Showing papers on "Fault indicator published in 2020"

Ensemble extreme learning machines for compound-fault diagnosis of rotating machinery

Abstract: The data-driven fault indicator for rotating machinery is designed to reveal the possible fault scenarios from the observed statistical vibration signals. This study develops a novel ensemble extreme learning machine (EELM) network to replace the conventional layout by combining binary classifiers (e.g., binary relevance) for compound-fault diagnosis of rotating machinery. The proposed EELMs consist of two sub-networks, namely, the first extreme learning machine (ELM) for clustering, and the second for multi-label classification. The first network generates the Euclidean distance representations from each point to every centroid with unsupervised clustering, and the second identifies potential output tags through multiple-output-node multi-label learning. Compared to the existing multi-label classifiers (e.g., multi-label radial basis function, rank support vector machine, back-propagation multi-label learning, and binary classifiers with binary relevance), the theoretical verification reveals EELMs perform the best in hamming loss, one-error, training time, and achieves the best overall evaluation for the two real-world databases (e.g., Yeast and Image). Regarding the real test for the compound-fault diagnosis of rotating machinery, this paper applies the particle swarm optimization-based variational mode decomposition to decompose the raw vibration signals into a series of intrinsic modes, and selects ten time-domain indicators and five frequency-domain statistical characteristics for feature extraction. The experimental results illustrate that the EELM-based fault diagnosis method achieves the best overall performance.

Classification and Detection of Wind Turbine Pitch Faults Through SCADA Data Analysis

Abstract: The development of electrical control system faults can lead to increased mechanical component degradation, severe reduction of asset performance, and a direct increase in annual maintenance costs. This paper presents a highly accurate data driven classification system for the diagnosis of electrical control system faults, in particular, wind turbine pitch faults. Early diagnosis of these faults can enable operators to move from traditional corrective or time based maintenance policy towards a predictive maintenance strategy, whilst simultaneously mitigating risks and requiring no further capital expenditure. Our approach provides transparent, human-readable rules for maintenance operators which have been validated by an independent domain expert. Data from 8 wind turbines was collected every 10 minutes over a period of 28 months with 10 attributes utilised to diagnose pitch faults. Three fault classes are identified: “no pitch fault”, “potential pitch fault” and “pitch fault established”. Of the turbines, 4 are used to train the system with a further 4 for validation. Repeated random sub-sampling of the majority fault class was used to reduce computational overheads whilst retaining information content and balancing the training and validation sets. A classification accuracy of 85.50% was achieved with 14 human readable rules generated via the RIPPER inductive rule learner. Of these rules, 11 were described as “useful and intuitive” by an independent domain-expert. An expert system was developed utilising the model along with domain knowledge, resulting in a pitch fault diagnostic accuracy of 87.05% along with a 42.12% reduction in pitch fault alarms.

Detection of Winding Asymmetries in Wound-Rotor Induction Motors via Transient Analysis of the External Magnetic Field

Abstract: Over recent decades, the detection of faults in induction motors (IMs) has been mainly focused in cage motors due to their extensive use. However, in recent years, wound-rotor motors have received special attention because of their broad use as generators in wind turbine units, as well as in some large power applications in industrial plants. Some classical approaches perform the detection of certain faults based on the fast Fourier transform analysis of the steady state current (motor current signature analysis); they have been lately complemented with new transient time–frequency-based techniques to avoid false alarms. Nonetheless, there is still a need to improve the already existing methods to overcome some of their remaining drawbacks and increase the reliability of the diagnostic. In this regard, emergent technologies are being explored, such as the analysis of stray flux at the vicinity of the motor, which has been proven to be a promising option to diagnose the motor condition. Recently, this technique has been applied to detect broken rotor bar failures and misalignments in cage motors, offering the advantage of being a noninvasive tool with simple implementation and even avoiding some drawbacks of well-established tools. However, the application of these techniques to wound rotor IMs (WRIMs) has not been studied. This article explores the analysis of the external magnetic field under the starting to detect rotor winding asymmetry defects in WRIMs by using advanced signal processing techniques. Moreover, a new fault indicator based on this quantity is introduced, comparing different levels of fault and demonstrating the potential of this technique to quantify and monitor rotor winding asymmetries in WRIMs.

Fault Diagnosis and Monitoring of Modular Multilevel Converter With Fast Response of Voltage Sensors

Abstract: Modular multilevel converter (MMC) has been one of the most popular candidates in high-voltage applications. However, reliability is a critical issue due to a large number of power switching devices and capacitors applied in the MMC. To improve the reliability of the MMC, a fast and reliable diagnosis strategy for the open-circuit fault is proposed in this paper, where submodule voltage sensors are relocated to the upper switching device. Furthermore, a voltage observer based on the submodule voltage sensor is established to monitor the capacitor and realize the power control of the MMC under normal operation. A Boolean logic operation based fault indicator is put forward based on the relationship of the operation state and binaried output of voltage sensors, which could detect and locate the open-circuit faults of the MMC very fast. The ratio of the increment of the observed and measured capacitor voltage during the period of positive arm current is applied to monitor the capacitor. The effectiveness of the proposed fault diagnosis strategy and the capacitor monitoring strategy is verified by the experiment results.

Optimal Placement of Fault Indicator and Sectionalizing Switch in Distribution Networks

Abstract: Distribution network automation is considered by power supply companies as an effective investment strategy to improve reliability and service quality. Switching devices and protective devices play an important role in the distribution automation system (DAS). This paper presents a novel method to optimize placement of fault indicators and sectionalizing switches in distribution networks with branch lines. The objective function of the proposed method includes the total cost of fault indicators and sectionalizing switches as well as interruption cost. Among different automation equipment, this paper considers fault indicators and remote controlled switches. Besides, manual switches are taken into account since their number and location have a significant impact on the optimal placement problem. Mixed-integer linear programming is used to model the problem, and the proposed model can be solved by large-scale commercial solvers. The solution to the problem is composed of the optimal number and location of fault indicators and sectionalizing switches. The validity of the proposed method is demonstrated by relevant case studies and sensitivity analysis. Moreover, the proposed method is applied to a real distribution network to verify its practicability.

Model-Based Analysis and Quantification of Bearing Faults in Induction Machines

Abstract: The detection of rolling-element bearing fault can be accomplished by monitoring and interpreting a variety of signals, including the vibration, the acoustic noise, and the stator current. The existence of a bearing fault as well as its specific fault type can be readily determined by performing frequency spectral analysis on the monitored signals with various signal processing techniques. However, this traditional approach, despite being simple and intuitive, is not able to identify the severity of a bearing fault in a quantitative manner. Moreover, it is often times tedious and time-consuming to apply this approach to electric machines with different power ratings, as the bearing fault threshold values need to be manually calibrated for each motor running at every possible speed and carrying any possible load. This article, thus, proposes a quantitative approach to estimate the bearing fault severity based on the airgap displacement profile, which is reconstructed from the mutual inductance variation profile estimated from a quantitative electrical model that takes the stator current as input. In addition, the accuracy of the developed electrical model and the estimated bearing fault severity are validated by the simulation and experimental results, and the explicit airgap variation profile is reconstructed with the superposition of multiple Fourier series terms estimated from the stator current via the proposed scheme. The proposed method offers a quantitative and universal bearing fault indicator for induction machines with any power ratings and operating under any speed and load conditions.

Bearing Fault Analysis of BLDC Motor for Electric Scooter Application

Abstract: In this paper, the bearing faults analysis of the brushless DC motor is presented. The research method is based on the analysis of the vibration signal of healthy as well as faulty bearings by the identification of specific frequencies on the vibration spectrum. For the experiment, the most common faults were inflicted on the bearings. As the used motor is intended for electric scooter applications, seven different damages were chosen, which are highly likely to occur during the scooter operation. The main bearing faults and the possibility of fault monitoring are addressed. The vibration data are gathered by the acceleration sensors placed on the motor at different locations and the spectrum analysis is performed using the fast Fourier transform. The variation in the amplitude of the frequency harmonics particularly the fundamental component is presented as a fault indicator.

Axial Stray Flux Sensor Proposal for Three-Phase Induction Motor Fault Monitoring by Means of Orbital Analysis

Abstract: This paper proposes a new form of stray flux sensor, placed on the inner side of the machine rear end plate, in order to monitor the presence and evolution of some most common faults in three-phase induction motors. The fault indicator is based on analysis of orbits built from time flux signals collected by the sensor. The effectiveness of this methodology was checked considering three types of faults under three different load conditions: incipient inter-turn short circuits, unbalanced voltage supplies and misalignment, all with 0%, 50% and 100% loads. The experimental results showed a viability of the technique for diagnosing and monitoring of induction motors. This can be adapted and used in predictive maintenance in industry.

Improved Process Monitoring Strategy Using Kantorovich Distance-Independent Component Analysis: An Application to Tennessee Eastman Process

Abstract: Vowing to the increasing complexity in industrial processes, the need for safety is of highest priority and this has led to development of efficient fault detection (FD) methods. Also, with rapid development of data acquisition systems, process history based methods have gained importance as their dependency is on large volume of sensor data extracted from the process. The industrial data exhibits some degree of non-gaussianity for which Independent Component Analysis (ICA) technique has usually been applied in practice. Recently, a new fault indicator based on Kantorovich Distance (KD) has been proposed which computes distance between two distributions and uses the distance as an indicator of fault. The KD metric has found to provide good monitoring results for data in presence of noise and offers enhanced detection of small magnitude faults. Considering the benefits offered by KD metric, the objective of this work is to amalgamate KD metric with ICA modeling framework to have a fault detection strategy that can improve process monitoring in noisy environment. The proposed ICA-KD FD strategy is illustrated on four processes that includes Modified Continuous Stirred Tank Heater (CSTH), Tennessee Eastman (TE) process and Experimental Distillation Column Process. The simulation results indicate that the proposed FD strategy exhibits improved performance over conventional strategies while monitoring different sensor faults in noisy environment.

A New Nonlinear Model-Based Fault Detection Method Using Mann–Whitney Test

Abstract: A well-established theory of statistical inference enables the generation of statistical fault detection approaches. Previous works mainly focus on parametric tests that assume that probabilistic distributions of both healthy and faulty residuals can be parameterized. However, such assumptions may be quite limited for general nonlinear stochastic systems because those residuals are usually with unknown distribution. In this article, a new fault indicator is defined to replace the traditional residual, and a multistep fault detection method is developed via the standard Mann–Whitney (MW) test. Moreover, with weak assumptions on faults and systems, a one-step fault detection approach is proposed by means of the modified MW test. Finally, the effectiveness of the proposed fault detection method is verified by a simulation of three water tank system.

Fault coil location of inter-turn short-circuit for direct-drive permanent magnet synchronous motor using knowledge graph

Abstract: Inter-turn short-circuit fault (ISF) degrades its reliability and may cause serious catastrophes for direct-drive permanent magnet synchronous motor (DDPMSM). Fault location technology can reduce maintenance time, increase the mean time between failure (MTBF), and then improve the reliability of DDPMSM. Hence, an intelligent fault locating system for DDPMSM is proposed in this paper. This system proposes a knowledge graph (KG) based diagnostic tool for detection and location of the fault coil. First, the fault model of the DDPMSM with multiple branches parallel winding is established, which is used to analyze the fault characteristics of motor. Second, the BDC and BRC are proposed as the fault indicator. The effectiveness and robustness of fault indicator are analyzed. Then, the KG system are designed and established according to the relationship between fault indicator and location of fault coil. Finally, the system is tested by data under different fault and operation conditions. The test results showed that the proposed fault locating system can detect and locate the fault coil in early stage. The minimum ratio of shorted turns to branch turns that can be detected is 0.52%. The minimum ratio of shorted turns to branch turns that can be located is 6.25%.

Detection and Identification of Rotor Faults in Axial Flux Permanent Magnet Synchronous Motors Due to Manufacturing and Assembly Imperfections

Abstract: In this paper, a method is proposed to detect and identify the types of rotor faults in single stator single rotor axial flux permanent magnet synchronous motors, due to manufacturing and assembly errors. Three types of faults are considered: A shift in the rotor geometry along the axial direction, a parallel shift of the rotor geometry axis with respect to the stator axis, and inclined rotor. The proposed method is based on using the motor voltages $(V_d$ and $V_q)$ , extracted from the controller, as features for detecting the type of the faults and estimating it's severity. The change of the point $(V_d,V_q)$ in the $V_d$ – $V_q$ plane is used as a fault indicator. The shift direction is used to detect the fault's type, and the amount of the shift is used to estimate the severity. A quasi-3D computation is used to model the axial flux motor under healthy operation and the three types of faults. A three dimensional (3D) Finite Element Analysis and experimental tests are performed on a 6 slots, 8 poles axial flux motor to validate the quasi-3D computation. Finally, an algorithm is proposed to detect and discriminate between the different faults under different operating conditions.

Threshold-Based Induction Motors Single- and Multifaults Diagnosis Using Discrete Wavelet Transform and Measured Stator Current Signal

Abstract: In this article, a threshold-based induction motor fault diagnosis method is proposed using the measured stator current signal. A 0.25-HP three-phase squirrel-cage induction motor fed directly online is tested in the laboratory with various single- and multielectrical faults under six different loading conditions. The discrete wavelet transform (DWT) is chosen as the signal processing technique for the measured stator currents. The threshold and energy values at each decomposition level of the DWT processing results are evaluated. Threshold values appear to be more consistent than energy values at different measured data windows, and thus, the threshold at the decomposition level d8 is chosen as a fault indicator. Curve fitting equations are developed to calculate threshold values for the motor loadings that were not tested in experiments. The suitability using threshold values for induction motor fault diagnosis is further validated using two probabilistic methods, the correlation analysis and the confidence interval estimation.

Model Based IAS Analysis for Fault Detection and Diagnosis of IC Engine Powertrains

Abstract: Internal combustion (IC) engine based powertrains are one of the most commonly used transmission systems in various industries such as train, ship and power generation industries. The powertrains, acting as the cores of machinery, dominate the performance of the systems; however, the powertrain systems are inevitably degraded in service. Consequently, it is essential to monitor the health of the powertrains, which can secure the high efficiency and pronounced reliability of the machines. Conventional vibration based monitoring approaches often require a considerable number of transducers due to large layout of the systems, which results in a cost-intensive, difficultly-deployed and not-robust monitoring scheme. This study aims to develop an efficient and cost-effective approach for monitoring large engine powertrains. Our model based investigation showed that a single measurement at the position of coupling is optimal for monitoring deployment. By using the instantaneous angular speed (IAS) obtained at the coupling, a novel fault indicator and polar representation showed the effective and efficient fault diagnosis for the misfire faults in different cylinders under wide working conditions of engines; we also verified that by experimental studies. Based on the simulation and experimental investigation, it can be seen that single IAS channel is effective and efficient at monitoring the misfire faults in large powertrain systems.

Bearing faults classification based on wavelet transform and artificial neural network

Abstract: The most common types of induction rotating machine failures are the mechanical faults induced by misalignment, mechanical imbalance and bearing fault. It is well known that the vibration is the best and the earliest indicator of arising mechanical defect. Thus, this paper presents a novel practical bearing fault diagnosis method based on wavelet package decomposition (WPD) associated with neural network. Firstly, the raw signal is segmented by the use of WPD to a set of sub-signals (coefficients futures). Then, the energy related to the most sensible coefficients that contained the greatest dominant fault information is selected as a distinctive feature fault. The analysis results show that this fault indicator varies under different loads and states (healthy or defective). In order to automate the detection and the location of bearing defect, this feature can be used as an input to the artificial neural network. The proposed approach is capable of discriminating faults from four conditions of rolling bearing, the healthy bearing and the three different types of defected bearings: outer race, inner race, and ball. The experimental results prove the effectiveness of this approach.

Vibration Signal Processing-Based Detection of Short-Circuited Turns in Transformers: A Nonlinear Mode Decomposition Approach

Abstract: Transformers are vital and indispensable elements in electrical systems, and therefore, their correct operation is fundamental; despite being robust electrical machines, they are susceptible to present different types of faults during their service life. Although there are different faults, the fault of short-circuited turns (SCTs) has attracted the interest of many researchers around the world since the windings in a transformer are one of the most vulnerable parts. In this regard, several works in literature have analyzed the vibration signals that generate a transformer as a source of information to carry out fault diagnosis; however this analysis is not an easy task since the information associated with the fault is embedded in high level noise. This problem becomes more difficult when low levels of fault severity are considered. In this work, as the main contribution, the nonlinear mode decomposition (NMD) method is investigated as a potential signal processing technique to extract features from vibration signals, and thus, detect SCTs in transformers, even in early stages, i.e., low levels of fault severity. Also, the instantaneous root mean square (RMS) value computed using the Hilbert transform is proposed as a fault indicator, demonstrating to be sensitive to fault severity. Finally, a fuzzy logic system is developed for automatic fault diagnosis. To test the proposal, a modified transformer representing diverse levels of SCTs is used. These levels consist of 0 (healthy condition), 5, 10, 15, 20, and 25 SCTs. Results demonstrate the capability of the proposal to extract features from vibration signals and perform automatic fault diagnosis.

Imbalance Fault Detection Based on the Integrated Analysis Strategy for Marine Current Turbines under Variable Current Speed.

Abstract: The conversion of marine current energy into electricity with marine current turbines (MCTs) promises renewable energy. However, the reliability and power quality of marine current turbines are degraded due to marine biological attachments on the blades. To benefit from all the information embedded in the three phases, we created a fault feature that was the derivative of the current vector modulus in a Concordia reference frame. Moreover, because of the varying marine current speed, fault features were non-stationary. A transformation based on new adaptive proportional sampling frequency (APSF) transformed them into stationary ones. The fault indicator was derived from the amplitude of the shaft rotating frequency, which was itself derived from its power spectrum. The method was validated with data collected from a test bed composed of a marine current turbine coupled to a 230 W permanent magnet synchronous generator. The results showed the efficiency of the method to detect an introduced imbalance fault with an additional mass of 80–220 g attached to blades. In comparison to methods that use a single piece of electrical information (phase current or voltage), the fault indicator based on the three currents was found to be, on average, 2.2 times greater. The results also showed that the fault indicator increased monotonically with the fault severity, with a 1.8 times-higher variation rate, as well as that the method is robust for the flow current speed that varies from 0.95 to 1.3 m/s.

Faulty Line-Section Identification Method for Distribution Systems Based on Fault Indicators

Abstract: This article proposes a fault identification method that is embedded in an asset management system (AMS) to identify the faulted location based on the fault flags reported from the fault indicators (FIs). The fault identification model based on the Petri-net technology is developed using the distribution network topology generated by a geographic information system. The proposed method uses the data set of fault flags generated by FIs, the statuses of circuit breakers, the available precurrent and postcurrent measurements of FIs, and the loadings of distribution feeders and laterals. The hybrid communication system is applied to provide two-way communication between the control center and FIs. The AMS with the embedded fault identification method can dispatch the repair crews to the fault location faster to accelerate the customer service restoration. A distribution feeder of Taiwan Power Company was selected for the computer simulation to demonstrate the effectiveness of the proposed method by using the communication-capable FIs to identify the exact fault location of distribution lines after a short circuit fault occurs in a distribution system.

Optimal Placement of IoT-Based Fault Indicator to Shorten Outage Time in Integrated Cyber-Physical Medium-Voltage Distribution Network

Abstract: Traditional fault indicators based on 3G and 4G cannot send out fault-generated information if the distribution lines are located in the system across remote mountainous or forest areas. Hence, power distribution systems in rural areas only rely on patrol to find faults currently, which wastes time and lacks efficiency. With the development of the Internet of things (IoT) technology, some studies have suggested combining the long-range (LoRa) and the narrowband Internet of Things (NB-IoT) technologies to increase the data transmission distance and reduce the self-built communication system operating cost. In this paper, we propose an optimal configuration scheme for novel intelligent IoT-based fault indicators. The proposed fault indicator combines LoRa and NB-IoT communication technologies with a long communication distance to achieve minimum power consumption and high-efficiency maintenance. Under this given cyber network and physical power distribution network, the whole fault location process depends on the fault indicator placement and the deployment of the communication network. The overall framework and the working principle of the fault indicators based on LoRa and NB-IoT are first illustrated to establish the optimization placement model of the proposed novel IoT-based fault indicator. Secondly, an optimization placement method has been proposed to obtain the optimal number of the acquisition and collection units of the fault indicators, as well as their locations. In the proposed method, the attenuation of the communication network and the power-supply reliability have been specially considered in the fault location process under the investment restrictions of the fault indicators. The effectiveness of the proposed method has been validated by the analysis results in an IEEE Roy Billinton Test System (IEEE-RBTS) typical system.

Novel Faulted Section Location Method for Distribution Network Based on Status Information of Fault Indicating Equipment

Abstract: This paper proposes a novel faulted section location method for power distribution networks based on the status information collected by fault-indicating equipments. It has an advantage that it does not need to analyze and calculate any electrical parameters, and it is not affected by fault types and system parameters, so this method can be easily applied to power distribution networks. Firstly, a novel method for automatically constructing a line list is proposed in this paper—it can represent the topology structure of the power distribution line and the status information of fault-indicating equipment. Based on the line list, a topology search algorithm is proposed in the method to locate the faulted section. Considering that information loss will cause errors in the line list, an information loss detection algorithm is proposed to detect and correct the wrong status of fault-indicating equipments. Then, different fault conditions such as single fault, multiple faults, distributed generations in system and information loss condition are tested in power distribution networks with different topologies, and the simulation results indicate that the proposed method can deal with each case well. Moreover, the proposed fault location method has an advantage that the running time does not necessarily increase when the node scale is expanded.

Fault Diagnosis of Wind Turbine Bearing Using Variational Nonlinear Chirp Mode Decomposition and Order Analysis

Abstract: In this paper, a method for bearing fault diagnosis without using a tachometer under variable speed conditions by combining the variational nonlinear chirp mode decomposition (VNCMD) with angular resampling technique is proposed. First of all, the original bearing vibration signal can be decomposed and the rotation mode can be reconstructed by using the VNCMD. Then the rotation phase is calculated from the reconstructed rotation mode. In the next step, the original bearing vibration signal is resampled on the basis of the extracted rotation angle information. Finally, the bearing fault indicator can be displayed in the envelope order spectrum and then the bearing fault can be diagnosed. This method addresses the problem that the frequency components are aliasing in the envelope spectrum under variable speed conditions. The reliability and effectiveness of the method proposed in this study are proved by the experiments.

An Imbalance Fault Detection Approach based on Differential Concordia Transform for Marine Current Turbine

Abstract: Marine current turbine (MCT) has gradually entered and contributed to world energy resources. However, MCT imbalance fault often occurs due to the blades are attached by marine biological growth or marine pollutants, and this imbalance fault will disorder the stator current or output power of generator. In this paper, a novel method based on Differential Concordia transform (DCT) for MCT imbalance fault detection is presented. The goal of this method is to provide a sensitive and robust fault indicator to detect the imbalance fault of MCT under wave and turbulence. The proposed method acquires the 3-phases stator current from the generator and using Concordia transform (CT). Then, reconstruct the Concordia transform components (CTC) to gain the Concordia transform modulus (CTM) and calculate differential to remove trend. Finally, the frequency spectral analysis is used to monitor the condition of blade. A 230-W prototype experimental study verified that the proposed method provides an effective fault indicator for MCT imbalance fault.

Detection of Abnormal Status of PV Modules at PV Stations with Complex Installation Conditions

Abstract: Fault diagnosis of PV arrays is important to improve reliability, efficiency, and safety of PV stations. Instead of conventional thresholding methods and artificial intelligent (AI) machine learning approaches, an innovative Gaussian Mixture Model (GMM) based fault detection approach is proposed in this paper. GMM is applied to represent the probabilistic distribution functions (PDF) of different PV module output, and based on Sandia PV Array Performance Model (SPAM), an orientation independent vector C is then developed to eliminate the probability distribution differences of power outputs caused by varying azimuth angles and tilt angles. Three methods (a pseudo method, a method of fitting and a method of group testing) are proposed to obtain PDF of the orientation independent variable. Jensen-Shannon (JS) divergence, which captures the differences between probability density of C of each PV module, are generated and used as a fault indicator. Simulation data acquired from SPAM are used to assess the performance of the proposed approaches, which are later compared in terms of ability to detect, the response time and the generalization capability. Results show that the proposed approaches can successfully detects faults in PV systems, but the method of fitting and method of group testing can detect faults more accurately. This work is especially suitable for the PV modules that have different installation parameters such as azimuth angles and tilt angles, and it does not require installation of irradiance or temperature sensors.

Differential Current Monitoring for the Detection of Inter-Turns Short Circuits in Power Transformers

Abstract: Inter-turns short circuits are a cause of failure that can result in high impact damage to the integrity of power transformers. Their early detection is essential to reduce repair and service interruption costs. One of the primary protections of power transformers are the conventional differential relays, which can detect inter-turns short circuits. However, if the fault involves a few turns, the differential relays are not able to detect it until the inter-turns short circuit progresses due to dielectric and thermal effects to a more severe fault. Therefore, it is of special interest to develop strategies that detect early-stage faults. These strategies can be supplementary and incorporated into the digital differential relays to improve their sensitivity. This paper proposes an algorithm for detecting inter-turns short circuits based on monitoring differential currents. From the analysis of the excitation currents of a power transformer, a single fault indicator applicable to differential currents is determined. Through laboratory experimental tests on a 10 kVA three-phase transformer specially designed to simulate faults, it was shown that for this case study the algorithm is sensitive to faults involving 1.5 to 3.3 % of the winding depending on the phase involved with a current in the fault loop of 1 pu. Moreover, the fault indicator is almost independent of the transformer's load condition, working from no-load condition to overload.

Method for accurately detecting fault information of IT equipment in machine room

Abstract: The invention discloses a method for accurately detecting fault information of IT equipment in a machine room The method comprises the steps that S1, a robot moves to a calibrated stationary point position in front of a to-be-detected cabinet; s2, a plurality of cameras on the robot record videos of the to-be-tested cabinet, and first frame images of the videos recorded by different cameras are spliced into an image covering the whole to-be-tested cabinet up and down; s3, picture analysis is carried out; s4, video analysis is carried out; and S5, picture analysis and video analysis results are integrated to obtain indicating lamp state fault information According to the invention, the state information of the static indicator lamp and the flashing lamp of the IT equipment is analyzed bycombining a deep learning image technical means; the equipment and the fault indicator lamp of the equipment can be detected in time, the information of the equipment and the fault indicator lamp of the equipment can be accurately alarmed, solutions can be pre-judged, workers only need to finally check the inspection report, an innovative technical detection means is provided for data management of the unattended machine room, and the machine room inspection problems of low inspection efficiency, incomplete recording, less inspection, false inspection and the like are effectively solved

Peak-Based Mode Decomposition for Weak Fault Feature Enhancement and Detection of Rolling Element Bearing

Abstract: Rolling element bearings are widely used in rotating machinery to support shafts, whose failures may affect the health of the whole system. However, strong noise interferences often make the bearing fault features submerged and difficult to be identified. Peak-based wavelet method is such a way to reduce certain noise and enhance the fault features by increasing the sparsity of monitored signals. But peak-based wavelet parameters need to be optimized due to the determined basis function and constant resolution, which will affect the efficiency of vibration signal analysis. To address these problems, a peak-based mode decomposition is proposed for weak bearing fault feature enhancement and detection. Firstly, to enhance the differences between repetitive transients and high-frequency noise, a peak-based piecewise recombination is used to convert the middle frequency parts into low-frequency ones. Then, the recombined signal is processed by empirical mode decomposition, combining with a criterion of cross-correlation coefficients and kurtosis. Subsequently, a backward peak transformation is performed to obtain the enhanced signal. Finally, the fault diagnosis is implemented by the squared envelope spectrum, whose normalized squared magnitude is used as a bearing fault indicator. The analysis results of the simulated signals and the experimental signals show that the proposed method can enhance and identify the weak repetitive transient features. The superiority of the proposed method for faint repetitive transient detection is also verified by comparing with the peak-based wavelet method.

Time-of-Failure Probability Mass Function Computation Using the First-Passage-Time Method Applied to Particle Filter-based Prognostics

Abstract: One of the main challenges in prognostics corresponds to the estimation of a system’s probability density function (PDF) for the time-of-failure (ToF) prior to reach a fault condition. An appropriate characterization of the ToF-PDF will let the user know about the remaining useful life of the system or component, allowing the users to prevent catastrophic failures through optimal maintenance schedules. However, the ToF-PDF estimation is not an easy task because it involves both the computation of long-term predictions of a fault indicator of the system and the definition of the hazard zone. In most cases, the trajectory of the fault indicator is assumed as a trajectory with monotonic behavior, and the hazard zone may be considered as a deterministic or probabilistic threshold. This monotonic behavior of the fault indicator enables assuming that the system will only fail once when this indicator reaches the hazard zone, and the ToF-PDF will be estimated according to mathematical definitions proposed in the state-of-the-art. Nevertheless, not all the fault indicators may be considered with a monotonic behavior due to its nature as a stochastic process or regeneration phenomenon, which may entail to errors in the ToF-PDF estimation. To overcome this issue, this paper presents an approach for the estimation of the ToF-PDF using the first-passage-time (FPT) method. This method is focused on the computation of the FPT-PDF when the stochastic process under analysis reaches a specified threshold for the first time only. Accordingly, this work aims to analyze the impact in the estimation of the ToF-PMF (probability mass function) when particle-filter-based prognostics algorithms are used to perform long-term predictions of the fault indicator and compute the probability of failure considering specific hazard zones (which may be characterized by a deterministic value or by a failure likelihood function). A hypothetical self regenerative degradation process is used as a case study to evaluate the performance of the proposed methods.

Installation phase sequence self-identification method for transient wave recording fault indicator

Abstract: An installation phase sequence self-identification method for transient wave recording fault indicators comprises: the transient wave recording fault indicators and a bus zero sequence voltage starting device under the same bus periodically reading time service, and all the transient wave recording fault indicators achieving synchronization of system time and sampling point positions; the transient recording fault indicator circularly storing current and electric field data of each phase; the bus zero-sequence voltage starting device starting triggering wave recording, and all transient wave recording fault indicators under the same bus acquiring current and electric field data; calculating respective current initial phases; taking the current initial phase of the ABC three-phase transientrecording indicator at the lower outlet of the bus as a reference, and respectively comparing the phase differences of the currents of all the other indicators; and determining the phase and the current direction coefficient of the transient recording indicator according to the phase difference. The method is simple to implement and high in efficiency, and is automatically implemented in the whole process through the transient wave recording fault indicator and the main station system without human intervention.