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Journal articles on the topic 'Real-Time Fault Detection'
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1
Leite, Denis, Aldonso Martins, Diego Rativa, Joao F. L. De Oliveira, and Alexandre M. A. Maciel. "An Automated Machine Learning Approach for Real-Time Fault Detection and Diagnosis." Sensors 22, no. 16 (August 17, 2022): 6138. http://dx.doi.org/10.3390/s22166138.
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This work presents a novel Automated Machine Learning (AutoML) approach for Real-Time Fault Detection and Diagnosis (RT-FDD). The approach’s particular characteristics are: it uses only data that are commonly available in industrial automation systems; it automates all ML processes without human intervention; a non-ML expert can deploy it; and it considers the behavior of cyclic sequential machines, combining discrete timed events and continuous variables as features. The capacity for fault detection is analyzed in two case studies, using data from a 3D machine simulation system with faulty and non-faulty conditions. The enhancement of the RT-FDD performance when the proposed approach is applied is proved with the Feature Importance, Confusion Matrix, and F1 Score analysis, reaching mean values of 85% and 100% in each case study. Finally, considering that faults are rare events, the sensitivity of the models to the number of faulty samples is analyzed.
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Sanchez, Oscar D., Gabriel Martinez-Soltero, Jesus G. Alvarez, and Alma Y. Alanis. "Real-Time Neural Classifiers for Sensor and Actuator Faults in Three-Phase Induction Motors." Machines 10, no. 12 (December 10, 2022): 1198. http://dx.doi.org/10.3390/machines10121198.
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The main steps involved in a fault-tolerant control (FTC) scheme are the detection of failures, isolation and reconfiguration of control. Fault detection and isolation (FDI) is a topic of interest due to its importance for the controller, since it provides the necessary information to adjust and mitigate the effects of the fault. Generally, the most common failures occur in the actuator or in sensors, so this article proposes a novel model-free scheme for the detection and isolation of sensor and actuator faults of induction motors (IM). The proposed methodology performs the task of detecting and isolating faults over data streams just after the occurrence of the failure of an induction motor (IM), by the occurrence of either disconnection, degradation, failure, or connection damage. Our approach proposes deep neural networks that do not need a nominal model or generate residuals for fault detection, which makes it a useful tool. In addition, the fault-isolation approach is carried out by classifiers that differentiate characteristics independently of the other classifiers. The long short-term memory (LSTM) neural network, bidirectional LSTM, multilayer perceptron and convolutional neural network are used for this task. The proposed sensors’ and actuator’s fault detection and isolation scheme is simple. It can be applied to various problems involving fault detection and isolation schemes. The results show that deep neural networks are a powerful and versatile tool for fault detection and isolation over data streams.
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CHAKKOR, Saad, Mostafa BAGHOURI, and Abderrahmane HAJRAOUI. "INTELLIGENT FAULT DETECTION DEVICE FOR WIND TURBINE IN REAL TIME CONDITION MONITORING." Acta Electrotechnica et Informatica 15, no. 1 (March 1, 2015): 34–41. http://dx.doi.org/10.15546/aeei-2015-0006.
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Abassi, Moez, Omar Khlaief, Oussama Saadaoui, Abdelkader Chaari, and Mohamed Boussak. "Real-time implementation of discrete Fourier transform phase analysis and fault tolerant control for PMSM in electric vehicles." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 37, no. 1 (January 2, 2018): 432–47. http://dx.doi.org/10.1108/compel-02-2017-0052.
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Purpose Electric vehicles (EVs) require uninterrupted and safe conditions during operations. Therefore, the diagnostic of power devices and electric motor faults are needed to improve the availability of the system. Hence, fault-tolerant control (FTC), which combines switch fault detection, hardware redundancy and post-fault control, is used. This paper aims to propose an accurate open-phase fault detection and FTC of a direct torque control permanent magnet synchronous motor electrical vehicles by using discrete Fourier-transform phase method. Design/methodology/approach The main idea is to propose detection and identification of open-phase fault (faulty leg) among three phases voltage source invertor (VSI)-fed permanent magnet synchronous motor drives. Once the faulty leg is detected and isolated, a redundant phase leg insertion, shared by a three-phase VSI, is done by using independent bidirectional TRIAC switches to conduct FTC system. This accurate fault detection significantly improves system availability and reliability. The proposed method of open-phase fault detection and identification is based only on stator phase current measurement. Findings A novel method is proposed with experimental validation for fault detection, isolation and FTC for a three-phase VSI-fed permanent magnet synchronous motor. Originality/value The novel discrete Fourier-transform phase method is proposed to detect an open phase based on the measurement in real time of the instantaneous phase of stator current components in the stationary frame. The experimental implementation is carried out on powerful dSpace DS1104 controller board based on the digital signal processor TMS320F240. The validity of the proposed method has been experimentally verified.
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Zhang, Chuang, Xiubin Zhao, Chunlei Pang, Liang Zhang, and Bo Feng. "The Influence of Satellite Configuration and Fault Duration Time on the Performance of Fault Detection in GNSS/INS Integration." Sensors 19, no. 9 (May 9, 2019): 2147. http://dx.doi.org/10.3390/s19092147.
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For the integration of global navigation satellite system (GNSS) and inertial navigation system (INS), real-time and accurate fault detection is essential to enhance the reliability and precision of the system. Among the existing methods, the residual chi-square detection is still widely used due to its good real-time performance and sensibility of fault detection. However, further investigation on the performance of fault detection for different observational conditions and fault models is still required. In this paper, the principle of chi-square detection based on the predicted residual and least-squares residual is analyzed and the equivalence between them is deduced. Then, choosing the chi-square detection based on the predicted residual as the research object, the influence of satellite configuration and fault duration time on the performance of fault detection is analyzed in theory. The influence of satellite configuration is analyzed from the number and geometry of visible satellites. Several numerical simulations are conducted to verify the theoretical analysis. The results show that, for a single-epoch fault, the location of faulty measurement and the geometry have little effect on the performance of fault detection, while the number of visible satellites has greater influence on the fault detection performance than the geometry. For a continuous fault, the fault detection performance will decrease with the increase of fault duration time when the value of the fault is near the minimal detectable bias (MDB), and faults occurring on different satellite’s measurement will result in different detection results.
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Wang, Haitao. "Application of Residual-Based EWMA Control Charts for Detecting Faults in Variable-Air-Volume Air Handling Unit System." Journal of Control Science and Engineering 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/1467823.
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An online robust fault detection method is presented in this paper for VAV air handling unit and its implementation. Residual-based EWMA control chart is used to monitor the control processes of air handling unit and detect faults of air handling unit. In order to provide a level of robustness with respect to modeling errors, control limits are determined by incorporating time series model uncertainty in EWMA control chart. The fault detection method proposed was tested and validated using real time data collected from real VAV air-conditioning systems involving multiple artificial faults. The results of validation show residual-based EWMA control chart with designing control limits can improve the accuracy of fault detection through eliminating the negative effects of dynamic characteristics, serial correlation, normal transient changes of system, and time series modeling errors. The robust fault detection method proposed can provide an effective tool for detecting the faults of air handling units.
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Ali, Mohamed Hassan, Abdelhamid Rabhi, Ahmed El Hajjaji, and Giuseppe M. Tina. "Real Time Fault Detection in Photovoltaic Systems." Energy Procedia 111 (March 2017): 914–23. http://dx.doi.org/10.1016/j.egypro.2017.03.254.
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Wang, Jie, and Jiwei Liu. "Fault-Tolerant Strategy for Real-Time System Based on Evolvable Hardware." Journal of Circuits, Systems and Computers 26, no. 07 (March 17, 2017): 1750111. http://dx.doi.org/10.1142/s0218126617501110.
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The evolvable hardware (EHW) is widely used in the design of fault-tolerant system. Fault-tolerant system is really a real-time system, and the recovery time is necessary in fault detection and recovery. However, when applying EHW, real-time characteristic is usually ignored. In this paper, a fault-tolerant strategy based on EHW is proposed. The recovery time, predicted by the fault tree analysis (FTA), is considered as a constraint condition. A configuration library is set up in the design phase to accelerate the repair process of the anticipated faults. An evolvable algorithm (EA) based on similarity is applied to evolve the repair circuit for the unanticipated faults. When the library reaches the upper, the target system is reconfigured by the EA-repair technology. Extensive experiments are conducted to show that our method can improve the fault-tolerance of the system while satisfying the real-time requirement on FPGA platform. In a long run system, our method can keep a higher fault recovery rate.
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El Merraoui, Khadidja, Abdellaziz Ferdjouni, and M’hamed Bounekhla. "Real time observer-based stator fault diagnosis for IM." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 1 (February 1, 2020): 210. http://dx.doi.org/10.11591/ijece.v10i1.pp210-222.
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This paper proposes a delta connected IM model that takes the Stator winding Inter-Turn Short Circuit (SITSC) fault into account. In order to detect the fault and evaluate its severity, an observer based FDI method is suggested. It allows the generation of residual using extended Kalman filter (EKF). To overcome the problem of the EKF initialization, the cyclic optimization method is applied to determine its tuning parameters. The advantage of the proposed approach is the real-time quantification of the fault severity and the quick fault detection. Using numerical simulation under both the healthy and the faulty conditions, the proposed IM model and EKF-based FDI approach are confirmed. Experimental results obtained by a real-time implementation on test-bench validate the simulated results.
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Zhang, Chuang, Xiubin Zhao, Chunlei Pang, Yong Wang, Liang Zhang, and Bo Feng. "Improved Fault Detection Method Based on Robust Estimation and Sliding Window Test for INS/GNSS Integration." Journal of Navigation 73, no. 4 (February 28, 2020): 776–96. http://dx.doi.org/10.1017/s0373463319000778.
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Real-time and accurate fault detection and isolation is very important to ensure the reliability and precision of integrated inertial navigation and global navigation satellite systems. In this paper, the detection performance of a residual chi-square method is analysed, and on this basis an improved method of fault detection is proposed. The local test based on a standardised residual is introduced to detect and identify faulty measurements directly. Differing from the traditional method, two appropriate thresholds are selected to calculate the weight factor of each measurement, and the gain matrix is adjusted adaptively to reduce the influence of the undetected faulty measurement. The sliding window test, which uses past measurements, is also added to further improve the fault detection performance for small faults when the local test based on current measurements cannot judge whether a fault has occurred or not. Several simulations are conducted to evaluate the proposed method. The results show that the improved method has better fault detection performance than the traditional detection method, especially for small faults, and can improve the reliability and precision of the navigation system effectively.
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Zhong, Lina, Jianye Liu, Rongbing Li, and Rong Wang. "Approach for Detecting Soft Faults in GPS/INS Integrated Navigation based on LS-SVM and AIME." Journal of Navigation 70, no. 3 (February 2, 2017): 561–79. http://dx.doi.org/10.1017/s037346331600076x.
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In life-critical applications, the real-time detection of faults is very important in Global Positioning System/Inertial Navigation System (GPS/INS) integrated navigation systems. A new fault detection method for soft fault detection is developed in this paper with the purpose of improving real-time performance. In general, the innovation information obtained from a Kalman filter is used for test statistic calculations in Autonomous Integrity Monitored Extrapolation (AIME). However, the innovation of the Kalman filter is degraded by error tracking and closed-loop correction effects, leading to time delays in soft fault detection. Therefore, the key issue of improving real-time performance is providing accurate innovation to AIME. In this paper, the proposed algorithm incorporates Least Squares-Support Vector Machine (LS-SVM) regression theory into AIME. Because the LS-SVM has a good regression and prediction performance, the proposed method provides replaced innovation obtained from the LS-SVM driven by real-time observation data. Based on the replaced innovation, the test statistics can follow fault amplitudes more accurately; finally, the real-time performance of soft fault detection can be improved. Theoretical analysis and physical simulations demonstrate that the proposed method can effectively improve the detection instantaneity.
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Zhang, Dapeng, Zhiling Lin, and Zhiwei Gao. "A Novel Fault Detection with Minimizing the Noise-Signal Ratio Using Reinforcement Learning." Sensors 18, no. 9 (September 13, 2018): 3087. http://dx.doi.org/10.3390/s18093087.
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In this paper, a reinforcement learning approach is proposed to detect unexpected faults, where the noise-signal ratio of the data series is minimized to achieve robustness. Based on the information of fault free data series, fault detection is promptly implemented by comparing with the model forecast and real-time process. The fault severity degrees are also discussed by measuring the distance between the healthy parameters and faulty parameters. The effectiveness of the algorithm is demonstrated by an example of a DC-motor system.
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Li, Qiuying, and Hoang Pham. "Modeling Software Fault-Detection and Fault-Correction Processes by Considering the Dependencies between Fault Amounts." Applied Sciences 11, no. 15 (July 29, 2021): 6998. http://dx.doi.org/10.3390/app11156998.
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Many NHPP software reliability growth models (SRGMs) have been proposed to assess software reliability during the past 40 years, but most of them have focused on modeling the fault detection process (FDP) in two ways: one is to ignore the fault correction process (FCP), i.e., faults are assumed to be instantaneously removed after the failure caused by the faults is detected. However, in real software development, it is not always reliable as fault removal usually needs time, i.e., the faults causing failures cannot always be removed at once and the detected failures will become more and more difficult to correct as testing progresses. Another way to model the fault correction process is to consider the time delay between the fault detection and fault correction. The time delay has been assumed to be constant and function dependent on time or random variables following some kind of distribution. In this paper, some useful approaches to the modeling of dual fault detection and correction processes are discussed. The dependencies between fault amounts of dual processes are considered instead of fault correction time-delay. A model aiming to integrate fault-detection processes and fault-correction processes, along with the incorporation of a fault introduction rate and testing coverage rate into the software reliability evaluation is proposed. The model parameters are estimated using the Least Squares Estimation (LSE) method. The descriptive and predictive performance of this proposed model and other existing NHPP SRGMs are investigated by using three real data-sets based on four criteria, respectively. The results show that the new model can be significantly effective in yielding better reliability estimation and prediction.
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Jang, C. W., J. C. Juang, and F. C. Kung. "Adaptive fault detection in real-time GPS positioning." IEE Proceedings - Radar, Sonar and Navigation 147, no. 5 (2000): 254. http://dx.doi.org/10.1049/ip-rsn:20000619.
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Costa, Bruno Sielly Jales, Plamen Parvanov Angelov, and Luiz Affonso Guedes. "Real-Time Fault Detection Using Recursive Density Estimation." Journal of Control, Automation and Electrical Systems 25, no. 4 (April 7, 2014): 428–37. http://dx.doi.org/10.1007/s40313-014-0128-4.
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Xie, Guo Cai, Yi Ding Fu, and Zhao Hui Li. "A Real-Time Fault Prewarning Approach to Generator Sets Based on Dynamic Threshold." Applied Mechanics and Materials 510 (February 2014): 248–53. http://dx.doi.org/10.4028/www.scientific.net/amm.510.248.
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Fault prewarning is important to guarantee the safe and stable operation of Generator Sets (GS). In order to generate prewarnings quickly and accurately before the failures or faults occur in GS, a real-time fault prewarning approach to GS based on dynamic threshold was put forward. This approach was consisted of five steps, that is operating condition (or abnormal event) synchronous, dynamic threshold selection, threshold analysis, fault detection and fault prewarning. The dynamic threshold (closely related to operating condition or abnormal event of GS) was the key of this approach, which can be obtained by means of expertise knowledge discovery. This approach can effectively reduce false positives and false negatives for the faults of GS, whose effectiveness is validated by the applications and practices of Gezhouba power plant.
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Yılmaz, Alper, and Gökay Bayrak. "Real-Time Disturbance Detection Using STFT Method in Microgrids." Academic Perspective Procedia 2, no. 3 (November 22, 2019): 1115–21. http://dx.doi.org/10.33793/acperpro.02.03.124.
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Power quality disturbances are the main concerns to be eliminated in microgrids and decrease the power quality and reliability of the grid. Numerous methods based on signal processing have been proposed in the literature for the detection of power quality disturbances. In this study, the proposed STFT-based method is applied to the voltage signal in real-time at the point of PCC in microgrids. By using the proposed method, it is tried to detection the sudden frequency changes and the over/under voltage events in case of fault conditions. As a result, the proposed method can detect faults in microgrids a very short time and with high accuracy within the limit values specified in international standards.
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Bella, Y., A. Oulmane, and M. Mostefai. "Industrial Bearing Fault Detection Using Time-Frequency Analysis." Engineering, Technology & Applied Science Research 8, no. 4 (August 18, 2018): 3294–99. http://dx.doi.org/10.48084/etasr.2135.
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Time-frequency fault detection techniques were applied in this study, for monitoring real life industrial bearing. For this aim, an experimental test bench was developed to emulate the bearing rotating motion and to measure the induced vibration signals. Dedicated software was used to analyze the acquired measurements in the time-frequency domain using several distributions with varying resolution. Results showed that each fault type exhibits a specific behavior in the time-frequency domain, which is exploited in the localization of the faulty component.
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Lv, Weiping, Jinshan Xie, and Jing Huang. "Singular Signal Measurement Based on Bidirectional Recursive Complex-Valued Wavelet Algorithm." Security and Communication Networks 2022 (May 14, 2022): 1–10. http://dx.doi.org/10.1155/2022/9013770.
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The normal operation fault of the power system is usually caused by a short-circuit fault. At this time, the system changes drastically from one state to another, accompanied by complex transient phenomena. Therefore, the measured signal contains a large number of transient components. How to effectively analyze such signals, extract their characteristics, and develop new protection devices has always been an important research field in power system protection technology. The protection of the power system is to achieve the purpose of correct action and elimination of faults by quickly detecting and locating faults. At present, the power signal analysis tools used in microcomputer protection include FFT, Kalman filter, and finite impulse response filter. They are efficient for the analysis of stationary signals, but have their limitations in analyzing nonstationary signals; especially it is difficult to identify nonlinear faults, such as the detection of high-impedance nonlinear short-circuit faults, which is a long-term unsolved problem in power systems. Based on wavelet transform, this paper selects complex-valued wavelet algorithm, analyzes a real-time recursive wavelet algorithm, and deduces the realization process of the algorithm in detail. The algorithm greatly reduces the computational complexity of the existing two-way recursive algorithm, can be used for real-time detection of fault signals in various fields of power system, and can be extended to realize other fast recursive algorithms of wavelet functions. Based on the sensitivity of complex-valued wavelet transform phase information to singularity, a method for real-time monitoring of power system fault mutation signals using the phase information of complex-valued wavelet fast recursion algorithm to assist amplitude information is proposed. The validity and practicability of this complex-valued wavelet and its real-time recursive algorithm for fault detection are demonstrated by an example.
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Kolios, Vasileios, Ioannis Templalexis, Ioannis Lionis, Emmanouil Antonogiannakis, and Petros Kotsiopoulos. "F100-PW-229 Engine Fault Detection Based on Real Time Data." MATEC Web of Conferences 304 (2019): 03006. http://dx.doi.org/10.1051/matecconf/201930403006.
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Gas turbine engines exhibit very high maintenance costs. Moreover, in the case of aero applications an in-flight engine incidence, shall, by all means, be avoided, a condition that drives total maintenance costs even higher. A measure in favor of balancing these costs is to monitor continuously the variation of engine performance data recorded during flight, establish methods to deduce useful information regarding the engine “health” status and, as a result, take appropriate actions to maintain a good engine operating condition. The current work presents such a method tailored on the “F100-PW-229” engine that is operated by the ellenic Air Force as the propulsion system of the “F-16 block 52M” aircraft [3]. CEDATS and MS Excel were the computational tools used for the current engine performance study. CEDATS is a software developed for the engine users. It provides basic data trend monitoring functions and engine fault warnings. It is well known that there is always space for improvement for such health monitoring tools since there are cases where engine operating faults are not captured. Within the frame of the current work, a data post – processing method on the engine performance data time series was applied using MS Excel, in order to raise early warnings of an uncaptured compressor operating fault.
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Huang, Peihao, Tao Wang, Lin Ding, Huhuang Yu, Yong Tang, and Dianle Zhou. "Comparative Analysis of Real-Time Fault Detection Methods Based on Certain Artificial Intelligent Algorithms for a Hydrogen–Oxygen Rocket Engine." Aerospace 9, no. 10 (October 7, 2022): 582. http://dx.doi.org/10.3390/aerospace9100582.
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The real-time fault detection and diagnosis algorithm of a liquid rocket engine is the basis of online reconfiguration of guidance and the control system of a launch vehicle, which is directly related to the success or failure of space mission. Based on previous related works, this paper carries out comparative experimental studies of relevant intelligent algorithm models for real-time fault detection engineering application requirements of a liquid hydrogen–oxygen rocket engine. Firstly, the working state and detection parameters’ selection of a hydrogen–oxygen engine are analyzed, and the proposed three real-time intelligent fault detection algorithm model design methods are elaborated again. Fault detection calculation and analysis are carried out through normal test data and fault test data. The comparative analysis results of real-time intelligent fault detection algorithm models is presented from three dimensions: detection time, fault detection, and stability and consistency. Finally, based on a correlation analysis, a comprehensive intelligent fault diagnosis model design framework is given to further solve the requirements of real-time fault detection and diagnosis engineering development of a liquid rocket engine, a complex piece of equipment.
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Xu, Qiwei, Hong Huang, Chuan Zhou, and Xuefeng Zhang. "Research on Real-Time Infrared Image Fault Detection of Substation High-Voltage Lead Connectors Based on Improved YOLOv3 Network." Electronics 10, no. 5 (February 25, 2021): 544. http://dx.doi.org/10.3390/electronics10050544.
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Currently, infrared fault diagnosis mainly relies on manual inspection and low detection efficiency. This paper proposes an improved YOLOv3 network for detecting the working state of substation high-voltage lead connectors. Firstly, dilated convolution is introduced into the YOLOv3 backbone network to process low-resolution element layers, so as to enhance the network’s extraction of image features, promote function propagation and reuse, and improve the network’s recognition performance of small targets. Then the fault detection model of the infrared image of the high voltage lead connector is created and the optimal infrared image test data set is obtained through multi-scale training. Finally, the performance of the improved network model is tested on the data set. The test results show that the improved YOLOv3 network model has an average detection accuracy of 84.26% for infrared image faults of high-voltage lead connectors, which is 4.58% higher than the original YOLOv3 network model. The improved YOLOv3 network model has an average detection time of 0.308 s for infrared image faults of high-voltage lead connectors, which can be used for real-time detection in substations.
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Sun, Yi Gang, Lei Wang, and Wei Xing Chen. "A Sensor of Aero-Engine Real-Time Fault Detection System Based on ARM9." Advanced Materials Research 591-593 (November 2012): 1470–74. http://dx.doi.org/10.4028/www.scientific.net/amr.591-593.1470.
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A system is designed to monitor fault of sensors for aircraft engine real-time. SCM C8051F120 is used to control sensor signal acquisition process, and after processing and storage, the data will be transferred to the data processing unit via Ethernet for analysis and detection. ARM9 embedded computer based on WinCE is used as a data processing core for the data processing unit, three layers BP neural network is used as a sensor fault detection algorithm and troubleshooting software with C++ is developed. It can handle large amounts of data and improve processing efficiency. It has a good interface as well. Compared with current systems, it has been greatly improved in real-time and accuracy. After verification, the system is accurate and strong real-time, and can monitor aircraft engine sensor faults correctly.
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Biddle, Liam, and Saber Fallah. "A Novel Fault Detection, Identification and Prediction Approach for Autonomous Vehicle Controllers Using SVM." Automotive Innovation 4, no. 3 (April 5, 2021): 301–14. http://dx.doi.org/10.1007/s42154-021-00138-0.
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AbstractFaults that develop in vehicle sensors have the potential to propagate unchecked throughout control systems if undetected. Automatic fault diagnosis and health monitoring algorithms will become necessary as automotive applications become more autonomous. The current fault diagnosis systems are not effective for complex systems such as autonomous cars where the case of simultaneous faults in different sensors is highly possible. Therefore, this paper proposes a novel fault detection, isolation and identification architecture for multi-fault in multi-sensor systems with an efficient computational burden for real-time implementation. Support Vector Machine techniques are used to detect and identify faults in sensors for autonomous vehicle control systems. In addition, to identify degrading performance in a sensor and predict the time at which a fault will occur, a novel predictive algorithm is proposed. The effectiveness and accuracy of the architecture in detecting and identifying multiple faults as well as the accuracy of the proposed predictive fault detection algorithm are verified through a MATLAB/IPG CarMaker co-simulation platform. The results present detection and identification accuracies of 94.94% and 97.01%, respectively, as well as a prediction accuracy of 75.35%.
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Khan, Sazzed Mahamud, and Abu Hena MD Shatil. "A Robust Fault Diagnosis Scheme using Deep Learning for High Voltage Transmission Line." AIUB Journal of Science and Engineering (AJSE) 21, no. 2 (November 23, 2022): 68–75. http://dx.doi.org/10.53799/ajse.v21i2.204.
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The transmission lines repeatedly face an aggregation of shunt-faults and its impact in the real time system increases the vulnerability, damage in load, and line restoration cost. Fault detection in power transmission lines have become significantly crucial due to a rapid increase in number and length. Any kind of interruption or tripping in transmission lines can result in a massive failure over a large area, which necessitates the need of effective protection. The diagnosis of faults help in detecting and classifying transients that eventually make the protection of transmission lines convenient. In this paper, we propose a deep learning-enabled technique for the detection and classification of transmission line faults. The faulty information are extracted using Discrete Wavelet Transform (DWT) and fed into the multilayer perceptron classification model. The results indicate that the proposed approach is capable of accurately classifying and detecting faults in transmission line with high precision.
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Stiphoudt, Christine van, Florian Stinner, Gerrit Bode, Alexander Kümpel, and Dirk Müller. "Fault detection and diagnosis in building energy systems: A tool chain for the automated generation of training data." Journal of Physics: Conference Series 2042, no. 1 (November 1, 2021): 012083. http://dx.doi.org/10.1088/1742-6596/2042/1/012083.
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Abstract The application of fault detection and diagnosis (FDD) algorithms in building energy management systems (BEMS) has great potential to increase the efficiency of building energy systems (BES). The usage of supervised learning algorithms requires time series depicting both nominal and component faulty behaviour for their training. In this paper, we introduce a method that automates Modelica code extension of BES models in Python with fault models to approximate real component faults. The application shows two orders of magnitude faster implementation compared to manual modelling, while no errors occur in the connections between fault and component models.
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Alwadie, Abdullah. "A Real Time Condition Monitoring System for Gears Operating under Variable Load Conditions." Indonesian Journal of Electrical Engineering and Computer Science 9, no. 2 (February 1, 2018): 493. http://dx.doi.org/10.11591/ijeecs.v9.i2.pp493-501.
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Gears are important component of the rotational power transmission system and are largely used in variable load and speed applications. The faults on the gear generate excessive vibration which leads to breakdown of the machine. Sensor based methods could diagnose gear faults but proved to be expensive and have limited applications due to heavy cost and need of access of gear box for sensor installation. The motor stator current analysis has been reported to overcome the drawbacks of the sensor based fault detection methods. However, motor stator current analysis has a limited capability for reliable detection of small gear fault signatures typically for low load conditions. This paper presents an alternative non-invasive approach based on instantaneous power analysis of the motor to reliably diagnose gear faults for variable load applications. The theoretical and experimental results indicates that the instantaneous power analysis offers three fault related harmonics and amplitude variations on these harmonics could give the indication of health status of the gear.<strong> </strong> The superiority of the proposed instantaneous power analysis technique has been confirmed through experiments performed on three operating points of the motor. The comparison of the amplitude sensitivity of the motor stator current and instantaneous power at three operating points has been performed to validate the superiority of the proposed technique.
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Hofreiter, Milan, and Gunça Garajaÿewa. "REAL-TIME FAULT DETECTION AND ISOLATION WITH SUPERVISED TRAINING." IFAC Proceedings Volumes 39, no. 13 (2006): 623–28. http://dx.doi.org/10.3182/20060829-4-cn-2909.00103.
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Hekmat, Shahrzad, and Reza Ravanmehr. "Real Time Fault Detection and Isolation: A Comparative Study." International Journal of Computer Applications 134, no. 6 (January 15, 2016): 5–12. http://dx.doi.org/10.5120/ijca2016907931.
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Zhu, Hui, Wen Yang, Shihong Li, and Aiping Pang. "An Effective Fault Detection Method for HVAC Systems Using the LSTM-SVDD Algorithm." Buildings 12, no. 2 (February 20, 2022): 246. http://dx.doi.org/10.3390/buildings12020246.
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Fault detection in heating, ventilation and air-conditioning (HVAC) systems can effectively prevent equipment damage and system energy loss, and enhance the stability and reliability of system operation. However, existing fault detection strategies have not realized high effectiveness, mainly due to the time-delay characteristics of HVAC system faults and the lack of system-fault operation data. Therefore, aiming at the time delay of system faults and the lack of actual system-fault operation data, this paper proposes a fault detection method that combines a system simulation model and an intelligent detection algorithm. The method first uses the Modelica modeling language to build a scalable simulation model of the system to obtain fault data that are not easily accessible in practice. The long short-term memory-support vector data description (LSTM-SVDD) algorithm is then applied to detect faults in real time by dynamically adjusting the fault residuals according to the absolute difference between the predicted and actual values. The experimental results show that the LSTM-SVDD method improves the average detection accuracy by 9.675% and 9.85% over the classical LSTM network and the extreme gradient boosting (XGBoost) method, respectively, under different fault levels.
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Ali Lilo, Moneer, and Maath Jasem Mahammad. "Design and implementation of wireless system for vibration fault detection using fuzzy logic." IAES International Journal of Artificial Intelligence (IJ-AI) 9, no. 3 (September 1, 2020): 545. http://dx.doi.org/10.11591/ijai.v9.i3.pp545-552.
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This paper aims at constructing the wireless system for fault detecting and monitoring by computer depending on the wireless and fuzzy logic technique. Wireless applications are utilized to identify, classify, and monitor faults in the real time to protect machines from damage .Two schemes were tested; first scheme fault collected X-Y-Z-axes mode while the second scheme collected Y-axis mode, which is utilized to protect the induction motor (IM) from vibrations fault. The vibration signals were processed in the central computer to reduce noise by signal processing stage, and then the fault was classified and monitored based on Fuzzy Logic (FL). The wireless vibration sensor was designed depending on the wireless techniques and C++ code. A fault collection, noise reduction, vibration fault classification and monitoring were implemented by MATLAB code. In the second scheme the processed real time was reduced to 60%, which is included collection, filtering, and monitoring fault level. Results showed that the system has the ability to early detect the fault if appears on the machine with time processing of 1.721s. This work will reduce the maintenance cost and provide the ability to utilize the system with harsh industrial applications to diagnose the fault in real time processing.
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Yang, Jun Gang, Jie Zhang, Jian Xiong Yang, and Ying Huang. "A Principal Component Analysis Based Fault Detection Method in Etch Process of Semiconductor Manufacturing." Key Engineering Materials 522 (August 2012): 793–98. http://dx.doi.org/10.4028/www.scientific.net/kem.522.793.
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A Principal Component Analysis based Fault Detection method is proposed here to detect faults in etch process of semiconductor manufacturing. The main idea of this method is to calculate the loading vector and build the fault detection model according to training data. Using this model, the main information of fault data can be obtained immediately and easily. Also the principal component subspace and residual subspace can be constructed. Then, faults are detected by calculating Squared Prediction Error. Finally, an industrial example of Lam 9600 TCP metal etcher at Texas Instruments is used to demonstrate the performance of the proposed PCA-based method in fault detection, and the results show that it has such advantages as simple algorithm and low time cost, thus especially adapts to the real time fault detection of semiconductor manufacturing.
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Fan, Xianfeng, and Ming J. Zuo. "Gearbox Fault Detection Using Hilbert and TT-Transform." Key Engineering Materials 293-294 (September 2005): 79–86. http://dx.doi.org/10.4028/www.scientific.net/kem.293-294.79.
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Machine vibration signal has been used in fault detection and diagnosis. Modulation and non-stationarity existing in the signal generated by a faulty gearbox present challenges to effective fault detection. Hilbert transform has the ability to address the modulation issue. This paper outlines a novel fault detection method called Hilbert & TT-transform (HTT-transform) which combines Hilbert transform and TT-transform obtained from the inverse Fourier transform of the S-transform. The principle of the proposed method is to analyze the modulating signal created by a faulty gear using a time-time representation. The method has the advantage of providing a new way of localizing the time features of the modulating signal around a particular point on the time axis through scaled windows. It is verified with simulated signals and real gearbox vibration signals. The results obtained by CWT, S-transform, TT- transform, and HTT-transform are compared. They show that utilizing the proposed method can improve the effectiveness of gearbox fault detection.
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El Mrabet, Zakaria, Niroop Sugunaraj, Prakash Ranganathan, and Shrirang Abhyankar. "Random Forest Regressor-Based Approach for Detecting Fault Location and Duration in Power Systems." Sensors 22, no. 2 (January 8, 2022): 458. http://dx.doi.org/10.3390/s22020458.
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Power system failures or outages due to short-circuits or “faults” can result in long service interruptions leading to significant socio-economic consequences. It is critical for electrical utilities to quickly ascertain fault characteristics, including location, type, and duration, to reduce the service time of an outage. Existing fault detection mechanisms (relays and digital fault recorders) are slow to communicate the fault characteristics upstream to the substations and control centers for action to be taken quickly. Fortunately, due to availability of high-resolution phasor measurement units (PMUs), more event-driven solutions can be captured in real time. In this paper, we propose a data-driven approach for determining fault characteristics using samples of fault trajectories. A random forest regressor (RFR)-based model is used to detect real-time fault location and its duration simultaneously. This model is based on combining multiple uncorrelated trees with state-of-the-art boosting and aggregating techniques in order to obtain robust generalizations and greater accuracy without overfitting or underfitting. Four cases were studied to evaluate the performance of RFR: 1. Detecting fault location (case 1), 2. Predicting fault duration (case 2), 3. Handling missing data (case 3), and 4. Identifying fault location and length in a real-time streaming environment (case 4). A comparative analysis was conducted between the RFR algorithm and state-of-the-art models, including deep neural network, Hoeffding tree, neural network, support vector machine, decision tree, naive Bayesian, and K-nearest neighborhood. Experiments revealed that RFR consistently outperformed the other models in detection accuracy, prediction error, and processing time.
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Barker, Collin, Sam Cipkar, Tyler Lavigne, Cameron Watson, and Maher Azzouz. "Real-Time Nuisance Fault Detection in Photovoltaic Generation Systems Using a Fine Tree Classifier." Sustainability 13, no. 4 (February 19, 2021): 2235. http://dx.doi.org/10.3390/su13042235.
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Nuisance faults are caused by weather events, which result in solar farms being disconnected from the electricity grid. This results in long stretches of downtime for troubleshooting as data are mined manually for possible fault causes, and consequently, cost thousands of dollars in lost revenue and maintenance. This paper proposes a novel fault detection technique to identify nuisance faults in solar farms. To initialize the design process, a weather model and solar farm model are designed to generate both training and testing data. Through an iterative design process, a fine tree model with a classification accuracy of 96.7% is developed. The proposed model is successfully implemented and tested in real-time through a server and web interface. The testbed is capable of streaming in data from a separate source, which emulates a supervisory control and data acquisition (SCADA) or weather station, then classifies the data in real-time and displays the output on another computer (which imitates an operator control room).
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Cheng, Qi, Ping Chen, Rui Sun, Junhui Wang, Yi Mao, and Washington Yotto Ochieng. "A New Faulty GNSS Measurement Detection and Exclusion Algorithm for Urban Vehicle Positioning." Remote Sensing 13, no. 11 (May 28, 2021): 2117. http://dx.doi.org/10.3390/rs13112117.
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The performance requirements for Global Navigation Satellite Systems (GNSS) are becoming more demanding as the range of mission-critical vehicular applications, including the Unmanned Aerial Vehicle (UAV) and ground vehicle-based applications, increases. However, the accuracy and reliability of GNSS in some environments, such as in urban areas, are often affected by non-line-of-sight (NLOS) signals and multipath effects. It is therefore essential to develop an effective fault detection scheme that can be applied to GNSS observations so as to ensure that the vehicle positioning can be calculated with a high accuracy. In this paper, we propose an online dataset based faulty GNSS measurement detection and exclusion algorithm for vehicle positioning that takes account of the NLOS/multipath affected scenarios. The proposed algorithm enables a real-time online dataset based fault detection and exclusion scheme, which makes it possible to detect multiple faults in different satellites simultaneously and accurately, thereby allowing real-time quality control of GNSS measurements in dynamic urban positioning applications. The algorithm was tested with simulated/artificial step errors in various scenarios in the measured pseudoranges from a dataset acquired from a UAV in an open area. Furthermore, a real-world test was also conducted with a ground-vehicle driving in a dense urban environment to validate the practical efficiency of the proposed algorithm. The UAV based simulation exhibits a fault detection rate of 100% for both single and multi-satellite fault scenarios, with the horizontal positioning accuracy improved to about 1 metre from tens of metres after fault detection and exclusion. The ground vehicle-based real test shows an overall improvement of 26.1% in 3D positioning accuracy in an urban area compared to the traditional least square method.
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D Patil, Dipti, Bindu S, and Sushil Thale. "A Novel Method for Real Time Protection of DC Microgrid Using Cumulative Summation and Wavelet Transform." International journal of electrical and computer engineering systems 13, no. 4 (June 2, 2022): 311–21. http://dx.doi.org/10.32985/ijeces.13.4.7.
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DC microgrid is a compact framework comprising interconnected nearby sources and loads. The renewable energy source used in DC microgrids being intermittent leads to the change in the power availability as well as the fault current levels. In such situations, detecting and clearing the faults is very important to protect the DC microgrid without compromising on fault clearing time and interruption of the load. This paper proposes a hybrid Cumulative Sum (CumSum) and Wavelet transform-based approach to detect the fault. The CumSum value raises the amplitude by averaging the fault current. Wavelet transforms obtain important fault current features by decomposing the current signal. The hybrid method of CumSum and Wavelet analysis proposed here enables the detection of the fault and differentiates the fault condition from sudden load variation. Additionally, it helps to recognize the location of the fault by the wavelet energy difference. The proposed scheme is tested with a developed ring-type low voltage DC (LVDC) microgrid hardware model under various fault conditions. The scheme is implemented using TMS320F28069 digital signal processors (DSP) of Texas Instruments. The hardware results are validated using MATLAB simulation. The proposed method performance is also compared with the existing methods used for DC microgrid protection. The outcome shows that the proposed method has a high accuracy of 98.72%, selectivity of 96.08%, and reliability of 99.01%. The execution time required by the proposed method is also less.
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Saadoon, Muntadher, Siti Hafizah Ab Hamid, Hazrina Sofian, Hamza Altarturi, Nur Nasuha, Zati Hakim Azizul, Asmiza Abdul Sani, and Adeleh Asemi. "Experimental Analysis in Hadoop MapReduce: A Closer Look at Fault Detection and Recovery Techniques." Sensors 21, no. 11 (May 31, 2021): 3799. http://dx.doi.org/10.3390/s21113799.
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Hadoop MapReduce reactively detects and recovers faults after they occur based on the static heartbeat detection and the re-execution from scratch techniques. However, these techniques lead to excessive response time penalties and inefficient resource consumption during detection and recovery. Existing fault-tolerance solutions intend to mitigate the limitations without considering critical conditions such as fail-slow faults, the impact of faults at various infrastructure levels and the relationship between the detection and recovery stages. This paper analyses the response time under two main conditions: fail-stop and fail-slow, when they manifest with node, service, and the task at runtime. In addition, we focus on the relationship between the time for detecting and recovering faults. The experimental analysis is conducted on a real Hadoop cluster comprising MapReduce, YARN and HDFS frameworks. Our analysis shows that the recovery of a single fault leads to an average of 67.6% response time penalty. Even though the detection and recovery times are well-turned, data locality and resource availability must also be considered to obtain the optimum tolerance time and the lowest penalties.
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Cheng, Kefei, Jiashun Xu, Liang Zhang, ChengXin Xu, and Xiaotong Cui. "Fault Detection Method for Wi-Fi-Based Smart Home Devices." Wireless Communications and Mobile Computing 2022 (November 2, 2022): 1–12. http://dx.doi.org/10.1155/2022/4328307.
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At present, the dynamic nature and unstable network connections in the deployment environments of Wi-Fi-based smart home devices make them susceptible to component damage, crashes, network disconnections, etc. To solve these problems, researchers have used various fault detection methods, such as alarming when monitored fault parameters exceed the preset values, model-based mathematical methods, device signal processing-based methods, and artificial intelligence-based methods. However, these methods require large numbers of fault parameters, the model are complex, and their fault detection accuracy is relatively poor. To more quickly and accurately detect faults in smart home devices and ensure the continuity of people’s daily work and lives, this paper analyzes both the Wi-Fi traffic characteristics of smart home devices and the complexity and difficulty of traditional fault detection methods and proposes a fault detection method based on TDD (Throughput and Delay Distribution). This method obtains throughput and data packet delay distribution by capturing Wi-Fi communication and sending test data. By dividing the throughput into heartbeat data and command information, we can calculate the real-time throughput and further calculate the similarity between the real-time throughput and the throughput in database. Also, the resulting delay distribution is compared with the probability distribution of delay in the database. When the throughput values are sufficiently similar and the delays are all in the normal range, the smart home secure devices are functioning properly. The experimental results show that the proposed TDD method can detect faults in household devices in real time and that it achieves high recall and good detection accuracy in Wi-Fi communication environment.
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Pandya, Keivalya, and Devesh Tarasia. "Bare PCB (Printed Circuit Board) Fault Detection in Real-time Using YOLOv5." International Journal of Computer Science and Mobile Computing 11, no. 12 (December 30, 2022): 91–98. http://dx.doi.org/10.47760/ijcsmc.2022.v11i12.009.
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With the continuous development of object detection technology, the YOLO series of algorithms with high precision and speed has been used in various fault detection tasks. This paper aims to establish a real-time quality control framework for PCB fault detection that can be adopted by industries. The development of more automized testing method by using YOLOv5. Detection of imperfections despite misalignment and incorrect orientation is required in the industry. Decreases quality check time and human examiner cost for quality control and auditing the product (as it might be slow and error-prone). Computer Camera is used of real-time fault detection in this prototype, however any digital camera integration would be possible and might improve the performance. Ultimately, increased revenue generation followed by better quality production and supply that meets the market demand.
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Eren, Levent. "Bearing Fault Detection by One-Dimensional Convolutional Neural Networks." Mathematical Problems in Engineering 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/8617315.
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Bearing faults are the biggest single source of motor failures. Artificial Neural Networks (ANNs) and other decision support systems are widely used for early detection of bearing faults. The typical decision support systems require feature extraction and classification as two distinct phases. Extracting fixed features each time may require a significant computational cost preventing their use in real-time applications. Furthermore, the selected features for the classification phase may not represent the most optimal choice. In this paper, the use of 1D Convolutional Neural Networks (CNNs) is proposed for a fast and accurate bearing fault detection system. The feature extraction and classification phases of the bearing fault detection are combined into a single learning body with the implementation of 1D CNN. The raw vibration data (signal) is fed into the proposed system as input eliminating the need for running a separate feature extraction algorithm each time vibration data is analyzed for classification. Implementation of 1D CNNs results in more efficient systems in terms of computational complexity. The classification performance of the proposed system with real bearing data demonstrates that the reduced computational complexity is achieved without a compromise in fault detection accuracy.
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Zhang, Zhujun, Gaoliang Peng, Weitian Wang, and Yi Chen. "Real-Time Human Fault Detection in Assembly Tasks, Based on Human Action Prediction Using a Spatio-Temporal Learning Model." Sustainability 14, no. 15 (July 23, 2022): 9027. http://dx.doi.org/10.3390/su14159027.
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Human fault detection plays an important role in the industrial assembly process. In the current unstructured industrial workspace, the definition of human faults may vary over a long sequence, and this vagueness introduces multiple issues when using traditional detection methods. A method which could learn the correct action sequence from humans, as well as detect the fault actions based on prior knowledge, would be more appropriate and effective. To this end, we propose an end-to-end learning model to predict future human actions and extend it to detect human faults. We combined the auto-encoder framework and recurrent neural network (RNN) method to predict and generate intuitive future human motions. The convolutional long short-term memory (ConvLSTM) layer was applied to extract spatio-temporal features from video sequences. A score function was implemented to indicate the difference between the correct human action sequence and the fault actions. The proposed model was evaluated on a model vehicle seat assembly task. The experimental results showed that the model could effectively capture the necessary historical details to predict future human actions. The results of several fault scenarios demonstrated that the model could detect the faults in human actions based on corresponding future behaviors through prediction features.
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Lipták, Róbert, and István Bodnár. "Simulation of fault detection in photovoltaic arrays." Analecta Technica Szegedinensia 15, no. 2 (December 15, 2021): 31–40. http://dx.doi.org/10.14232/analecta.2021.2.31-40.
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In solar systems, faults in the module and inverter occur in proportion to increased operating time. The identification of fault types and their effects is important information not only for manufacturers but also for investors, solar operators and researchers. Monitoring and diagnosing the condition of photovoltaic (PV) systems is becoming essential to maximize electric power generation, increase the reliability and lifetime of PV power plants. Any faults in the PV modules cause negative economic and safety impacts, reducing the performance of the system and making unwanted electric connections that can be dangerous for the user. In this paper have been classified all possible faults that happen in the PV system, and is presented to detect common PV array faults, such as open-circuit fault, line-to-line fault, ground fault, shading condition, degradation fault and bypass diode fault. In this studies examines the equivalent circuits of PV arrays with different topological configurations and fault conditions to evaluate the effects of these faults on the performance of a solar system, taking into account the influence of temperature and solar radiation. This work presents the validation of a simulated solar network by measuring the output curves of a low-power photovoltaic array system under real outdoor conditions. This method can be useful in future solar systems.
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Al-Zuriqat, Thamer, Carlos Chillón Geck, Kosmas Dragos, and Kay Smarsly. "Adaptive Fault Diagnosis for Simultaneous Sensor Faults in Structural Health Monitoring Systems." Infrastructures 8, no. 3 (February 22, 2023): 39. http://dx.doi.org/10.3390/infrastructures8030039.
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Structural health monitoring (SHM) is a non-destructive testing method that supports the condition assessment and lifetime estimation of civil infrastructure. Sensor faults may result in the loss of valuable data and erroneous structural condition assessments and lifetime estimations, in the worst case with structural damage remaining undetected. As a result, the concepts of fault diagnosis (FD) have been increasingly adopted by the SHM community. However, most FD concepts for SHM consider only single-fault occurrence, which may oversimplify actual fault occurrences in real-world SHM systems. This paper presents an adaptive FD approach for SHM systems that addresses simultaneous faults occurring in multiple sensors. The adaptive FD approach encompasses fault detection, isolation, and accommodation, and it builds upon analytical redundancy, which uses correlated data from multiple sensors of an SHM system. Specifically, faults are detected using the predictive capabilities of artificial neural network (ANN) models that leverage correlations within sensor data. Upon defining time instances of fault occurrences in the sensor data, faults are isolated by analyzing the moving average of individual sensor data around the time instances. For fault accommodation, the ANN models are adapted by removing faulty sensors and by using sensor data prior to the occurrence of faults to produce virtual outputs that substitute the faulty sensor data. The proposed adaptive FD approach is validated via two tests using sensor data recorded by an SHM system installed on a railway bridge. The results demonstrate that the proposed approach is capable of ensuring the accuracy, reliability, and performance of real-world SHM systems, in which faults in multiple sensors occur simultaneously.
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Wang, Hai Tao, You Ming Chen, Cary W. H. Chan, and Jian Ying Qin. "A Model-Based Online Fault Detection Method for Air Handling Units of Real Office Buildings." Applied Mechanics and Materials 90-93 (September 2011): 3061–67. http://dx.doi.org/10.4028/www.scientific.net/amm.90-93.3061.
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The increasing performance demands and the growing complexity of heating, ventilation and air conditioning (HVAC) systems have created a need for automated fault detection and diagnosis (FDD) tools. Cost-effective fault detection and diagnosis method is critical to develop FDD tools. To this end, this paper presents a model-based online fault detection method for air handling units (AHU) of real office buildings. The model parameters are periodically adjusted by a genetic algorithm-based optimization method to reduce the residual between measured and predicted data, so high modeling accuracy is assured. If the residual between measured and estimated performance data exceeds preset thresholds, it means the occurrence of faults or abnormalities in the air handling unit system. In addition, an online adaptive scheme is developed to estimate and update the thresholds, which vary with system operating conditions. The model-based fault detection method needs no additional instrumentation in implementation and can be easily integrated with existing energy management and control systems (EMCS). The fault detection method was tested and validated using in real time data collected from a real office building.
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Al-Raheem, Khalid, Asok Roy, K. Ramachandran, David Harrison, and Steven Grainger. "The Exploitation of Wavelet De-Noising To Detect Bearing Faults." Journal of Konbin 3, no. 1 (January 1, 2007): 7–16. http://dx.doi.org/10.2478/v10040-008-0001-2.
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The Exploitation of Wavelet De-Noising To Detect Bearing Faults Failure diagnosis is an important component of the Condition Based Maintenance (CBM) activities for most engineering systems. Rolling element bearings are the most common cause of rotating machinery failure. The existence of the amplitude modulation and noises in the faulty bearing vibration signal present challenges to effective fault detection method. The wavelet transform has been widely used in signal de-noising due to its extraordinary time-frequency representation capability. In this paper, we proposed new approach for bearing fault detection based on the autocorrelation of wavelet de-noised vibration signal through a wavelet base function derived from the bearing impulse response. To improve the fault detection process the wavelet parameters (damping factor and center frequency) are optimized using maximization kurtosis criteria to produce wavelet base function with high similarity with the impulses generated by bearing defects, that leads to increase the magnitude of the wavelet coefficients related to the fault impulses and enhance the fault detection process. The results show the effectiveness of the proposed technique to reveal the bearing fault impulses and its periodicity for both simulated and real rolling bearing vibration signals.
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Baek, Sujeong. "System integration for predictive process adjustment and cloud computing-based real-time condition monitoring of vibration sensor signals in automated storage and retrieval systems." International Journal of Advanced Manufacturing Technology 113, no. 3-4 (January 29, 2021): 955–66. http://dx.doi.org/10.1007/s00170-021-06652-z.
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AbstractAs automation and digitalization are being increasingly implemented in industrial applications, manufacturing systems comprising several functions are becoming more complex. Consequently, fault analysis (e.g., fault detection, diagnosis, and prediction) has attracted increased research attention. Investigations involving fault analysis are usually performed using real-time, online, or automated techniques for fault detection or alarming. Conversely, recovery of faulty states to their healthy forms is usually performed manually under offline conditions. However, the development of intelligent systems requires that appropriate feedback be provided automatically, to facilitate faulty-state recovery without the need for manual operator intervention and/or decision-making. To this end, this paper proposes a system integration technique for predictive process adjustment that determines appropriate recovery actions and performs them automatically by analyzing relevant sensor signals pertaining to the current situation of a manufacturing unit via cloud computing and machine learning. The proposed system corresponds to an automated predictive process adjustment module of an automated storage and retrieval system (ASRS). The said integrated module collects and analyzes the temperature and vibration signals of a product transporter using an internet-of-things-based programmable logic controller and cloud computing to identify the current states of the ASRS system. Upon detection of faulty states, the control program identifies corresponding process control variables and controls them to recover the system to its previous no-fault state. The proposed system will facilitate automatic prognostics and health management in complex manufacturing systems by providing automatic fault diagnosis and predictive recovery feedback.
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Li, Teng, Zhijie Jiao, Lina Wang, and Yong Mu. "A Method of DC Arc Detection in All-Electric Aircraft." Energies 13, no. 16 (August 13, 2020): 4190. http://dx.doi.org/10.3390/en13164190.
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Arc faults in an aircraft’s power distribution system (PDS) often leads to cable and equipment damage, which seriously threatens the personal safety of the passengers and pilots. An accurate and real-time arc fault detection method is needed for the Solid-State Power Controller (SSPC), which is a key protection equipment in a PDS. In this paper, a new arc detection method is proposed based on the improved LeNet5 Convolutional Neural Network (CNN) model after a Time–Frequency Analysis (TFA) of the DC currents was obtained, which makes the arc detection more real-time. The CNN is proposed to detect the DC arc fault for its advantage in recognizing more time–frequency joint details in the signals; the new structure also combines the adaptive and multidimensional advantages of the TFA and image intelligent recognition. It is confirmed by experimental data that the combined TFA–CNN can distinguish arc faults accurately when the whole training database has been repeatedly trained 3 to 5 times. For the TFA, two kinds of methods were compared, the Short-Time Fourier Transform (STFT) and Discrete Wavelet Transform (DWT). The results show that DWT is more suitable for DC arc fault detection. The experimental results demonstrated the effectiveness of the proposed method.
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Shadi, Mohammad Reza, Hamid Mirshekali, Rahman Dashti, Mohammad-Taghi Ameli, and Hamid Reza Shaker. "A Parameter-Free Approach for Fault Section Detection on Distribution Networks Employing Gated Recurrent Unit." Energies 14, no. 19 (October 5, 2021): 6361. http://dx.doi.org/10.3390/en14196361.
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Faults in distribution networks can result in severe transients, equipment failure, and power outages. The quick and accurate detection of the faulty section enables the operator to avoid prolonged power outages and economic losses by quickly retrieving the network. However, the occurrence of diverse fault types with various resistances and locations and the highly non-linear nature of distribution networks make fault section detection challenging for numerous conventional techniques. This study presents a cutting-edge deep learning-based algorithm to distinguish fault sections in distribution networks to address these issues. The proposed gated recurrent unit model utilizes only two samples of the angle between the voltage and current on either side of the feeders, which record by smart feeder meters, to detect faulty sections in real time. When a network fault occurs, the protection relays trigger the trip command for the breakers. Immediately, the angle data are obtained from all smart feeder meters of the network, which comprises a pre-fault sample and a post-fault sample. The data are then employed as an input to the pre-trained gated recurrent unit model to determine the faulted line. The performance of this novel algorithm was validated through simulations of various fault types in the IEEE-33 bus system. The model recognizes the faulty section with competitive performance in terms of accuracy.
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Ribolzi, S., J. Mercklé, J. Gresser, and P. E. Exbrayat. "Real-Time Fault Detection on Textiles Using Opto-electronic Processing." Textile Research Journal 63, no. 2 (February 1993): 61–71. http://dx.doi.org/10.1177/004051759306300201.
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