Fault Indicators Research Articles

Optimal Placement of Fault Indicators to Identify Fault Zones in Distribution Systems

The optimal placement of fault indicatorss (FIs) is one of the most viable alternatives to improve the reliability of customer service of the distribution utilities. This letter approaches the concept of fault zone as being the portion of the system characterized by the actuation of FI devices. The proposed method consists of FI placement in locations of the system that provide smaller fault zones. Moreover, these devices must be close to customers with high power demand to decrease the service interruption costs. The proposed method is tested on the IEEE 34-node test feeder, in which the results demonstrated that it is efficient to reach the stated objectives.

Time-Frequency Spectral Power Assessment of Rolling Element Bearing Faults Using Adaptive Modified Morlet Wavelet Transform

Objectives: The main objectives of this manuscript are to investigate and diagnose rolling element bearing defects in its inception time. Methods: Vibration signal generated by the induction motor contains a series of frequency components that have rich and viable information about bearing health conditions. Recently, the Maximum Energy Concentration (MEC), measure of time-frequency spectrum has been employed to investigate the small variations in low frequency biomedical signal spectrum. In this paper, the above technique has been modified and applied to study the bearing defects of induction motor using vibration signal and it is termed as Adaptive Modified Morlet Wavelet (AMMW) transform. Initially, this proposed method was validated on two medium frequency synthetic time series signals in terms of MEC measurement at different Signal to Noise Ratio (SNR). Results: The simulated results have depicted that the AMMW method provides excellent timefrequency localization capability over other time-frequency methods like Morlet wavelet transform, modified Morlet wavelet transform, adaptive S-transform and adaptive modified S-transform. Then, this method has been applied to the standard database of vibration signal to determine interquartile power for fault detection purpose and also fault index parameter termed as IFI has been analyzed to detect small variation in vibration signals.

Scale Dependence of Earthquake Rupture Prestress in Models With Enhanced Weakening: Implications for Event Statistics and Inferences of Fault Stress

AbstractDetermining conditions for earthquake slip on faults is a key goal of fault mechanics highly relevant to seismic hazard. Previous studies have demonstrated that enhanced dynamic weakening (EDW) can lead to dynamic rupture of faults with much lower shear stress than required for rupture nucleation. We study the stress conditions before earthquake ruptures of different sizes that spontaneously evolve in numerical simulations of earthquake sequences on rate‐and‐state faults with EDW due to thermal pressurization of pore fluids. We find that average shear stress right before dynamic rupture (aka shear prestress) systematically varies with the rupture size. The smallest ruptures have prestress comparable to the local shear stress required for nucleation. Larger ruptures weaken the fault more, propagate over increasingly under‐stressed areas due to dynamic stress concentration, and result in progressively lower average prestress over the entire rupture. The effect is more significant in fault models with more efficient EDW. We find that, as a result, fault models with more efficient weakening produce fewer small events and result in systematically lower b‐values of the frequency‐magnitude event distributions. The findings (a) illustrate that large earthquakes can occur on faults that appear not to be critically stressed compared to stresses required for slip nucleation; (b) highlight the importance of finite‐fault modeling in relating the local friction behavior determined in the lab to the field scale; and (c) suggest that paucity of small events or seismic quiescence may be the observational indication of mature faults that operate under low shear stress due to EDW.

FAULT DETECTION AND ISOLATION BASED ON HYBRID SLIDING MODE OBSERVER and FUZZY LOGIC

Faults detection and isolation scheme on the bases of a hybrid Sliding Mode Observer (SMO) technique and fuzzy logic technique has been presented in this paper. The SMO technique was used for the fault detection purpose based on residual signal which represent the difference between output of the process and output of the model that used as a fault indicator, while the fuzzy logic technique was used as a fault isolator depending on fuzzy rules base and fuzzy membership functions. The performance of this hybrid technique is evaluated on a model of DC motor, the proposed scheme is illustrated on a Matlab/Simulink simulator and the results demonstrated a successful implementation of the proposed Fault Detection and Isolation FDI scheme.

A novel tacholess order analysis method for bearings operating under time-varying speed conditions

Order tracking (OT) of vibration envelope signal based on instantaneous frequency (IF) estimated from a time-frequency representation (TFR) is generally applied to detect bearing defects under time-varying speed conditions. However, these TFR based OT approaches require a high resolution for accurate estimation of the IF. To overcome these issues, a tacholess OT technique based on the fast dynamic time warping (FDTW) algorithm is applied to detect bearing defects for the first time in this paper. FDTW based signal transformation is initially applied to align a filtered shaft signal to a reference signal of constant frequency, followed by reconstructing the bearing envelope signal. The resulting envelope signal is able to provide a richer fault indication than the original signal. The efficacy of the developed OT method is established using a simulation analysis followed by experimental validation using measurements obtained from a laboratory test-rig and a wind turbine bearing.

Vibration modelling and fault evolution symptom analysis of a planetary gear train for sun gear wear status assessment

The diagnosis and monitoring of surface wear are very critical for the prevention of shutdown and even catastrophic destruction of a geared transmission system. However, the detection for the evolutionary status of surface wear in a planetary gear train is quite challengeable due to the complicated and weak characteristics buried in the vibration signals. To assess the wear status of gears in a planetary gear train, a sophisticated vibration signal analysis based methodology is proposed. First, an analytical dynamic model of single-stage planetary gear train is established, which incorporates the effects of distributed surface wear of mating gears. With the analytical dynamic model, a resultant vibration signal model is further established after considering the influences of transfer path, direction variation of gear action lines and background noise. This vibration signal model is used to represent the actual vibration signals collected by transducers installed on the housing of planetary gearbox. Based on the constructed resultant vibration signals, two novel fault indicators are defined to reveal the evolution law of fault symptom. The fault evolution symptom analysis is used to identify healthy status and wear status of mating gears in the planetary transmission system. The effectiveness of the proposed model as well as the fault indicators are validated by both simulated signals and experimental signals.

Analysis and detection of turn‐to‐turn short circuit fault in a permanent magnet Vernier generator based on modified winding function

Operation of Vernier machines is based on flux modulation. They have some advantages including maintaining the rated torque in motor applications and the rated output power in generator applications at low speeds, high torque density and relatively low torque ripple due to their magnetic gear property and fault-tolerance. In recent years, the motor with various topologies has been developed for different applications. Therefore, analysing and monitoring different faults is essential in the design and implementation stages. This study has two main purposes. First, to introduce an accurate frequency index independent of load variations for detecting the turn-to-turn short circuit (TTSC) faults in the stator windings of a permanent magnet Vernier machine. Second, modelling the machine in a healthy condition and in the presence of TTSC faults to investigate its effect on machine performance and output characteristics. An analytical model is presented using the modified winding function theory under the TTSC fault by taking into account the effect of stator slots which enhances the accuracy of the air-gap permeance function estimation. In the process of calculating the fault index, frequency components of the output characteristics of the machine are obtained from the mathematical equations of the magnetic field. It is shown that the proposed indicators are robust against load variations.

An adaptive order-band energy ratio method for the fault diagnosis of planetary gearboxes

Condition monitoring of planetary gearboxes in time-varying speed service is an important and actual engineering requirement, however, also an intractable task. The traditional sideband energy ratio (SER) method limits its application to complex operational conditions due to the frequency spectrum based and manual bandwidth selection. To this end, this paper proposes an adaptive order-band energy ratio (AOER) method, an enhanced version of the SER, to quantitatively and intelligently diagnose gear faults under different operational conditions. First, a signal model based on angular increment and order components is proposed to properly describe the vibrations for both stationary and non-stationary conditions. The proposed model provides the theoretical source and reliance for the order spectrum analysis and the diagnostic mechanism of the OER. Second, the OER is theoretically proved to be a reliable fault indicator by analyzing the proposed signal model. The offset of the uncertainty terms highlighting the fault symptoms demonstrates the potential success of using OER for the fault diagnosis. Finally, OERs generated from the vibration data and three typical machine learning algorithms are used to adaptively obtain the optimal bandwidth and thus the AOER. The AOER can be directly used for the remaining vibration data of the corresponding operational condition. Comparing with SER and the convolutional neural network, the experimental results demonstrate the effectiveness and outperformance of AOER for both stationary and non-stationary operational conditions. More importantly, since AOER can cope with diagnostic tasks for different operational conditions, it enables an improved ability to monitor a PG’s health in the actual complex environment.

Active Faulting in Lake Constance (Austria, Germany, Switzerland) Unraveled by Multi-Vintage Reflection Seismic Data

Probabilistic seismic hazard assessments are primarily based on instrumentally recorded and historically documented earthquakes. For the northern part of the European Alpine Arc, slow crustal deformation results in low earthquake recurrence rates and brings up the necessity to extend our perspective beyond the existing earthquake catalog. The overdeepened basin of Lake Constance (Austria, Germany, and Switzerland), located within the North-Alpine Molasse Basin, is investigated as an ideal (neo-) tectonic archive. The lake is surrounded by major tectonic structures and constrained via the North Alpine Front in the South, the Jura fold-and-thrust belt in the West, and the Hegau-Lake Constance Graben System in the North. Several fault zones reach Lake Constance such as the St. Gallen Fault Zone, a reactivated basement-rooted normal fault, active during several phases from the Permo-Carboniferous to the Mesozoic. To extend the catalog of potentially active fault zones, we compiled an extensive 445 km of multi-channel reflection seismic data in 2017, complementing a moderate-size GI-airgun survey from 2016. The two datasets reveal the complete overdeepened Quaternary trough and its sedimentary infill and the upper part of the Miocene Molasse bedrock. They additionally complement existing seismic vintages that investigated the mass-transport deposit chronology and Mesozoic fault structures. The compilation of 2D seismic data allowed investigating the seismic stratigraphy of the Quaternary infill and its underlying bedrock of Lake Constance, shaped by multiple glaciations. The 2D seismic sections revealed 154 fault indications in the Obersee Basin and 39 fault indications in the Untersee Basin. Their interpretative linkage results in 23 and five major fault planes, respectively. One of the major fault planes, traceable to Cenozoic bedrock, is associated with a prominent offset of the lake bottom on the multibeam bathymetric map. Across this area, high-resolution single channel data was acquired and a transect of five short cores was retrieved displaying significant sediment thickness changes across the seismically mapped fault trace with a surface-rupture related turbidite, all indicating repeated activity of a likely seismogenic strike-slip fault with a normal faulting component. We interpret this fault as northward continuation of the St. Gallen Fault Zone, previously described onshore on 3D seismic data.

Two Current-Based Methods for the Detection of Bearing and Impeller Faults in Variable Speed Pumps

The growing number of variable speed drives (VSDs) in industry has an impact on the future development of condition monitoring methods. In research, more and more attention is being paid to condition monitoring based on motor current evaluation. However, there are currently only a few contributions to current-based pump diagnosis. In this paper, two current-based methods for the detection of bearing defects, impeller clogging, and cracked impellers are presented. The first approach, load point-dependent fault indicator analysis (LoPoFIA), is an approach that was derived from motor current signature analysis (MCSA). Compared to MCSA, the novelty of LoPoFIA is that only amplitudes at typical fault frequencies in the current spectrum are considered as a function of the hydraulic load point. The second approach is advanced transient current signature analysis (ATCSA), which represents a time-frequency analysis of a current signal during start-up. According to the literature, ATCSA is mainly used for motor diagnosis. As a test item, a VSD-driven circulation pump was measured in a pump test bench. Compared to MCSA, both LoPoFIA and ATCSA showed improvements in terms of minimizing false alarms. However, LoPoFIA simplifies the separation of bearing defects and impeller defects, as impeller defects especially influence higher flow ranges. Compared to LoPoFIA, ATCSA represents a more efficient method in terms of minimizing measurement effort. In summary, both LoPoFIA and ATCSA provide important insights into the behavior of faulty pumps and can be advantageous compared to MCSA in terms of false alarms and fault separation.

A protection scheme for distribution utility grid with wind energy penetration

The impact of fault circumstances on distribution grid parameters in the presence of wind power generation is explored in this research. A protection algorithm (PA) is also proposed for detecting faulty events by using the proposed wind fault index, which is computed by analysing current signals using the Wigner distribution function and the Stockwell transform (ST). The sort of fault is determined by the number of faulty phases found. The zero sequence currents introduced to identify the ground’s involvement in a fault event are analysed to calculate a wind ground fault index. Single phase to the ground, double phase to the ground, double phase, three-phases, and three phases to the ground faults are all investigated fault events. It has been determined that PA outperforms discrete Wavelet Transform and ST-based approaches in terms of fault estimation time and noise effect. The study was conducted on the IEEE-13 bus test feeder, which was connected to wind power plants (WPPs).

Signal-based diagnostic approach to enhance fuel cell durability

To face the dependency to fossil fuels and limit the carbon emissions, fuel cells are a very promising technology and appear to be a key candidate to face the increase of the energy demand and promote the energy transition and energy mix. To meet future needs for both automotive and stationary applications, fuel cells will require improved durability. In this paper, a new concept to enhance fuel cell performance and lifetime is provided, by introducing a disruptive and high-efficiency diagnostic approach based on signal processing. The results show that the failures on the fuel cell system can be detected and isolated efficiently thanks to fault indicators. The innovative concept proposed here, can guarantee improvements of the durability and of the reliability of the fuel cell system by detecting abnormal operating conditions rapidly and efficiently.

Research on Fault Waveform Recording Testing of Single Phase to Earth for Distribution Line Fault Indicator

To evaluate the recognitional capacity of single-phase grounding fault based on transient recording method in distribution system, the corresponding test method about fault waveform recording is presented for distribution line fault indication. The principle and implementation process of transient recording method is introduced. Ten 10 key indexes are selected from typical characteristic signals of earth fault, and a similarity comparison method of fault waveforms is designed. The proposed method is verified by laboratory tests, which shows its feasibility and effectiveness for test of the recognitional capacity of single phase to earth fault.

Acceptance Ratio Analysis: An Early Fault Indicator for Grid-Connected Photovoltaic System

This paper presents the Acceptance Ratio (AR) analysis for three different grid-connected photovoltaic (GCPV) systems working under the Malaysia tropical climate. AR is a ratio between actual AC power, PAC_actual, and predicted AC power, PAC_predicted. According to Malaysian Standard MS2692:2020,the AR value must ≥ 0.9 to classify as accepted in testing and commissioning test. In contrast, a rate < 0.9 indicates a non-accepted GCPV system. Historical data of the AC power output, solar irradiance, and module temperature from January 1 to December 31, 2019, were used for the analysis. The analysis procedure was carried out using Matlab and Microsoft Excel software. The analysis covers the AC power analysis and the AR analysis based on the threshold of 0.9. The plotted monthly AC power graph shows that all systems have lower than 15 % differences between actual and predicted AC power. On the AR analysis, System 1 was found to show early fault indicator with a monthly cumulative percentage of AR < 0.9 ranges from 34 % to 71 %, meanwhile System 2 and System 3 have a lower cumulative percentage of AR < 0.9 ranges from 5 % to 19 %. This result suggested that only System 2 and 3 are fault-free GCPV systems and working in good condition. The outcome of this study has succeeded in providing preliminary AR analysis results for three GCPV systems located in Malaysia. This study would help to evaluate AR threshold reliability to indicate an early fault of a GCPV system.

Advances in Fault Diagnostics and Post‐Fault Operation of Electrical Drives

Advances in Fault Diagnostics and Post‐Fault Operation of Electrical Drives

Neutralization of temperature effects in damage diagnosis of MDOF systems by combinations of autoencoders and particle filters

In the last years, scientific and industrial communities have put a lot of efforts into the development of a new framework for the assessment of structural integrity, generally known as Structural Health Monitoring (SHM), which should allow real-time, automatic evaluations of the state of the structures based on a network of permanently installed sensors. In the context of mechanical, aerospace and civil structures, several approaches have been proposed to address the SHM problem, yet, it remains often difficult to diagnose damages and estimate the structural health when dealing with varying operating and environmental conditions. Particle Filters have already been proposed as a time-domain-based method in the field of SHM, showing promising results as estimators of hidden, not directly observable states, such as those typically related to damages. At the same time, neural networks-based autoencoders have been proposed for structural damage detection, demonstrating to be capable of capturing damage-related features from vibration measurements. This work aims at exploiting the individual advantages offered by the two approaches by combining them in a novel algorithm for structural damage detection and localization, robust with respect to changing environmental conditions. The algorithm is further equipped with a fault indicator module stemming from the introduction of an automatic threshold and both deterministic and probabilistic fault indicators, thus offering a complete, valuable tool for supporting decision making with limited human intervention. The method is demonstrated with reference to a numerical MDOF system whose parameters are taken from a literature benchmark case study.

Negative Sequence Current Compensation for Induction Motor Stator Inter-Turn Short Circuit and Off-Diagonal Term in Sequence Impedance Matrix as Fault Indicator

This work proposes an artificial neural network supported method to establish an automatic detection of stator turn fault in induction motor. The sequence current analysis is done for turn fault condition. Various factors influencing the total measured negative sequence current such as unbalanced voltage and inherent asymmetry have been reviewed. To compensate the voltage unbalance and non-idealities in the machine, utilization of measured negative sequence current, impedance, admittance, or semi empirical formula is developed. The output of a well-trained feed forward back propagation neural network classifies the severity of fault level in stator winding. The method of considering the effects of turn faults on inter-turn fault detection improves sensitivity meanwhile reduces the prospect of misdetection.

MILP-Based Fault Diagnosis Model in Active Power Distribution Networks

With a rapid growth in the deployment of renewable and distributed energy resources (DERs) in active power distribution networks, especially the inverter-interfaced distributed generators (IIDGs), existing fault section estimation methods are facing additional challenges. This paper proposes an optimization model for fault diagnoses in active power distribution systems, which aims at simultaneous identifications of suspected fault line sections and potential false alarms sent by remote fault indicators (RFIs). First, mathematical expressions of expected and false alarms of oriented RFIs (ORFIs) and non-oriented RFIs (NRFIs) are formulated to construct a basic fault diagnosis model. Minimizing the number of false alarms is chosen as the objective function to find the most credible one from all possible fault hypotheses which represent different combinations of elements in the outage area. Next, considering IIDGs, a modified fault diagnosis model is established by eliminating misleading components from the objective function which are produced by incompetent RFIs and have failed to detect weak currents. The monitoring data provided by distribution level phasor measurement units (D-PMUs) for weak IIDG fault currents are leveraged to construct the equivalent RFI current, which is compared with the trigger threshold to find incompetent sensors. The proposed problem is formulated as a mixed-integer linear programming (MILP) model, which is solved by off-the-shelf commercial solvers. The effectiveness of the proposed fault diagnosis model is validated by test cases representing several DERs and false alarms applied to a utility feeder.

Fault Indexing Parameter Based Fault Detection in Induction Motor via MCSA with Wiener Filtering

Fault detection in an induction motor, particularly at premature stage has become necessary to avoid unexpected damage in industrial process. In this paper, an approach to detect the early stage faults in induction machine using motor current signature analysis (MCSA) is presented. It is proposed to estimate the fault severity from stator current using noise cancelation by an adaptive filter (Wiener filter). Wavelet De-noising technique is implemented to reduce the effect of noise floor in noise canceled stator current. Different categories of bearing faults, broken rotor fault and stator inter turn faults in induction motor are estimated with and without de-nosing using pre-fault component cancelation (Noise cancelation). In addition, fault index based on standard deviation (SD) and simple square integral (SSI) value of noise canceled stator current are proposed. The proposed fault detection topology is examined using simulations and experiments on a 3HP, 1HP and 0.5HP induction motors for bearing, broken rotor and stator inter turn faults respectively.

Interpretable Detection of Partial Discharge in Power Lines with Deep Learning.

Partial discharge (PD) is a common indication of faults in power systems, such as generators and cables. These PDs can eventually result in costly repairs and substantial power outages. PD detection traditionally relies on hand-crafted features and domain expertise to identify very specific pulses in the electrical current, and the performance declines in the presence of noise or of superposed pulses. In this paper, we propose a novel end-to-end framework based on convolutional neural networks. The framework has two contributions: First, it does not require any feature extraction and enables robust PD detection. Second, we devise the pulse activation map. It provides interpretability of the results for the domain experts with the identification of the pulses that led to the detection of the PDs. The performance is evaluated on a public dataset for the detection of damaged power lines. An ablation study demonstrates the benefits of each part of the proposed framework.