Fault Indicators Research Articles

Distribution grid fault diagnosis based on fault indicator and random matrix

The analysis and utilization of massive recording data of distribution grid fault indicator is beneficial to improve the effect of distribution grid fault diagnosis. In this paper, the distribution grid monitoring are realized by the random matrix theory (RMT) of high dimensional statistical analysis. The fault diagnosis method based on RMT has the advantages of no need for detailed distribution grid topology, comprehensive utilization of wide-area spatiotemporal data, and observation from a muti-dimensional view. By explaining the application principle of limit spectrum distribution function, the linear eigenvalue statistics (LES) is proposed as the state monitoring index. Distribution grid fault diagnosis based on fault indicator and RMT provides a new data-driven method for distribution grid fault diagnosis while efficiently utilizing massive fault indicator data.

A method to distinguish local demagnetization and short-circuit faults of six-phase interior permanent-magnet machine based on voltage in synchronous frame

Short-circuit fault and local demagnetization faults are two common fault types of permanent-magnet machines, aiming to improve the reliability of multi-phase PM machines in safety-critical applications, a method to distinguish these two kinds of faults in six-phase interior PM machines is proposed. The voltage characteristics in synchronous frame of local demagnetization fault and short-circuit fault are investigated, and the influences of load current and internal power factor angle on the voltage harmonic characteristics are analyzed. To solve the problem of the interaction between the extracted harmonic amplitudes in integer and non-integer orders, the average and variance of the harmonic coefficients of the voltages in synchronous frame are utilized to distinguish two types of faults. Through comparing the fault indices under different load conditions of the six-phase interior PM machine, the distinguishing ability of the method is validated.

Optimal layout model of feeder automation equipment oriented to the type of fault detection and local action

In feeder automation transformation there are difficulties in equipment and location selection. To help with this, an optimal layout model of feeder automation equipment oriented to the type of fault detection and local action is proposed. It analyzes the coordination relationship of the three most common types of automation equipment, i.e., fault indicator, over-current trip switch and non-voltage trip switch in the fault handling process, and the explicit expressions of power outage time caused by a fault on different layouts of the above three types of equipment are given. Given constraints of power supply reliability and the goal of minimizing the sum of equipment-related capital investment and power interruption cost, a mixed-integer quadratic programming model for optimal layout is established, in which the functional failure probability of equipment is linearized using the 3delta principle in statistics. Finally, the basic characteristics of the proposed model are illustrated by different scenarios on the IEEE RBTS-BUS6 system. It can not only take into account fault location and fault isolation to enhance user power consumption perception, but also can guide precise investment to improve the operational quality and efficiency of a power company.

Comparative study of field-oriented control of an induction machine with inter-turn short-circuit in healthy and faulty cases

The vector control of an induction machine is among the most used controls, the inter-turn short-circuit fault is a very common fault in this type of machine. Vector controllers use current regulators that are highly insensitive to internal disturbances and can therefore mask the onset of a short-circuit fault. In this article we propose simulations allowing to study the behavior of the indirect field-oriented control when an inter-turn short-circuit fault appears. The primary aim of this article is a comparative study of the effect of this type of defect on estimated quantities such as rotor flux, slip and electromagnetic torque. The results of the simulation show that these estimated quantities can be used as fault indicators instead of the stator currents. The results of this comparative study also show that the ripple of the estimated rotor flux is a very good indicator of this type of fault in this type of regulator-machine association.

Identification and classification of faults using fuzzy logic controller in transmission line

This manuscript discusses the identification and categorization of faults in transmission network system with fuzzy logic controller (FLC) with high-speed digital protective relay, which can be used for real-time data analysis. The FLC uses signals for the extraction of original signals for multi-resolution analysis. The proposed FLC technique detects the fault occurrence based on fault index. The stoutness of technique is demonstrated on transmission line models under different fault situations on MATLAB Simulink platform for the post-fault current of all three phases the end of line. In the projected technique, different rules have been structured for phase and ground faults. Simulation has been carried out on various faults type to test the effectiveness of the method which is used for a power station of 400 kV voltages with a 300 km long line. The extensive simulation effort has been carried out to recognize and categorize the types of faults using projected methodology with higher efficiency. This method is appropriate for high-impedance faults carried out in high-voltage line. The result obtained from Simulation led to the conclusion that the presented work is quite robust for medium and long lines.

A novel nonlinear vibration feature-based approach for evaluating bolt-loosening faults in aerospace structures

Aerospace structures such as aircraft and satellites are frequently subjected to bolt-loosening faults in service, which will seriously affect structural integrity, reliability and safety. In this paper, the development of a novel approach based on nonlinear vibration features is presented to evaluate potential bolt-loosening faults in these structures. Firstly, the complex aerospace structure is decomposed into some simple T-type substructures based on structural configurations and features. Then, a general T-type multi-degree-of-freedom (MDOF) model simulating bolt-loosening faults and nonlinear boundaries as nonlinear damper-spring components is built for nonlinear vibration analysis. After that, two novel fault features, which are functions of structural properties, nonlinear output spectra and bolt-loosening fault-induced forces, are defined to form a novel bolt-loosening fault indicator. Finally, a novel approach with detailed operational procedures is proposed, and its effectiveness and superiority are verified through experimental studies on a simple lab structure with bolt-loosening faults and nonlinear boundaries.

Clustering Wind Turbines for SCADA Data-Based Fault Detection

Condition monitoring systems are commonly employed for incipient fault detection of wind turbines (WTs) to reduce downtime and increase availability. Data from supervisory control and data acquisition (SCADA) systems offer a potential monitoring solution. Multi-turbine approaches, which merge variables recorded from different WTs on the same wind farm, have been developed to improve fault detection performance by reducing the influence of variations in environmental conditions. However, in complex terrain, environmental conditions vary among WTs. Moreover, manual control (e.g., maintenance and curtailment) can also raise false alarms in some WTs. Here, the false alarm characteristics of a wind farm in complex terrain are investigated. A clustering-based multi-turbine fault detection approach is proposed, consisting of three steps: WT clustering, single-turbine modeling, and fault indicator calculation. First, k-medoids clustering with dependent multivariate dynamic time warping is applied for WT clustering. Then, an autoregressive neural network is used to construct a single-turbine model. Finally, residuals between median values of the model output of all WTs in the same cluster and the target WT are used to calculate the anomaly level. Evaluation results for real large-scale SCADA data confirm that the proposed approach raises fewer false alarms without degrading detection performance.

DPMSLM Demagnetization Fault Detection Based on Texture Feature Analysis of Grayscale Fusion Image

This study investigates a novel image morphology texture feature extraction method to realize the demagnetization fault location and severity detection of double-sided permanent magnet synchronous linear motor (DPMSLM). Initially, according to the constraints of DPMSLMs topology structure, the three lines magnetic density signal in motor air gap is extracted by finite element analysis as effective fault signal. Then, the grayscale fusion image (GFI) method is introduced to transform 1D data signal to 2D fused grayscale image which can better describe the demagnetization fault information. The unique features are visualized using the image enhancement techniques, and the image morphology texture features such as the area, Euler number, perimeter operator, correlation of binary image and so on can be extracted to constitute the demagnetization fault indexes. In addition, fisher score (FS) is used for feature optimization which can reduce the feature dimension. Furthermore, the two-level multiverse optimization support vector machine (MVO-SVM) algorithm is established to conduct demagnetization fault classification. Comparison experiments with other classifiers show that the MVO-SVM has a high fault identification accuracy of more than 98.3% and low running time less than 2.57s. Finally, the motor prototype experiment results show that the proposed method can accurately identify the location and severity of DPMSLM demagnetization faults, and it is an effective and feasible method which can be applied in DPMLM batch demagnetization inspection before delivery.

Fault indicator based on stator current under early stage SISC in synchronous condensers

In this study, the stator current characteristics of synchronous condensers for stator interturn short-circuit (SISC) fault in the early stage are investigated using theoretical derivation, finite-element analysis (FEA) and experimental research. In contrast to previous studies, this study considers the real operation characteristics of synchronous condensers that need to change the exciting current to meet the reactive power demand of the power grid. Thus, a new SISC fault indicator (FI) based on the stator current is proposed in this study. The new FI can identify incipient SISC and suppress the interference of the exciting current variation. A customized synchronous machine and TTS-300-2 synchronous condenser are simulated using FEA to demonstrate the effectiveness of the FI. Furthermore, experimental tests are conducted in a customized synchronous machine to prove the practical significance of the studies.

Inter-Turn Fault Detection of Induction Motors Using End-Shield Leakage Fluxes

In this article, a non-invasive online technique to detect inter-turn stator winding fault of induction motors using end-shield leakage fluxes is proposed. The fault indicator is based on the fundamental component of the summation of the leakage fluxes which are spatially separated by an angle of 360°/number of the poles. Theoretical analysis using the analytical and finite element models revealed that the proposed fault indicator is independent of number of poles and location of the fault in any of the coils of the stator windings. Both theoretical analysis and experimental validation on a 10 hp induction motor showed that the proposed anomaly indicator is directly proportional to the fault severity level. A comparative study with various non-invasive, terminal measurements-based online techniques reveals that the proposed methodology is the most sensitive technique with an additional advantage of more than 92% reduction in the sensor cost for a 4-pole, 10 hp induction motor. Real-time evaluation of the proposed anomaly indicator with the prototype Motor Winding Monitoring Device (MWMD) shows that even under intermittent conditions, as small as 1-turn fault (0.5%) can be detected within 150 ms without any false alarms.

PMSM Demagnetization Fault Diagnosis Based on Back-EMF Signal

To study the irreversible demagnetization fault of the permanent magnets (PMs) in the permanent magnet synchronous motor (PMSM), a two-dimensional Finite Element (FE) model of the PMSM is built by the Finite Element Method (FEM). Analysis of demagnetization faulty mechanism of the electric and magnetic signals under local demagnetization conditions is conducted. The no-load Back-EMF signal is adopted as the indicator of the demagnetization faults of PMSM. In this paper, the faults of uniform demagnetization and single magnet demagnetization are studied through FEA. Therefore, the relationship between the amplitude of the back EMF and the degree of demagnetization is obtained. Moreover, the fitting curve is obtained by using the least squares method from multiple sets of FEA results. The results show that the method can effectively diagnose the demagnetization fault of the PMs of the PMSM, and could provide the guideline for online health monitoring of the PMSM in future research works.

Real-Time Defect Detection of Die Cast Rotor in Induction Motor Based on Circular Flux Sensing Coils

The die cast rotor bars in production squirrel cage induction motors (SCIMs) are easily subjected to porosity or other defects in production, which considerably affects the motors’ reliability and efficiency in operation. Planar flux sensing coils have been investigated for the defect detection of SCIM rotor. However, these types of sensors cannot accurately evaluate the severity of porosity or broken bar. This article develops a novel instrument to inspect and quantitatively analyze the rotor quality of SCIM. The sensor consists of the electromagnetic flux sensing coils directly from an SCIM stator. By injecting a dc voltage at phases A and B of the sensor, the induced voltage signal is generated from phase C. A quantitative fault indicator (QFI) is constructed on the basis of the instrument voltage output. The variation trend of the QFI with respect to fault severity is investigated by establishing a theoretical sensor model. Experimental results indicate that the proposed method can accurately detect the porosity and broken bar, and evaluate their severities for the die cast rotor. The developed solution can be easily implemented with low cost and computational complexity, which can achieve real-time inspection of SCIM rotor in theline.

Contrast Estimation in Vibroacoustic Signals for Diagnosing Early Faults of Short-Circuited Turns in Transformers under Different Load Conditions

The transformer is one of the most important electrical machines in electrical systems. Its proper operation is fundamental for the distribution and transmission of electrical energy. During its service life, it is under continuous electrical and mechanical stresses that can produce diverse types of damage. Among them, short-circuited turns (SCTs) in the windings are one of the main causes of the transformer fault; therefore, their detection in an early stage can help to increase the transformer life and reduce the maintenance costs. In this regard, this paper proposes a signal processing-based methodology to detect early SCTs (i.e., damage of low severity) through the analysis of vibroacoustic signals in steady state under different load conditions, i.e., no load, linear load, nonlinear load, and both linear and nonlinear loads, where the transformer is adapted to emulate different conditions, i.e., healthy (0 SCTs) and with damage of low severity (1 and 2 SCTs). In the signal processing stage, the contrast index is analyzed as a fault indicator, where the Unser and Tamura definitions are tested. For the automatic classification of the obtained indices, an artificial neural network is used. It showed better results than the ones provided by a support vector machine. Results demonstrate that the contrast estimation is suitable as a fault indicator for all the load conditions since 89.78% of accuracy is obtained if the Unser definition is used.

Acoustic feature enhancement in rolling bearing fault diagnosis using sparsity-oriented multipoint optimal minimum entropy deconvolution adjusted method

Rolling element bearings are of great importance and widely used in rotating machineries, whose fault detection and diagnosis (FDD) are essential for insuring reliability of the entire mechanical system. Therefore, extracting fault-related transient features from noisy signals to reveal bearing weak fault is a crucial prerequisite for long term condition monitoring. However, in complex working conditions, the measured acoustic signals are typically multi-component, submerged by strong interference noise and affected by unknown transmission path, resulting in the indistinctive fault-induced features and unsatisfactory diagnosis accuracy. To tackle this problem, a sparsity-oriented Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA) method is proposed for bearing fault feature enhancement and diagnosis based on acoustic signals. As a non-iterative blind deconvolution approach, MOMEDA has been proven to be an effective tool to extract fault-related impulses from the noisy signal and compensate the complex unknown transmission path, enabling target propositioning and fault indication of bearings at an earlier termination condition. Furthermore, a sparsity operation which is originally designed for acoustic signal analysis, based upon the Laplace distribution characteristics of the fault-induced outliers, can further suppress the noise component and enhance the periodic fault impulses. The feasibility and effectiveness of the proposed sparsity-oriented MOMEDA method is validated by both simulated and experimental data. It is worth mentioning that bearing cage fault diagnosis and bearing mixed fault diagnosis, usually receiving limited attention before, are also investigated by the proposed method. The results demonstrate that the presented method gets preferable performance than existing methods, and it can achieve robust FDD of rolling bearings based on acoustic signals.

Fault Detection and Classification of a Photovoltaic Generator Using the BES Optimization Algorithm Associated with SVM

In this work, an innovative approach based on the estimation of the photovoltaic generator (GPV) parameters from the Bald Eagle Search (BES) optimization algorithm, associated with a support vector machine (SVM) classification algorithm, allowed to highlight a new tool for the classification of the signatures of shading and moisture PV defects. It recognizes signatures generated by the GPV in healthy and erroneous operation using the optimized parametric vector and classifies defects using the same optimized vector. The technique emphasizes the resilience of parameter estimate in terms of error on all parameters. The classification accuracy is 93%. The residuals between the estimated curve in healthy operation with a minimum error of the order of 10-4 and the one at fault are used as an indicator of faults.

Fault Assessment in Piezoelectric-Based Smart Strand Using 1D Convolutional Neural Network

The smart strand technique has been recently developed as a cost-effective prestress load monitoring solution for post-tensioned engineering systems. Nonetheless, during its lifetime under various operational and environmental conditions, the sensing element of the smart strand has the potential to fail, threatening its functionality and resulting in inaccurate prestress load estimation. This study analyzes the effect of potential failures in the smart strand on impedance characteristics and develops a 1D convolutional neural network (1D CNN) for automated fault diagnosis. Instead of using a realistic experimental structure for which transducer faults can be hard to control accurately, we adopt a well-established finite element model to conduct all experiments. The results show that the impedance characteristics of a damaged smart strand are relatively different from other piezoelectric active sensing devices. While the slope of the susceptance response is widely accepted as a promising fault indicator, this study shows that the resistance response is more favorable for the smart strand. The developed network can accurately diagnose the potential faults in a damaged smart strand with the highest testing accuracy of 94.1%. Since the network can autonomously learn damage-sensitive features without pre-processing, it shows great potential for embedding in impedance-based damage identification systems for real-time structural health monitoring.

Faults Feature Extraction Using Discrete Wavelet Transform and Artificial Neural Network for Induction Motor Availability Monitoring—Internet of Things Enabled Environment

Motivation: This paper presents the high contact resistance (HCR) and rotor bar faults by an extraction method for an induction motor using Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN). The root mean square (RMS) and mean features are obtained using DWT, and ANN is used for classification using activation functions. Activation provides output by assigning the specific input with respect to the transfer function according to the nature and type of the activation function. Method: The faulty conditions are induced using MATLAB by adopting the motor current signature analysis (MCSA) method to achieve current signature signals of the healthy and faulty motors. Results: The DWT technique has been applied to obtain fault-specific features of the average continuously varying signal (RMS) and an average of the data points (mean) at levels 5, 7, 8, and 9, followed by ANN to classify the faults for condition monitoring. Utility: The utility of the results is to reduce unscheduled downtime in the industry, thus saving revenue and reducing production losses. This work will help provide support to ensure early indication of faults in induction motors under operating conditions, enabling in-service engineers to take timely preventive measures as part of the availability of resources in IoT-enabled systems. Application: Resource availability and cybersecurity are becoming vital in an environment that supports the Internet of Things (IoT) as the essential components of Industry 4.0 scenarios. The novelty of this research lies in the implementation of high contact resistance and rotor bar faults using DWT and ANN with different activation functions to achieve accuracy up to 98%.

A novel online method for locating fault coil in direct‐drive permanent magnet synchronous motor with inter‐turn short‐circuit fault using search coil array

This study presents a fault coil localisation approach for a direct-drive permanent magnet synchronous motor (DDPMSM) with inter-turn short-circuit fault (ISF) using the search coil (SC) array. The SCs are wound around specific stator tooth of DDPMSM through the proposed SC array arrangement principle, which reduces the invasiveness of the fault coil localisation method. The SCs are classified as several search coil groups (SCGs), the differential between SC back electromotive forces (EMFs) in the same SCGs (δ1) and SC residual back EMF (δ2) are used as fault indicators for ISF localisation. The peak values extracted from δ1 and δ2 are the basis for diagnosing ISF. Hence, the proposed approach does not need a complicated signal processing algorithm, and it is less complex. First, the SC back EMF analytical model is developed to investigate the mapping relationship between the back EMF of each SC and fault coil location. Second, fault coil localisation indicators for ISF are analysed based on the proposed analytical model, and the exact ISF localisation approach is presented. Finally, two basic installation units and the arrangement principle of SC array in DDPMSM are proposed. Simulation and experimental results justify the effectiveness of the selected fault indicators and the feasibility of the proposed SC array for diagnosing ISF.

Using discrete Bayesian networks for diagnosing and isolating cross-level faults in HVAC systems

Fault detection and diagnosis (FDD) technologies are critical to ensure satisfactory building performance, such as reducing energy wastes and negative impacts on occupant comfort and productivity. Existing FDD technologies mainly focus on component-level FDD solutions, which could lead to mis-diagnosis of cross-level faults in heating, ventilating, and air-conditioning (HVAC) systems. Cross-level faults are those faults that occur in one component or subsystem, but cause operational abnormalities in other components or subsystems, and result in a building level performance degradation. How to effectively diagnose the root cause of a cross-level fault is the focus of this study. This paper presents a novel discrete Bayesian Network (DisBN)-based method for diagnosing cross-level faults in an HVAC system commonly used in commercial buildings. A two-level DisBN structure model is developed in this study. The parameters used in the DisBN model are obtained either from expert knowledge or through machine-learning strategies from normal system operation data. Meanwhile, the probability parameters are discretized to incorporate the uncertainties associated with typical expert knowledge. Thus, the developed DisBN method addresses the challenges many other BN based FDD methods face, i.e., the lack of fault data for BN parameter training. The developed DisBN represents causal relationships between a fault and its cross-level system impacts (i.e., fault symptoms or fault indicators) by considering how fault impacts propagate across different levels in an HVAC system. A weather and schedule information-based Pattern Matching (WPM) method is employed to automatically create WPM baseline data sets for each incoming real time snapshot data from the building systems. Consequently, BN inference and real-time diagnostics are achieved by comparing incoming snapshot data and the WPM baseline data set. The proposed method is evaluated using experimental fault data collected in a campus building. Fault diagnosis results demonstrate that the WPM-DisBN method is effective at locating the root causes of cross-level faults in an HVAC system.

Backstepping Control of a Switched Reluctance Motor with Inter-Turn Short-Circuit

Improving the behavior of speed control in electric vehicles is currently a major challenge for researchers and engineers. For this study, we chose the context of a Switched Reluctance Motor (6/4 SRM) used in a hybrid Electric Vehicle with Extended Range (EREV). Speed regulation is an essential feature on long-distance trip. Speed regulators of backstepping type are very effective in this context given the non-linear nature of switched reluctance motors. The estimation of non-linear quantities, flux and inductance, uses Legendre polynomials. The control strategy uses four regulators, one for speed and three for stator currents. It is based on the Torque Sharing Function (TSF) and the Torque Inverse Model (TIM). Our simulation consists of studying the behavior of this type of control when an Inter-Turn Short-Circuit (ITSC) fault appears on one of the phases of the 6/4 SRM. In this paper, we are interested in temporal behavior of phasic currents and we will show the interest of these quantities as fault indicators allowing the real time diagnosis of this type of controller-machine.