New Method For Fault Detection Research Articles

Fault Detection in a Single-Bus DC Microgrid Connected to EV/PV Systems and Hybrid Energy Storage Using the DMD-IF Method

Variations in fault currents, short times to clear the fault, and a lack of a natural current zero-crossing point are the most important challenges that DC microgrid protection faces. This challenge becomes more complicated with the presence of electric vehicles and energy storage systems due to their uncertainties. For this reason, in this paper, a new method for fault detection in DC microgrids with the presence of electric vehicles and energy storage systems is proposed. The new proposed method uses the combination of dynamic mode decomposition and instantaneous frequency for fault detection. In this method, first, a reference signal is made using the voltage and current signal sampled from the DC microgrid using the dynamic mode decomposition method. Next, in order to detect the fault, the instantaneous frequency value of the reference signal is calculated by the Hilbert transform. The simultaneous use of voltage and current signals reduces the transient effects of the control system on the proposed protection method. In order to measure voltage and current signals, only one intelligent electronic device unit is used in this paper. The proposed new method has been tested on a single-bus DC microgrid with the presence of electric vehicles and energy storage systems in MATLAB 2019b software. The results show that this method can detect all types of faults in DC microgrids, electric vehicles, and photovoltaics. Also, this method is immune to the uncertainties of the generation of distributed generation resources and the existence of noise distortions in the measured signals.

A New Method for Fault Detection and Location in a Low-Resistance Grounded Power Distribution Network Using Voltage Phasor of D-PMUs Data

A New Method for Fault Detection and Location in a Low-Resistance Grounded Power Distribution Network Using Voltage Phasor of D-PMUs Data

Dynamic convolutional gated recurrent unit attention auto-encoder for feature learning and fault detection in dynamic industrial processes

The dynamic characteristics (i.e. autocorrelation and cross-correlation) in modern industrial systems have raised great challenges to process fault detection. To cope with the dynamics and uncertainty of dynamic industrial processes, this paper proposes a new process control method, dynamic convolutional gated recurrent unit attention auto-encoder (DCGRUA-AE) for fault detection in dynamic processes. Firstly, DCGRUA-AE integrates a convolutional gated recurrent unit (CGRU) with a local convolution layer to learn both global and local features of dynamic process data in an unsupervised fashion. Secondly, a dual attention module is embedded in the deep network to preserve effective features. Finally, DeconvGRU combined with a dense layer is used as the encoder to reconstruct the original process data. Two statistics (i.e. T-square (T 2) and squared prediction error (SPE)) based on DCGRUA-AE are used to set the control limits for fault detection. The feasibility and superiority of DCGRUA-AE-based fault detection method have been verified on four industrial processes. The experimental results indicate that the CGRU and dual attention mechanism can significantly improve the fault detection performance of DCGRUA-AE in dynamic processes. The hybrid of CGRU, attention mechanism and auto-encoder provides a new method for fault detection in dynamic industrial processes.

Fault Detection in HVDC System with Gray Wolf Optimization Algorithm Based on Artificial Neural Network

Various methods have been proposed to provide the protection necessitated by the high voltage direct current system. In this field, most of the research is confined to various types of DC and AC line faults and a maximum of two switching converter faults. The main contribution of this study is to use a new method for fault detection in HVDC systems, using the gray wolf optimization method along with artificial neural networks. Under this method, with the help of faulted and non-faulted signals, the features of the voltage and current signals are extracted in a much shorter period of the signal. Subsequently, differences are detected with the help of an artificial neural network. In the studied HVDC system, the behavior of the rectifier, along with its controllers and the required filters are completely modeled. In this study, other methods, such as artificial neural network, radial basis function, learning vector quantization, and self-organizing map, were tested and compared with the proposed method. To demonstrate the performance of the proposed method the accuracy, sensitivity, precision, Jaccard, and F1 score were calculated and obtained as 99.00%, 99.24%, 98.74%, 98.00%, and 98.99%, respectively. Finally, according to the simulation results, it became evident that this method could be a suitable method for fault detection in HVDC systems.

Condition Monitoring of the Dampers in the Railway Vehicle Suspension Based on the Vibrations Response Analysis of the Bogie.

This paper investigates the possibility of developing a new method for fault detection of a damper in the primary suspension of the railway vehicle, based on the analysis of the vertical vibration’s response of the bogie. To this purpose, experimental data are used, along with results from numerical simulations regarding the Root Mean Square (RMS) accelerations measured/simulated in four reference bogie points—two points on the chassis, against the suspension, and two points located against the axle boxes. The experimental data are utilized to define the normal area of operating and the damper failure area in the bogie primary suspension, as well as a basis for validating the results of numerical simulations. The numerical simulations are developed on the basis of two original models of the vehicle–track system, rigid-flexible coupled type, which take into account the elasticity of the vehicle carbody and the elasticity of the wheel-rail contact: a reference model with 15 degrees of freedom, for simulating the bogie response to vertical vibrations for the normal operating of the primary suspension dampers, and an extended model with 20 degrees of freedom, for simulating the bogie vibration response to the failure damper of a primary suspension. The presented results show that there are clear premises on the possibilities of developing a fault detection method of any of the four dampers of the primary suspension corresponding to a vehicle bogie, based on the RMS accelerations measured only in two reference points of the bogie.

Fault Diagnosis Method for MMC-HVDC Based on Bi-GRU Neural Network

The Modular Multilevel Converter-High Voltage Direct Current (MMC-HVDC) system is recognized worldwide as a highly efficient strategy for transporting renewable energy across regions. As most of the MMC-HVDC system electronics are weak against overcurrent, protections of the MMC-HVDC system are the major focus of research. Because of the insufficiencies of the conventioned fault diagnosis method of MMC-HVDC system, such as hand-designed fault thresholds and complex data pre-processing, this paper proposes a new method for fault detection and location based on Bidirectional Gated Recurrent Unit (Bi-GRU). The proposed method has obvious advantages of feature extraction on the bi-directional structure, and it simplifies the pre-processing of fault data. The simplified pre-processing avoids the loss of valid information in the data and helps to extract detailed fault characteristics, thus improving the accuracy of the method. Extensive simulation experiments show that the proposed method meets the speed requirement of MMC-HVDC protections (2 ms) and the accuracy rate reaches 99.9994%. In addition, the method is not affected by noise and has a high potential for practical applications.

A new method for fault detection in a free model context

This paper presents a new method of fault detection based on residual signal generation. Most of the existing diagnostic methods that use the residual to detect a failure are often based on the knowledge of the system model. The developed method does not require a precise knowledge or deep information about the system model. It is based on the reconstruction of the system output via an ultra-local model and a model-free controller. The reconstructed/estimated output is used to build the residual signal which is the fault indicator. Several simulation tests have been performed to evaluate the potential of the proposed approach for fault diagnosis. A fault on an actuator of the system is simulated in linear, non-linear and multi-input non-linear case studies. The simulation results reveal that the fault is successfully detected for all these systems under a noisy environment.

Robust Detection of Minute Faults in Uncertain Systems Using Energy Activity

Fault detection is one of the key steps in Fault Detection and Isolation (FDI) and, therefore, critical for subsequent prognosis or implementation of Fault Tolerant Control (FTC). It is, therefore, advisable to utilize detection algorithms which are quick and can detect the smallest faults. Model-based detection methods satisfy both these criteria and should be preferred. However, a big limitation for model-based methods is that they require the accurate value of the component parameters, which is difficult to obtain in real situations. This limits the accuracy of model-based methods. This paper proposes a new method for fault detection using Energy Activity (EA) which can detect minute levels of fault in systems with high component uncertainty. Different forms of EA are developed for use as an FDI metric. The proposed forms are simulated using a two-tank system under various types of faults. The results are compared with each other and with the traditional model-based FDI method using Analytical Redundancy Relations (ARRs). The simulations are performed considering model uncertainties to check the inherent performance of the methods. From initial simulations, it is established that the integral form of EA is most suited for fault detection. The integral for if EA is then tested using a real two-tank system considering both the model and measurement uncertainties.

A new method for fault detection of aero-engine based on isolation forest

The research on fault detection of aero-engine is of great significance to its safe and reliable operation. In this paper, a dynamic threshold method for aero-engine fault detection based on Isolation Forest (iForest) is proposed. The proposed method can use only normal aero-engine data for training to build the fault detection model, which solves the problem that there is no large amount of fault data for training in the field of aero-engine fault detection due to the limitations of actual conditions. The method is verified by the residual data of the turbofan engine gas path system which is generated by the state variable model under three different fault states. Compared with the results of other methods, it is found that the proposed method can not only achieve high detection accuracy but also has a short running time. It is proved that the proposed method is suitable for fault detection of aero-engine.

A novel unknown input observer‐based fault detection with application to a two‐stage chemical reactor

SummaryThis article presents a new method of fault detection for the two‐stage chemical reactor system. The process can be carried out effectively in the presence of the time‐delay and the unknown inputs and the parameter uncertainties by using the observer‐based method technique. In order to detect the actuator fault, a novel unknown input observer is employed as the residual generator. Multi‐objective optimization techniques and a new performance index are adopted to ensure the robustness and sensitivity of the fault detection observer. Then the problem of fault detection is reduced to the problem of model matching. Furthermore, sufficient conditions are obtained to guarantee that the error system is asymptotically stable with an performance by means of the Lyapunov function technique. Finally, a two‐stage chemical system is borrowed to demonstrate the effectiveness of the obtained methods.

A New Method for Fault Detection of Rotating Machines in Motion Control Applications Using PROFIdrive Information and Support Vector Machine Classifier

Abstract Electric motors are widely used in the industry. Several studies have proposed methods to detect anomalies in their operation, but always using sensors dedicated to this purpose. In this sense, this work aims to fill gaps in related works presenting a method for the detection of faults in rotating machines driven by electric motors in motion control applications using PROFINET network and PROFIdrive profile. The proposed method does not require any additional or dedicated sensors to provide data to the diagnostic system. Instead, the proposed methodology is based on the analysis of data transmitted in the communication network, which already exists for control purposes. Support vector machine (SVM) is used as a classifier of five different mechanical faults. The results provide that the methodology is feasible and efficient under different machine operating conditions, achieving, in the worst case, 97.78% efficiency.

The instrument fault detection and identification based on kernel principal component analysis and coupling analysis in process industry

This paper investigates the fault detection problem of instruments in process industry. Considering the difficulty of fault identification and the problems of multivariable and large computation complexity based on traditional kernel principal component analysis (KPCA), this paper presents a new method for fault detection and identification, which combines the coupling analysis with kernel principal component for multivariable fault detection and employed the local outlier factor (LOF) for multivariable fault identification. The new method consists of three parts. Firstly, according to nonlinear correlation of multivariable, coupling analysis and module division of variables based on detrended cross-correlation analysis (DCCA) are considered to reduce false alarm rate (FAR) and missed detection rate (MDR) in fault detection and identification. Secondly, KPCA is employed to detect fault in each sub-module of variables. Finally, for the sub-module which has the fault detected in second step, the LOF is adopted to calculate abnormal contribution of each variable in sub-modules to realize fault identification. To prove that the new method has the better capability of processing multivariable fault detection and the more accuracy rate on fault detection and identification than the conventional methods of KPCA, a case study on Tennessee process is carried out at the end.

Area-based non-maximum suppression algorithm for multi-object fault detection.

Unmanned aerial vehicle (UAV) photography has become the main power system inspection method; however, automated fault detection remains a major challenge. Conventional algorithms encounter difficulty in processing all the detected objects in the power transmission lines simultaneously. The object detection method involving deep learning provides a new method for fault detection. However, the traditional non-maximum suppression (NMS) algorithm fails to delete redundant annotations when dealing with objects having two labels such as insulators and dampers. In this study, we propose an area-based non-maximum suppression (A-NMS) algorithm to solve the problem of one object having multiple labels. The A-NMS algorithm is used in the fusion stage of cropping detection to detect small objects. Experiments prove that A-NMS and cropping detection achieve a mean average precision and recall of 88.58% and 91.23%, respectively, in case of the aerial image datasets and realize multi-object fault detection in aerial images.

Identifying faults in the building system based on model prediction and residuum analysis

The energy efficiency of the building HVAC systems can be improved when faults in the running system are known. To this day, there are no cost-efficient, automatic methods that detect faults of the building HVAC systems to a satisfactory degree. This study induces a new method for fault detection that can replace a graphical, user-subjective evaluation of a building data measured on site with an automatic, data-based approach. This method can be a step towards cost-effective monitoring. For this research, the data from a detailed simulation of a residential case study house was used to compare a faultless operation of a building with a faulty operation. We argue that one can detect faults by analysing the properties of residuals of the prediction to the actual data. A machine learning model and an ARX model predict the building operation, and the method employs various statistical tests such as the Sign Test, the Turning Point Test, the Box-Pierce Test and the Bartels-Rank Test. The results show that the amount of data, the type and density of system faults significantly affect the accuracy of the prediction of faults. It became apparent that the challenge is to find a decision rule for the best combination of statistical tests on residuals to predict a fault.

Fault diagnosis integrating physical insights into a data-driven classifier

The main goal of this paper is to present a new method for fault detection and isolation with a Bayesian network (BN). This method combines model-based and data-driven frameworks to detect and diagnose single, multiple and unknown faults. We propose an original BN structure with new decision rules. These rules are constructed to take advantage of the prior model knowledge and the available data. Our network presents new perspective to detect unknown fault and outperforms some recent work proposed in Bayesian networks literature. The performance of the method is illustrated on a heating water process simulating several scenarios of operating conditions.

Bearing Fault Diagnosis Based on Collaborative Representation Using Projection Dictionary Pair

In state analysis of rolling bearings using collaborative representation theory, how to construct an excellent redundant dictionary to collaboratively represent the acquired normal or abnormal data has been being a significant issue. Thus, a new method for fault detection and classification of rolling bearings is proposed in this paper. The proposed algorithm mainly consists of three components. First, a wavelet transform is employed to extract features, which takes advantage of the observation that vibration signals under different conditions have similar frequency spectra. This similarity ensures that we can collaboratively represent any test sample by using training samples. Second, under the similarity assumption, a dictionary pair learning strategy is employed to build an overcomplete dictionary pair, which is used to realize an optimal representation of the vibration signal. Meanwhile, the sparse constraint is also taken into account during dictionary training to enhance the robustness of the classification. Finally, the learned dictionary combined with collaborative representation is used to intelligently perform pattern classification of rolling bearings. The effectiveness and superiority of the method are verified by applying the proposed algorithm on the simulated and real vibration signals. The results show that, for different fault categories generated from different fault size and motor loads, our method can rapidly and accurately identify the fault category to which the input sample belongs.

A new fault diagnosis and fault-tolerant control method for mechanical and aeronautical systems with neural estimators

A new method of fault detection and fault-tolerant control is proposed in this article for mechanical systems and aeronautical systems. The faults to be estimated and diagnosed are malfunctions that occurred within the control loops of the systems, rather than some static faults, such as gearbox fault, component cracks, and so on. In the proposed method, two neural networks are used as online estimators, the fault will be accurately estimated when the estimators are adapted online with the post-fault dynamic information. Furthermore, the estimated values of faults are used to compensate for the impact of the faults, so that the stability and performance of the system with the faults are maintained until the faulty components to be repaired. The sliding mode control is used to maintain system stability under the post-fault dynamics. The control law and the neural network learning algorithms are derived using the Lyapunov method, so that the neural estimators are guaranteed to converge to the fault to be diagnosed, while the entire closed-loop system stability is guaranteed with all variables bounded. The main contribution of this article to the knowledge in this field is that the proposed method can not only diagnose and tolerant with constant fault but also diagnose and tolerant with the time-varying faults. This is very important because most faults occurred in industrial systems are time varying in nature. A simulation example is used to demonstrate the design procedure and the effectiveness of the method. The simulation results are compared with the two existing methods that can cope with constant faults only, and the superiority is demonstrated.

Fault diagnosis for gearbox based on EMD-MOMEDA

In this paper, a new method for fault detection of parallel shaft gearbox based on the Empirical Mode Decomposition (EMD) and Multipoint Optimal Minimum Entropy Deconvolution (MOMEDA) is proposed. MOMEDA can overcome the shortcomings of minimum entropy deconvolution (MED) and Maximum Correlated Kurtosis Deconvolution (MCKD), and it is introduced to extract the fault cycle of gearbox signals. The vibration signals of gearbox are complex, including fault signals, noise signals and deterministic signals such as gear meshing component. Fault signal is often buried in these other components, which increases the difficulty of gearbox fault detection. Thus the EMD is proposed to decompose the signal and extract the fault impact components from the signal. The parallel shaft gearbox preset fault experiment is carried out to verify the effectiveness of method. In addition, some traditional methods, such as Fourier transform, cepstrum analysis, MED and MCKD, are used to compare with the proposed methods. Experimental results show that the effectiveness of the proposed method is better than that of traditional methods.

A Trust-Based Formal Model for Fault Detection in Wireless Sensor Networks.

Wireless Sensor Networks (WSNs) are prone to failures and malicious attacks. Trust evaluation is becoming a new method for fault detection in WSNs. In our previous work, a comprehensive trust model based on multi-factors was introduced for fault detection. This model was validated by simulating. However, it needs to be redeployed when adjustment to network parameters is made. To address the redeployment issue, we propose a Trust-based Formal Model (TFM) that can describe the fault detection process and check faults without simulating and running a WSN. This model derives from Petri nets with the characteristics of time, weight, and threshold. Basic structures of TFM are presented with which compound structures for general purposes can be built. The transition firing and marking updating rules are both defined for further system analysis. An efficient TFM analysis algorithm is developed for structured detection models. When trust factor values, firing time, weights, and thresholds are loaded, precise assessment of the node can be obtained. Finally, we implement TFM with the Generic Modeling Environment (GME). With an example, we illustrate that TFM can efficiently describe the fault detection process and specify faults in advance for WSNs.

Decentralized fault detection and isolation using bond graph and PCA methods

This paper proposes a new method for fault detection and isolation of a large-scale system by the bond graph (BG) model and the principal component analysis (PCA) technique in a decentralized architecture. The proposed method is entitled the BG-PCA technique. The main objective is to address the problem of monitoring large-scale system components by fault detection and isolation (FDI) methods. In the modeling framework, the BG model is presented by exploiting its structural and causal properties and the diagnostic bond graph (DBG). In measurement noise and perturbation conditions, the probability density function takes place to generate a clear decision procedure to detect the operating mode. In our approach, we firstly generate the structured residues. Furthermore, with a decentralized architecture, we can locate in what subsystem a fault is first detected. Finally, for isolation, the generation of the structured residues method from PCA is exploited to ensure localization. To validate the suggested BG-PCA algorithm, simulations are computed on a three-tank system to show the efficiency of the proposed FDI method, and satisfactory results are found.