Online Fault Diagnosis Research Articles

Research on Parameter Distribution Features of Photovoltaic Array under the Cover and Shadow Shading Conditions

The outdoor operating photovoltaic arrays have two different shading conditions, shadowing and covering. The shading causes a decrease in output power of photovoltaic system and may bring hot spots which causes physical damage to the array. This paper studies the electrical parameter distribution feature of photovoltaic array under different shading conditions by means of analog simulation and empirical testing. Through introducing theoretical computational method of the electrical parameters, it describes the distribution features of the electrical parameters of photovoltaic array. The results indicate that the influence of local shadowing on the current of array can be neglected. Shadowing decreases the optimal operating voltage while covering leads to a decrease in the optimal operating voltage and the open-circuit voltage. The drop magnitude of voltage is associated with the number of the shaded cell strings and the string voltage. The two shading types can be identified on the basis of distribution rules of open-circuit voltage and optimal operating voltage. Simulations and experiments verify the consistency of the rules. Relevant conclusions provide a reference for modeling, online fault diagnosis, and optimization design of the maximum power tracking algorithm of photovoltaic array under different shading conditions.

Fault Feature Extraction of Diesel Engine Based on Bispectrum Image Fractal Dimension

Fault feature extraction has a positive effect on accurate diagnosis of diesel engine. Currently, studies of fault feature extraction have focused on the time domain or the frequency domain of signals. However, early fault signals are mostly weak energy signals, and time domain or frequency domain features will be overwhelmed by strong background noise. In order consistent features to be extracted that accurately represent the state of the engine, bispectrum estimation is used to analyze the nonlinearity, non-Gaussianity and quadratic phase coupling (QPC) information of the engine vibration signals under different conditions. Digital image processing and fractal theory is used to extract the fractal features of the bispectrum pictures. The outcomes demonstrate that the diesel engine vibration signal bispectrum under different working conditions shows an obvious differences and the most complicated bispectrum is in the normal state. The fractal dimension of various invalid signs is novel and diverse fractal parameters were utilized to separate and characterize them. The value of the fractal dimension is consistent with the non-Gaussian intensity of the signal, so it can be used as an eigenvalue of fault diagnosis, and also be used as a non-Gaussian signal strength indicator. Consequently, a symptomatic approach in view of the hypothetical outcome is inferred and checked by the examination of vibration signals from the diesel motor. The proposed research provides the basis for on-line monitoring and diagnosis of valve train faults.

Ultraviolet differential spectroscopy quantitative analysis of SF 6 decomposition component SO 2

SO2 is a symbolic gas that can indicate the insulation status of SF6 insulation electrical equipment. Its accurate quantitative measurement is of the great significance for the judgement of the type and severity of faults inside the insulation equipment. For this reason, a detection system of trace SO2 based on ultraviolet (UV) spectrometry was presented. The measured UV absorption spectra of SO2 were processed using the wavelet analysis to remove useless spectra information and get the UV differential absorption spectra of SO2. The fast Fourier transform (FFT) was used to analyse UV differential spectra in the band of 190–230 and 290–310 nm and extract the feature information. The results show that there is a good linear relationship between the FFT characteristic peak value and the concentration of trace SO2 in the two wave bands. The detection limit in the band of 190–230 nm was even lower than that in the band of 290–310 nm. Besides, the differential absorption cross section of SO2 decreases with the increase of temperature. The study has laid a foundation for the application of the UV differential absorption spectroscopy in the online monitoring and fault diagnosis of SF6 insulation electrical equipment.

Detecting the internal short circuit in large-format lithium-ion battery using model-based fault-diagnosis algorithm

Abstract The spontaneous internal short circuit that sporadically occurs during operation is an unsolved safety problem that hinders the widespread application of lithium ion batteries. An online fault-diagnosis algorithm is an urgent requirement for early detection of the spontaneous internal short circuit of lithium-ion batteries to guarantee safe operation. This paper presents a model-based fault-diagnosis algorithm for online internal-short-circuit detection. Relying on the theory of model-based control, the algorithm transforms the measured voltage and temperature to the intrinsic electrochemical status that can reflect typical internal-short-circuit features, i.e. the excessive depletion of capacity and abnormal heat generation. The estimated status of the suspicious cell deviates from the average value of the battery pack, therefore the algorithm can capture the internal-short-circuit fault by evaluating the levels of deviation. Simultaneously considering the diagnosis result calculated from both the voltage and temperature signal helps enhance the robustness of the algorithm with few false alarms. Substitute internal-short-circuit tests confirm that the algorithm is capable of identifying the internal-short-circuit fault before it develops into a severe hazard, e.g., thermal runaway. The equivalent short resistance, which can reflect the level of the internal short circuit, can be estimated with small error by the online fault-diagnosis algorithm.

On-line diagnosis of inter-turn short circuit fault for DC brushed motor

Extensive research effort has been made in fault diagnosis of motors and related components such as winding and ball bearing. In this paper, a new concept of inter-turn short circuit fault for DC brushed motors is proposed to include the short circuit ratio and short circuit resistance. A first-principle model is derived for motors with inter-turn short circuit fault. A statistical model based on Hidden Markov Model is developed for fault diagnosis purpose. This new method not only allows detection of motor winding short circuit fault, it can also provide estimation of the fault severity, as indicated by estimation of the short circuit ratio and the short circuit resistance. The estimated fault severity can be used for making appropriate decisions in response to the fault condition. The feasibility of the proposed methodology is studied for inter-turn short circuit of DC brushed motors using simulation in MATLAB/Simulink environment. In addition, it is shown that the proposed methodology is reliable with the presence of small random noise in the system parameters and measurement.

A Portable Fault Site Investigation and Diagnosis Device for Energy Metering Equipment

Currently, the number of on-site installation acquisition terminals and energy meters in the power system is very large. Because of the complexity of the environment, result in more on-site measurement of failure to field operation and maintenance personnel a great deal of pressure, there is an urgent need for fast, automated troubleshooting and fault diagnosis of the metering device. However, at present the domestic market has not specifically for the measurement of field fault detection device. And on-line monitoring fault diagnosis system reported in the literature, can only diagnose the communication of the metering device, which is difficult to meet the actual needs. This paper proposes a portable fault site investigation and diagnosis device for energy metering equipment. The proposed device can be applied to the field of energy metering equipment maintenance, which is proved by our experiments.

A Fault Detection Device for Energy Metering Equipment

Currently, the number of on-site installation acquisition terminals and energy meters in the power system is very large. Because of the complexity of the environment, result in more on-site measurement of failure to field operation and maintenance personnel a great deal of pressure, there is an urgent need for fast, automated troubleshooting and fault diagnosis of the metering device. However, at present the domestic market has not specifically for the measurement of field fault detection device. And on-line monitoring fault diagnosis system reported in the literature, can only diagnose the communication of the metering device, which is difficult to meet the actual needs. This paper proposes a fault detection device for energy metering equipment. The proposed device can be applied to the field of energy metering equipment maintenance, which is proved by our experiments.

An analytical method for mapping alarm information to model of power grid fault diagnosis

Online fault diagnosis systems have recently been applied in power grids. However, the complex modeling and high‐quality requirements for power grid fault diagnosis have restricted the wide application of online systems. This paper proposes an automatic method for mapping alarm information to a fault diagnosis model. First, we automatically extract the basic logic variables from the alarm information. These can be used in power grid fault diagnoses by employing a key character‐matching algorithm. We then build the associative relationship between electrical devices and circuit breakers based on connection analysis. Finally, an associative matrix of all electrical devices is designed to express the cooperative relationship between different devices in a fault event based on the short‐circuit power mark. Based on these modules, cause–effect events expressing the associated relationship between alarms are established according to the protection configuration principle and protective relay setting principle. Expressing the associated relationship between alarms according to the logical requirements of a fault diagnosis model enables the automatic mapping from alarm information to fault diagnosis model to be realized. The validity of the proposed method for online fault diagnosis is verified using a real fault case that occurred in a power grid.

Online Fault Diagnosis in Discrete Event Systems with Partially Observed Petri Nets

This paper investigates the fault detection problem for Discrete Event Systems (DES) which can be modeled by Partially Observed Petri Nets (POPN). To overcome the problem of low diagnosability in the POPN online fault diagnoser in current use, we propose an improved online fault diagnosis algorithm that integrates Generalized Mutual Exclusion Constraints (GMEC) and Integer Linear Programming (ILP).We assume that the POPN structure and its initial markings are known, and the faults are modeled as unobservable transitions. First, the event sequence is observed and recorded. We use GMEC for elementary diagnosis of the system behavior,then the ILP problem of POPN is solved for further diagnosis. Finally, we modeled and analyzed an example of a real DES to test the new fault diagnoser. The proposed algorithm increased the diagnosability of the DES remarkably, and the effectiveness of the new algorithm integrating GMEC and ILP was verified.

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

In “Online Incipient Fault Diagnosis Based on Kullback-Leibler Divergence and Recursive Principal Component Analysis” by Chai, Tao, Mao, Zhang, and Zhu,1 the Acknowledgements section with the following text was omitted. This work was supported by the National Natural Science Foundation of China (Grants No. 61374135 and 61633005). The authors regret the omission.

Study on fault diagnosis algorithm in WSN nodes based on RPCA model and SVDD for multi-class classification

For characteristics of the wireless sensor network (WSN) nodes data streaming in the application environment, the limitations of conventional principal component analysis (PCA) method which depend on the static model in practical application are discussed, an online fault diagnosis algorithm in WSN nodes based on recursive PCA (RPCA) model and support vector data description (SVDD) for multi-class classification is proposed in this paper. The main contents of the method include:The algorithm first applies recursive eigenvalue decomposition techniques based on first-order perturbation (FOP) analysis to update the PCA model adaptively and realize the online fault detection, and then uses SVDD based multi-class classification algorithm to diagnose the fault types. Experimental results show that the algorithm can satisfy the real time needs of data stream processing, but also can track the data changes well. The experimental results based on data sets in real field and experimental data off our typical node failures demonstrate the effectiveness of the proposed algorithm. The algorithm proposed in this paper would improve the safety factor of monitoring sites and it can allows us to know the working state of the node in time and repair or replace it at first time.

Bolster spring fault detection strategy for heavy haul wagons

ABSTRACTAn on-board health monitoring system is proposed for heavy haul wagons in this paper including a signal-based fault detection and isolation (FDI) method and an on-line fault diagnose strategy. Such a system, to be feasible on freight wagons, must be sufficiently cheap and robust, hence the design assumes the constraint of using only two accelerometers mounted on the front left and right rear part of each carbody in a heavy haul train. This paper focuses on the detection of bolster spring fault conditions. The problem is made more complex by the modes of failure which might be expected in bolster spring nests. Types of spring failure are firstly identified and discussed covering situations of broken (shortening springs) and softening (individual spring loss from a nest or cross-section loss through corrosion). The effects of these faults and their detectability were investigated using simulations on straight and curved track and using a fully detailed model of a typical 40 t axle-load heavy haul wagon. The simulation results were then examined and compared using cross-correlation analysis and an FDI system was proposed. The FDI system introduced five possible fault indicators. Initial results indicated that it was possible to detect changes in bolster stiffness of ±25%. An on-line fault diagnoses strategy is proposed for bolster spring faults which only requires data from wagon monitoring during travel around sharp curves to detect and the occurrence of confirm faults. The functionality envisaged needs only a ‘once per trip’ monitoring site, such as a sharper curve, and is aimed at condition monitoring rather than the provision of alarms or comprehensive monitoring of all events.

An intelligent hybrid methodology of on-line system-level fault diagnosis for nuclear power plant

Abstract To assist operators to properly assess the current situation of the plant, accurate fault diagnosis methodology should be available and used. A reliable fault diagnosis method is beneficial for the safety of nuclear power plants. The major idea proposed in this work is integrating the merits of different fault diagnosis methodologies to offset their obvious disadvantages and enhance the accuracy and credibility of on-line fault diagnosis. This methodology uses the principle component analysis-based model and multi-flow model to diagnose fault type. To ensure the accuracy of results from the multi-flow model, a mechanical simulation model is implemented to do the quantitative calculation. More significantly, mechanism simulation is implemented to provide training data with fault signatures. Furthermore, one of the distance formulas in similarity measurement—Mahalanobis distance—is applied for on-line failure degree evaluation. The performance of this methodology was evaluated by applying it to the reactor coolant system of a pressurized water reactor. The results of simulation analysis show the effectiveness and accuracy of this methodology, leading to better confidence of it being integrated as a part of the computerized operator support system to assist operators in decision-making.

Online fault diagnosis in partially observed Petri nets

This paper investigates the fault detection problem for discrete event systems (DES) which can be modelled by partially observed Petri nets (POPN). To overcome the problem of low diagnosability in the POPN online fault diagnoser in current use, we propose an improved online fault diagnosis algorithm that integrates generalised mutual exclusion constraints (GMEC) and integer linear programming (ILP). We assume that the POPN structure and its initial markings are known, and the faults are modelled as unobservable transitions. First, the event sequence is observed and recorded. Then, the ILP problem of POPN is solved for elementary diagnosis of the system behaviour. While this system diagnoses that some faults may have happened, we also use GMEC for further diagnosis. Finally, we modelled and analysed an example of a real DES to test the new fault diagnoser. The proposed algorithm increased the diagnosability of the DES remarkably, and the effectiveness of the new algorithm integrating GMEC and ILP was verified.

On-line Fault Diagnosis of Produced Water Treatment with Multilevel Flow Modeling

Making sense of alarms can be difficult on oil and gas platforms. Multilevel Flow Modeling provides a structure for modelling plant functionality and inferring causes for alarms and predicting consequences. Currently, Multilevel Flow Modeling has limited application for on-line fault diagnosis. Based on a fault emulated on a pilot plant for offshore produced water treatment, Multilevel Flow Modeling is used for reasoning about causes for triggered alarms. The inferred causes are analysed to investigate the current maturity of Multilevel Flow Modeling for on-line diagnosis.

Fault Diagnosis of Uncertain Hybrid Systems

This paper presents an online monitoring and fault diagnosis architecture for hybrid systems with nonlinear uncertain discrete-time dynamics. The architecture features a modular observer based on a modified hybrid automaton framework, that models each subsystem individually and the whole system as a composition of these models. The fault detection scheme can detect both discrete and parametric faults using a mode dependent time-varying threshold that guarantees no false-positives alarms due to modeling uncertainty. The fault isolation scheme can anticipate which fault events may have occurred and dynamically employs only the necessary number of isolation estimators that try to isolate the fault by calculating residual thresholds and approximating the unknown parameter values. Simulation results of a two-tank hybrid system example illustrate the effectiveness of the proposed approach.

A Fault Diagnosis Method for On Load Tap Changer of Aerospace Power Grid Based on the Current Detection

As one of the key components of aerospace power grid, the performance of on load tap changer (OLTC) of power transformer is very important. So it is necessary to realize real-time monitoring of its status. At present, there is no feasible monitoring scheme to carry out on-line monitoring and fault diagnosis for detecting faults of OLTC. The current waveform of the switching process will be abnormal due to the contact fault, the transition resistance fault, and most of the mechanical faults. This paper presents a fault diagnosis method for OLTC based on the current detection in the process of switching. Through on-line acquisition of current waveform in the switching process, empirical mode decomposition and Hilbert–Huang transform is used to calculate normalized time-frequency spectrum, and extract fault characteristic quantity, which can realize the comprehensive diagnosis of the running state of the equipment, and identify the early fault characteristics, and promote the process of the OLTC’s maintenance, from regular test to condition based maintenance. Experiment results prove that the designed fault diagnosis instrument can effectively monitor the working condition of OLTC during the switching process and make effective fault diagnosis.

Application of artificial neural networks for the fault detection and diagnosis of active magnetic bearings

Active magnetic bearings (AMBs) are the class of advanced mechatronic systems. The stable operation of these depends on the normal operation of its sensors and actuators whose malfunctioning may disturb the stability of the supported rotor. Therefore, online fault detection and diagnosis (FDD) of sensors and actuators of AMB system is essential for safe and reliable operation. Model based FDD requires complex mathematical modelling and has higher chances of subjected to modelling errors. Redundant sensors and actuators based FDD incurs additional cost and also requires additional space for installation. Therefore, in the present work, simulation data driven artificial neural network (ANN) based methodology with statistical analysis is proposed for FDD of AMBs. Faults in single position-sensor or actuator as well as in multiple sensors and actuators are detected and diagnosed simultaneously. Various types of faults such as bias, multiplicative and noise addition are considered for the diagnosis.

Application of artificial neural networks for the fault detection and diagnosis of active magnetic bearings

Active magnetic bearings (AMBs) are the class of advanced mechatronic systems. The stable operation of these depends on the normal operation of its sensors and actuators whose malfunctioning may disturb the stability of the supported rotor. Therefore, online fault detection and diagnosis (FDD) of sensors and actuators of AMB system is essential for safe and reliable operation. Model based FDD requires complex mathematical modelling and has higher chances of subjected to modelling errors. Redundant sensors and actuators based FDD incurs additional cost and also requires additional space for installation. Therefore, in the present work, simulation data driven artificial neural network (ANN) based methodology with statistical analysis is proposed for FDD of AMBs. Faults in single position-sensor or actuator as well as in multiple sensors and actuators are detected and diagnosed simultaneously. Various types of faults such as bias, multiplicative and noise addition are considered for the diagnosis.

On-line Fault Diagnosis of High Temperature Ceramic Filter Fracture Based on Dynamic Pressure

On-line Fault Diagnosis of High Temperature Ceramic Filter Fracture Based on Dynamic Pressure