On-line Diagnosis Method Research Articles

On-Line Diagnosis and Fault State Classification Method of Photovoltaic Plant

This paper presents an on-line diagnosis method for large photovoltaic (PV) power plants by using a machine learning algorithm. Most renewable energy output power is decreased due to the lack of management tools and the skills of maintenance engineers. Additionally, many photovoltaic power plants have a long down-time due to the absence of a monitoring system and their distance from the city. The IEC 61724-1 standard is a Performance Ratio (PR) index that evaluates the PV power plant performance and reliability. However, the PR index has a low recognition rate of the fault state in conditions of low irradiation and bad weather. This paper presents a weather-corrected index, linear regression method, temperature correction equation, estimation error matrix, clearness index and proposed variable index, as well as a one-class Support Vector Machine (SVM) method and a kernel technique to classify the fault state and anomaly output power of PV plants.

An Online Classification Method for Fault Diagnosis of Railway Turnouts.

Railway turnout system is a key infrastructure to railway safety and efficiency. However, it is prone to failure in the field. Therefore, many railway departments have adopted a monitoring system to monitor the operation status of turnouts. With monitoring data collected, many researchers have proposed different fault-diagnosis methods. However, many of the existing methods cannot realize real-time updating or deal with new fault types. This paper—based on imbalanced data—proposes a Bayes-based online turnout fault-diagnosis method, which realizes incremental learning and scalable fault recognition. First, the basic conceptions of the turnout system are introduced. Next, the feature extraction and processing of the imbalanced monitoring data are introduced. Then, an online diagnosis method based on Bayesian incremental learning and scalable fault recognition is proposed, followed by the experiment with filed data from Guangzhou Railway. The results show that the scalable fault-recognition method can reach an accuracy of 99.11%, and the training time of the Bayesian incremental learning model reduces 29.97% without decreasing the accuracy, which demonstrates the high accuracy, adaptability and efficiency of the proposed model, of great significance for labor-saving, timely maintenance and further, safety and efficiency of railway transportation.

Diagnosis of Lithium Ion Batteries for Power Electronic Applications

This poster presents a lithium ion battery State-of-charge (SOC) and the capacity estimation technique for electric vehicle applications. The proposed strategy achieves online parameter estimation by making use of adaptive control theory and Kalman observer. The convergence and stability are guaranteed by Lyapunov’s direct method. The estimation strategy takes into account the surface temperature variation. Experiments have been carried-out under different operating temperature conditions and various discharge currents. The effectiveness of the proposed method is verified by experiments for different aging states.State-of-Health (SOH) is an important aspect in Battery Management Systems (BMS) since it is considered, as the battery’s energy. Therefore, bad SOH estimation ultimately results in damaging the battery and reducing its lifespan. Similar to other chemical-based energy storage systems, the battery’s use generates irreversible physical and chemical changes and hence, its performance tends to deteriorate gradually over its lifetime. Several studies have been presented for lithium-ion battery calendar aging and show an internal resistance increase and a capacity decrease. So, the definition of the End-of-Life (EoL) of the battery depends on these aging indicators. The limit is generally set to 80% of the nominal capacity.In our case, the cells are prepared at different State-of-Charge (SOC) values and stressed with different temperatures. The cell parameters as the internal resistance and the capacity are measured periodically with well-defined discharge conditions. In this study, a 20Ah, 3.2V Lithium iron phosphate battery, LiFePO4, is used in the experiments. The battery is characterized, initially and after each aging phase, at different constant currents and at different operating temperatures.In order to study the evolution of the OCV-SOC characterization during the battery lifetime, we have compared the OCV-SOC characterization for different aging states. The results shown that the different between the OCV-SOC estimation and measured is negligible from one aging state to another.Using the experimental protocol results for a constant current, the internal resistance and the capacity evolution for different currents and the different operating temperatures are measured and estimated, and compared.Conclusion : In this study, an online SoC and SOH estimation method is presented for lithium-ion batteries. The proposed strategy capitalizes on the capabilities of Kalman filtering for the design of an extended Kalman observer to estimate SOC. Moreover, the adaptive estimation technique achieves online robust SoH estimation. Unlike other methods, this paper presents an online diagnosis method considering the surface temperature variation of the battery. Moreover, only voltage, current and temperature measurements are required, which reduces the number of sensors compared to other methods. The effectiveness of the proposed online observer is shown through a set of experiments. Results highlight its good performance in parameters estimation for different conditions of operating temperature, discharging current and battery lifetime.

An On-line Diagnosis Method of Broken Rotor Bars Fault in Motor of Aerospace Surveying Vessel via Salient Characteristic Residual Signal from Dynamic Model

Diagnosis of broken rotor bar fault in induction motors is a hot research focus in recent years. The most common method of diagnosis of this fault is to analyse the spectral characteristics of stator current. However the characteristic frequencies are too close to both sides of power frequency and usually covered by it in early failure. To overcome this limitation, this paper presents a method by introducing a dynamic model based on field-oriented control (FOC), which was widely applied in control. The FOC technique is modified to generate an estimated stator current signal in a little computation. The residual signal of stator current is produced by comparing the measured one and the estimated one as the motor running. The result of simulation shows that power frequency component is greatly reduced and the feature of fault is more significant than the measured current signal.

Hydrogen Leakage Diagnosis for Proton Exchange Membrane Fuel Cell Systems: Methods and Suggestions on Its Application in Fuel Cell Vehicles

Proton exchange membrane fuel cell vehicles using hydrogen as fuel has become one of the key development directions in the field of new energy vehicles. However, hydrogen is flammable, it is easy to trigger serious safety accidents once hydrogen leak occurs. Therefore, the application of hydrogen leakage diagnosis methods is essential for the safe operation of proton exchange membrane fuel cell system. This paper reviews the existing methods of diagnosing hydrogen leakage for proton exchange membrane fuel cell system. The principles, research status and application scopes of different methods are elaborated, and these methods are analyzed based on three aspects of on-line diagnosis, real-time performance and calculation complexity. Furthermore, suggestions are provided for the selection of on-line hydrogen leakage diagnosis methods for fuel cell vehicles. Based on this review, readers can easily understand the current status of hydrogen leakage diagnosis methods for proton exchange membrane fuel cell system, and it can provide references for the application selection of the hydrogen leakage diagnosis methods for fuel cell vehicles.

Online monitoring and diagnosis method of transformer winding deformation

Winding deformation has become an important factor affecting the safe and stable operation of transformers. Parameters such as winding resistance, leakage inductance, and capacitance will change as a result of winding deformation. Therefore, a method of diagnosis of transformer winding deformation based on the above three parameters is proposed in this paper. An equivalent model of the transformer including winding resistance, leakage inductance, and equivalent capacitance is established. Online identification of the winding parameters is realized by the recursive least squares algorithm. Both the winding deformation classification and diagnosis of deformation degree are realized by using the winding parameters identified online. A simulation model of the actual transformer is established for verification and analysis. The results indicate that the method has high precision and overall correct rate. It meets the needs of online monitoring of winding deformation engineering. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Online monitoring method based on locus-analysis for high-voltage cable faults

Online diagnosis methods for high-voltage (HV) cable faults have been extensively studied. Power cable fault monitoring is not accurate, and the degree of data fusion analysis of current methods is insufficient. To address these problems, an online monitoring method based on the locus-analysis for HV cable faults is developed. By simultaneously measuring two circulating currents in a coaxial cable, a two-dimensional locus diagram is drawn. The fault criteria and database are established to detect the fault by analyzing the changes of the locus characteristic parameters. A cross-connected grounding simulation model of a high-voltage cable is developed, and several faults at different locations are simulated. Fault identification is established based on the changes in the long axis, short axis, eccentricity, and tilt angle of the track. The simulation and field experiments show that this method can provide an increased amount of fault-monitoring reference information, which can improve the monitoring accuracy and provide a new approach to cable fault monitoring.

Online Diagnosis of Inter-turn Short Circuit for Dual-Redundancy Permanent Magnet Synchronous Motor Based on Reactive Power Difference

The Dual-Redundancy Permanent Magnet Synchronous Motor (DRPMSM) with weak thermal coupling and no electromagnetic coupling among phase windings has two sets of three-phase symmetrical windings. Under normal conditions, two sets of windings can operate simultaneously, and once one set has an inter-turn short circuit fault (ISCF), the other set will operate alone, so a DRPMSM can be applied in fields with high reliability requirements. According to the equivalent circuit principle, a simplified model of a DRPMSM when ISCF occurs is established in this paper, and mathematical equations for the equivalent resistance, inductance and induction electromotive force (EMF) are derived. Based on the simplified circuit principle and instantaneous power theory, combined with the analysis of the fundamental wave in the two sets, an online diagnosis method of the ISCF for DRPMSM is proposed based on the reactive power difference and the feasibility of the method is verified by experiments.

오버랩 제어기법 및 2차 저역통과 RC필터를 이용한 영구자석 BLDC 전동기의 온라인 권선 단락 진단기법

Recently, BLDC motors are used in widened area because of permanent magnet manufacturing technology and drive technology are developed. The use of permanent magnets has been actively pursued for application to fields requiring high efficiency motors due to their high output relative to weight as well as high efficiency advantages over induction motors and wire wound type synchronous motors. However, BLDC motors are not reliable in the realtime diagnosis results, so they are mainly used for diagnosis by direct measurement after decomposition. This method has the disadvantage that the accuracy is guaranteed but the diagnosis time is very long. In order to overcome these drawbacks, this paper proposes a method to diagnose turn fault by using overlap control method and 2nd order RC filter for online diagnosis of permanent magnet BLDC motor. The validity of the proposed diagnosis method is verified by analyzing the results of 2D finite element analysis.

Study of on-line deterioration diagnosis method of engine oil by the electrical property

Study of on-line deterioration diagnosis method of engine oil by the electrical property

A Multimodal Feature Fusion-Based Deep Learning Method for Online Fault Diagnosis of Rotating Machinery.

Rotating machinery usually suffers from a type of fault, where the fault feature extracted in the frequency domain is significant, while the fault feature extracted in the time domain is insignificant. For this type of fault, a deep learning-based fault diagnosis method developed in the frequency domain can reach high accuracy performance without real-time performance, whereas a deep learning-based fault diagnosis method developed in the time domain obtains real-time diagnosis with lower diagnosis accuracy. In this paper, a multimodal feature fusion-based deep learning method for accurate and real-time online diagnosis of rotating machinery is proposed. The proposed method can directly extract the potential frequency of abnormal features involved in the time domain data. Firstly, multimodal features corresponding to the original data, the slope data, and the curvature data are firstly extracted by three separate deep neural networks. Then, a multimodal feature fusion is developed to obtain a new fused feature that can characterize the potential frequency feature involved in the time domain data. Lastly, the fused new feature is used as the input of the Softmax classifier to achieve a real-time online diagnosis result from the frequency-type fault data. A simulation experiment and a case study of the bearing fault diagnosis confirm the high efficiency of the method proposed in this paper.

3D and simplified pseudo-2D modeling of single cell of a high temperature solid oxide fuel cell to be used for online control strategies

In the current study, a single cell of a planar SOFC is firstly modeled in 3D using commercial SOFC module of ANSYS Fluent and the results are validated against the experimental investigations in the literature. Many researchers have used ANSYS Fluent for simulating solid oxide fuel cells. However, there is a huge gap in the literature on explaining the detailed procedure that should be followed in order to use this software effectively. A thorough step-by-step approach is presented to provide a deep insight into the software. Thereafter, a simplified quasi-2D method with infinitely shorter computational time is developed and the results are compared with the 3D model. It is found that the reduced model is capable of being utilized as an alternate method for both online diagnosis and designing active control strategies.

On-Line Detection of Coil Inter-Turn Short Circuit Faults in Dual-Redundancy Permanent Magnet Synchronous Motors

In the aerospace and military fields, with high reliability requirements, the dual-redundancy permanent magnet synchronous motor (DRPMSM) with weak thermal coupling and no electromagnetic coupling is needed. A common fault in the DRPMSM is the inter-turn short circuit fault (ISCF). However, research on how to diagnose ISCF and the set of faulty windings in the DRPMSM is lacking. In this paper, the structure of the DRPMSM is analyzed and mathematical models of the motor under normal and faulty conditions are established. Then an on-line ISCF detection scheme, which depends on the running modes of the DRPMSM and the average values for the difference of the d-axis voltages between two sets of windings in the latest 20 sampling periods, is proposed. The main contributions of this paper are to analyze the calculation for the inductance of each part of the stator windings and propose the on-line diagnosis method of the ISCF under various operating conditions. The simulation and experimental results show that the proposed method can quickly and effectively diagnose ISCF and determine the set of faulty windings of the DRPMSM.

Online diagnostic method of open‐switch faults in PWM voltage source rectifier based on instantaneous AC current distortion

The three-phase pulse-width modulating (PWM) voltage source rectifier (VSR) system is widely used because of its advantages in performance. Numerous studies show that power semiconductors are significant contributors to the overall failure rate of power converters. This study presents an online diagnosis method for a single open-switch fault of a three-phase PWM VSR, because existing research mainly focuses on fault diagnosis of inverters. The proposed method employs current distortion to diagnose faults in a convenient and simple way. It requires no extra hardware but only AC current signals, which are available in the control system and can be embedded into the existing driving software as a subroutine without excessive computational effort. The proposed method offers high diagnostic effectiveness and reliability. Experimental results show that the proposed method can diagnose the faulty switch at low currents as well as under abrupt load transient conditions without false alarms. Furthermore, the average diagnostic time of this method is only 1.8 ms.

Issue Highlights

The Canadian Journal of Chemical EngineeringVolume 96, Issue 2 p. 423-423 Issue HighlightsFree Access Issue Highlights First published: 02 January 2018 https://doi.org/10.1002/cjce.22973AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat 426 Online Incipient Fault Diagnosis Based on Kullback–Leibler Divergence and Recursive Principle Component Analysis Yi Chai, Songbing Tao, Wanbiao Mao, Ke Zhang and Zhiqin Zhu For principal component analysis (PCA), the Hotelling's T2, that is the most common used statistical distance, can fail in detecting small shifts such as a sensor incipient fault with low fault-to-noise ratio (FNR). In this study, a realistic online diagnosis method for incipient faults with low FNR is presented utilizing the Kullback-Leibler divergence (KLD) and recursive PCA. From simulations, it is shown that the proposed approach can detect, isolate, and estimate the sensor incipient fault of the multivariate AR system successfully. 444 Modified Partial Least Square for Diagnosing Key-Performance-Indicator-Related Faults Siyuan He, Youqing Wang and Changqing Liu Faults related to a key performance indicator (KPI) should be paid much attention in the field of multivariate statistical process monitoring. To detect KPI faults, the process data should be divided into KPI-related and KPI-unrelated parts; however, all existing methods did not decompose the residual space according to KPI. In this study, balancing the influence of principal component space and residual space on KPI, the authors find the suitable projection matrix for partial least square (PLS), which can achieve perfect partition of process data in terms of KPI. The novel modification is named as KPI-PLS. 463 Fault Diagnosis Based on the Integration of Exponential Discriminant Analysis and Local Linear Embedding Ruixuan Wang, Jing Wang, Jinglin Zhou and Haiyan Wu The paper proposes two fault diagnosis methods based on the fusion of the exponential discriminant analysis and manifold learning methods, LLEDA and NPEDA. LLEDA, a parallel strategy, focuses on the global supervised discriminant with the tradeoff of local nonlinearity preserving. NPEDA, a cascaded strategy, implements the discriminant analysis with each data firstly constructed by the linear weighted combination of its neighbours. These methods both combine the global discriminant analysis with local structure preserving, so they are particularly suitable for the feature extraction of data with strong nonlinearity and high dimensions. References 1 Y. Chai, S. Tao, W. Mao, K. Zhang, Z. Zhu, Can. J. Chem. Eng. 2018, 96, 426. 2 S. He, Y. Wang, C. Liu, Can. J. Chem. Eng. 2018, 96, 444. 3 R. Wang, J. Wang, J. Zhou, H. Wu, Can. J. Chem. Eng. 2018, 96, 463 Volume96, Issue2February 2018Pages 423-423 ReferencesRelatedInformation

Online incipient fault diagnosis based on Kullback‐Leibler divergence and recursive principle component analysis

AbstractFault detection and isolation (FDI) methods based on the principal component analysis (PCA) model have achieved a large number of theoretical studies and applications, especially for complex and highly dimensional processes. However, the Hotelling's T2, that is the most common used statistical distance, can fail in detecting small shifts such as a sensor incipient fault with low fault‐to‐noise ratio (FNR). Although an incipient fault develops slowly, it cannot be ignored and is necessary to be detected early enough to avoid more serious consequences. In this study, a realistic online diagnosis method for incipient faults with low FNR is presented. Based on probability distribution measure, the Kullback‐Leibler divergence (KLD) is utilized to compare the probability density of each of the latent scores to a reference one. Under the hypothesis of Gaussian distribution, dynamic changes of KLDs are computed via the mean and variance of score vectors, which can be updated online utilizing the recursive principal component analysis (RPCA). From simulations, it is shown that the proposed approach can detect, isolate, and estimate the sensor incipient fault of the multivariate AR system successfully.

Diagnosability and online diagnosis of discrete-event systems modeled by acyclic labeled Petri nets

We address in this paper the problems of online diagnosis and verification of language diagnosability of discrete event systems (DES) modeled by acyclic labeled Petri nets, in which, different transitions can be labeled by the same event (observable, unobservable and failure). The proposed diagnoser makes its decision regarding the failure occurrence by storing the sequence of observed events and, after each occurrence of observable event, it verifiers if two sets of inequalities are satisfied; the first set accounts for the normal whereas the second one accounts for the faulty behavior of the system. We also consider the problem of diagnosability verification by creating new sets of inequalities that, when satisfied, allow us to decide whether the language generated by the Petri net is diagnosable. Our method for online diagnosis has the advantage over previously ones for relying only on the verification of set of inequalities. Regarding language diagnosability, our verification algorithm does not require any knowledge of automaton theory, being self-contained within the Petri net formalism.

Online Diagnosis and Localization of High-Resistance Connection in PMSM With Improved Fault Indicator

This paper proposes a method for online diagnosis and localization of high-resistance connection (HRC) in permanent magnet synchronous machine (PMSM) with an improved fault indicator. In the proposed method, the amplitude and initial phase angle of zero-sequence voltage component are both analyzed. Based on the analysis, the fault indicator with angle difference is defined for fault diagnosis and localization. It is shown that not only the HRC can be detected, but also the fault severity can be assessed and the faulty phases can be identified in this method. The simulation and experiment are carried out to verify the theoretical analysis. Both the results show that the proposed method can be effectively applied for online detection and identification of the HRC in the PMSM.

Supercapacitor Aging and Diagnosis

The lifetime of an energy storage component is the time required to fail. The failure is defined as the lack of ability of a component to fulfill it's specified function. One of its characteristics will be out of the specification. For example the capacitance will be below its specified limit value, or the series resistance will be above its specified value, the component will be leaking, or will be opened. This paper is about supercapacitor aging and diagnosis. At the first, supercapacitor aging according to the temperature and to the voltage is presented. Experimental tests are realized at constant temperature when the supercapacitors are polarized at the maximum voltage. To quantify the supercapacitor aging, the equivalent series resistance and the equivalent capacitance are measured. To lead to the determination of the supercapacitor lifetime, Arrhenius law, that describes the effect of temperature on the velocity of a chemical reaction, is considered. The second part of this paper presents an online diagnosis method for supercapacitors’ aging problem. The proposed strategy capitalizes on the capabilities of the sliding mode for the design of a sliding mode observer. Therefore, online parameters’ estimation is achieved, which yields State-of-Health (SoH) prediction. Unlike other methods such as electrochemical impedance spectroscopy, where estimation is performed offline and requires interruption of the system’s operation, this paper presents an online diagnosis method. Moreover, only voltage and current measurements are required. The effectiveness of the proposed online observer is shown through a set of experiments. Results highlight its good performance in parameters estimation with robustness to current’s nonlinearities.

Impact of Electrical Contact Resistance on the High-Speed Transmission and On-Line Diagnosis of Electrical Connector Intermittent Faults

Electrical connectors are widely used in many kinds of main equipment. It is very difficult, costly, and time consuming for the off-line diagnosis of intermittent faults (IFs) in connectors. To realize the on-line diagnosis by the traditional method, the voltage drop between two ends of the connector should be measured continuously at a high sample rate. This continuous measurement requires enormous testing resources, which is impractical in engineering fields. To solve this problem, this paper proposes an on-line diagnosis method in condition of the single-end test for the electrical connector IFs. To determine the fault threshold of the electrical contact resistance (ECR), first the impact of the ECR on high-speed transmission is studied, and then the connector fault feature in condition of the single-end test, which is called the insertion loss increment (ILI), is extracted. The measurement method of the ILI is also presented. Based on the above analysis, an ILI-based on-line diagnosis method for the connector IFs is specified. According to the qualitative and quantitative tests, the proposed diagnosis method is verified. The verification results show that the cumulative insertion loss increment, which is calculated from the ILI can truly exhibit the variation of ECR, and the error of the ILI-based diagnosis method is only about 1%.