is effective, it can significantly reduce the cost of widescale application of ML fault classification. The support vector machine (SVM) algorithm was used. An additional question for practical application is whether the number of sensors required for input feature measurement can be reduced. This work also tests the feature reduction effect, in combination with applying an ML classifier trained on one system, to classify faults in two other systems. The results are disappointing, showing that fault classification is poor when the SVM classifier is trained on a different system than it is applied to. However, with additional work, this method has potential for improvement, possibly to a point where it can be a viable approach for field deployment.

Static and Dynamic Eccentricity Faults Diagnosis in PM Synchronous Motor Using Planar Search Coil

Eccentricity fault in electrical motor causes increased noise and vibration, and degradation of motor characteristics. Thus, early detection of the eccentricity fault is required. Motor current signal analysis, a common fault detection method, may fail to detect eccentricity faults, depending on the type of eccentricity. This paper proposes an eccentricity fault detection method using a new planar search coil (PSC). The model of PSC and the contribution of eccentricity to PSC are presented. PSC is a new type of search coil that generates a voltage signal from magnetic flux. The output signal of PSC is changed by the inductance deviation, a result of the rotor eccentricity. Finally, the experimental verification of fault detection using PSC is shown. The eccentricity fault detection method using PSC is proven to diagnose both dynamic eccentricity and static eccentricity faults.

Подтверждение необходимости и целесообразности применения различных методик интерпретации результатов газохроматографического анализа силовых трансформаторов

The method of analysis of gases dissolved in oil is one of the most informative methods of early detection of defects in power oil-filled transformers. Now, the decision on the state of the transformers is based on the method of interpretation of the results of the gas chromatographic (GC) according to the guideline document RD 153-34.0-46.302-00. At the same time, there are situations when this document does not provide accurate analysis results. Thus, it is proposed to use several methods of interpreting the results of the GC to obtain refined conclusions. The purpose of the study is to substantiate the need to use various methods of interpretation of the results of gas chromatographic analysis of the oil of power transformers. The following methods for dissolved gas analysis have been reviewed: Rogers Ratio Method, IEC 60599 Standard Method, Doernenburg Ratio Method, Duval Triangle Method, ETRA method, as well as the guideline document RD 153-34.0-46.302-00 method adopted in the Russian Federation. These methods are implemented in various power companies of the Russian Federation, such as PJSC “Rosseti MR”, PJSC “FGC UES” and PJSC “Mosenergo”. The article reveals the need to consider the totality of all available methods and techniques based on RD 153-34.0-46.302-00 and development of training samples. The scientific novelty and significance of the conducted research lies in the confirmation of the need to use a set of methods for interpreting the results of the GC. An algorithm for the complex application of the methods described in the article for interpreting the results of the GC and training samples has been formed. The obtained results allow us to consider the possibility to develop software for the complex application of the methods of interpretation of the results of the GC described in the article and the formation of training samples based on the developed algorithm.

Early detection of PMSM faults at static and dynamic operating points by using artificial neural network for automated electric vehicle

Early detection of PMSM faults at static and dynamic operating points by using artificial neural network for automated electric vehicle

An improved detection method of GNSS faults with fractional information divergence

Early detection of faults guarantees the integrity of navigation systems. The standard receiver autonomous integrity monitoring (RAIM) incorporated with the Kullback-Leibler divergence (KLD) has been proposed to improve detection performance. However, the KLD-RAIM cannot ensure a high detection rate in the presence of small or growing faults. This paper broadens the existing KLD-RAIM to further enhance the detection ability for satellite navigation-based aviation applications. The Cumulative residual KL information (CRKL) replaces the KLD to quantify the dissimilarity between the current and normal cumulative distribution functions for the CRKL-RAIM construction. Then CRKL is extended to fractional orders using Riemann–Louville (RL) calculus to form fractional CRKL-RAIM for detection sensitivity enhancement. Simulations using static GNSS station data were conducted to compare four methods: the standard RAIM, existing KLD-RAIM, the proposed CRKL-RAIM, and fractional CRKL-RAIM. When dealing with small step faults, the detection sensitivity increases progressively, with the fractional CRKL-RAIM capable of detecting 10-meter faults. Regarding growing faults, the standard RAIM fails to detect and KLD-RAIM barely detects faults around 600 s with 50% missed detection rate. By contrast, CRKL-RAIM and fractional CRKL-RAIM detect faults around 100 and 200 s in advance, respectively, without any missed detection events.

Wideband Vibro-Acoustic Modulation for Crack Detection in Wind Turbine Blades

Wind turbines (WT) are a popular method used in energy production, but blade failure and maintenance costs pose significant challenges for the industry. Early detection of blade defects is vital to prevent collapse. This paper examines the modulation of blade vibrations via low-frequency blade rotation, mirroring the vibro-acoustic modulation (VAM) method. Specifically, we study the modulation of blade vibrations, which are generated via blade interactions with air turbulence and have a wide frequency range. These vibrations are modulated by the alternating bending stress experienced during blade rotation. For the simulation of VAM, we employ a simple breathing crack model, which considers a mechanical oscillator with parameters that are periodically changed in response to low-frequency blade rotation. The modulation of the wideband signal by blade rotation can be extracted using the detection of envelope modulation on noise (DEMON) algorithm. This model was applied for the estimation of the modulation of a large (52-m-long) WT blade. Steel specimens have been used in laboratory experiments to demonstrate the feasibility of VAM using a probe broadband noise signal. This paper presents the first work to experimentally and theoretically apply wideband signals in VAM. It further explores the analysis of the use of natural vibrations within VAM for the SHM of WT blades.

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.

Early Bearing Fault Detection Using EEMD and Three-Sigma Rule Denoising Method

Rotating electrical machines have several physical phenomena. Vibration is one of the important phenomena in the operation of rotating electrical machines. In addition, the vibration signal is considered an important source to have good information on the state of rotating electrical machinery. But this signal is rich in noise, especially under the presence of the bearing fault. This paper proposes a bearing fault diagnosis method based on EEMD and a denoising method based on three-sigma rule. In the first step, the EEMD decomposed the vibration signal into several components called Intrinsic Mode Functions (IMFs). After the calculation of the kurtosis of each IMF component, the signal is reconstructed by choosing components with higher values. To enhance periodic impulses, the three-sigma rule de-noising is applied to the reconstructed signal. As a final step, the envelope spectrum is used to determine the fault characteristic frequency. As a result of testing the bearing with inner race fault and the bearing with outer race, it was verified that the proposed approach suppressed noise effectively and extracted rich fault information from the vibration signals of bearings compared to the EEMD.

Guidelines for designing a digital twin for Li-ion battery: A reference methodology

The integration of digital technologies is causing a significant change in the energy sector. These innovations have transformed traditional energy grids into intelligent grids. As a result, the digital replica of Battery Energy Storage Systems (BESS) has become one of the most crucial components in the energy sector. Digital twin technology enables the seamless integration of BESS into intelligent grids and offers numerous benefits, such as easy identification and prediction of faults, real-time system monitoring, optimization, temperature regulation, and estimation of parameters. As a result, the overall performance of BESS is improved by the digital twin technology. Consequently, this paper discusses the general guidelines that must be followed to develop a digital twin for a Li-ion BESS successfully. The main function is to define how to design a digital twin able to optimize the system and facilitate early and predictive fault detection and diagnosis.

A novel semi-supervised fault detection and isolation method for battery system of electric vehicles

The detection and isolation of early and minor faults in vehicle battery systems is vital to safe driving and improving power utilization. This paper proposes a data-driven model to achieve accurate, early, and economical fault detection and isolation. The model is based on kernel principal component analysis (KPCA), which maps complex nonlinear data from the input space into a high-dimensional feature space to gain a detection model with good performance. To overcome the difficulty of hyperparameter selection, KPCA is trained using Bayesian Optimization (BO) iterations with a small amount of labeled data and a large amount of unlabeled data. This step can obtain the optimal hyperparameter to greatly improve the model fault detection capability, which is beneficial for detecting both early faults and minor faults. In addition, a unified contribution graph based on the partial differentiation of KPCA was adopted to build a reasonable isolation scheme. The semi-supervised model of KPCA based on Bayesian Optimization and contribution graph is developed to reveal the relationship between fault and variable. Finally, the proposed method is fully tested on four fault datasets and the results prove the excellent detection capability in the early stage of faults compared with other methods and the accurate fault isolation capability from the occurrence to the end of the fault.

Time-Series Transfer Learning: An Early Stage Imbalance Fault Detection Method Based on Feature Enhancement and Improved Support Vector Data Description

Early stage fault detection plays a pivotal role in Industrial equipment accidents avoidance and scientific maintenance. While limited by the complex operation background, its application encounters with the conundrum of fault feature indistinctness. To address the challenge, a time-series transfer learning (TSTL) method is proposed, which contains two phases: first, early stage series are transferred to their corresponding serious stage for fault feature enhancement. Moreover, due to the improvement of model structure and loss function, the limitation of mismatched working condition is well-weaken. Second, a transferred fault mode recognition model is trained by using transferred normal series that provides a novel solution for data imbalance. Finally, the TSTL method is verified by actual vibration datasets of power pole tower bolts. Its superiority in feature transfer and fault detection is confirmed by several groups of comparative experiments and results demonstrate TSTL outperforms mainstream methods.

Revolutionizing condition monitoring techniques with integration of artificial intelligence and machine learning

The field of condition monitoring has made great strides in the development of innovative techniques that facilitate early detection and diagnosis of faults in equipment. Present article provides a comprehensive overview of condition-monitoring technologies, namely, ultrasound technique and radiography. By providing detailed insight into the strengths and weaknesses of mentioned techniques, present work highlights their suitability for various industrial applications and how artificial intelligence and machine learning can be integrated to form multimodal systems that improve overall performance. Additionally, it evaluates developments in artificial intelligence and machine learning that enable accurate and reliable condition monitoring systems.

Crack detection of a modified high contact ratio gear

For the purpose of vibration-based fault detection and to avoid gear failure consequences it is necessary to investigate the possibility of early fault detection. A reduction in the gear time-varying mesh stiffness (TVMS) due to the existence of a crack can be used to detect and evaluate tooth damage. The reduction in TVMS has an impact on the system’s dynamic response signal, and then the used fault detection indicators are affected. The current article sheds light on the fault detection possibility of high contact ratio (HCR) gears as compared to those standard ones of low contact ratio (LCR). The influence of modifying the gear tooth addendum dimension for obtaining a high contact ratio on the possibility of early fault detection is studied. Twenty different crack cases are examined by using both the LCR and HCR design cases. For every crack case, the mesh stiffness is evaluated and introduced to the used gear dynamic model for obtaining the system’s dynamic response. The TVMS evaluation is presented for both the LCR and HCR gear cases. Four fault detection indicators, namely the RMS, kurtosis, peak value and crest factor are applied as different measures to enhance the research outcome. The results show that the studied indicators will have less increase when the crack size increases in the HCR design than in the LCR one. Therefore, the HCR design will be less sensitive or more difficult to detect from the fault detection perspective. Lowering the amount of signal noise is applied to the HCR case to examine the impact on enhancing the obtained indicators. The four applied indicators show different behaviours with reducing noise.

Data-driven condition monitoring of two-stroke marine diesel engine piston rings with machine learning

ABSTRACT Maintaining the condition of a vessel and its equipment guarantees the scheduled completion of voyages and the safety of the crew. This paper presents condition monitoring techniques for early detection of faults related to piston rings in remote cylinders of two-stroke marine diesel engines. Operational sensor data from the main engine of a container ship are provided by a shipping company. A graphical approach complimented by correlation heatmaps and feature importance from gradient boosting trees are used for feature selection. Support Vector Machine, Random Forest and Extreme Gradient Boosting Trees are tested for residual generation from the nominal behavior. The residual time series gives a good indication of the degradation of the system and can be used for alarm raising under strict rules. It is proven that the proposed method could alert the engine crew of a change in the condition of the piston rings much earlier than existing methods.

Wind Turbine Gearbox Diagnosis Based on Stator Current

Early detection of faults in wind energy systems can reduce downtime, operating, and maintenance costs while increasing productivity. This paper proposes a method based on the analysis of generator stator current signals to detect faults in a wind turbine gearbox equipped with a doubly fed induction generator (DFIG). A localized parameter model was established to simulate the vibratory response of a two-stage gear system under healthy and faulty conditions. The simulation was performed in the MATLAB/Simulink environment. The results include a detailed analysis of the mechanical part of the gearbox, highlighting mesh stiffness, output speed, and accelerations. Additionally, the electrical part was evaluated based on the current supplied by the doubly fed induction generator. The results were presented in the case of healthy gears and in the presence of faults such as a broken or cracked tooth. Fast Fourier transform (FFT) analysis was employed to detect gear defects in the stator current signal. The presence of a crack or broken tooth in the gearbox induces modulation of the DFIG stator current signals according to the shaft frequencies corresponding to the faulty gear. These findings provide a preliminary basis for the detection and diagnosis of this type of failure.