Zeitschriftenartikel: „Multi-Fault“

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Levitin, Gregory, und Suprasad V. Amari. „Multi-state systems with multi-fault coverage“. Reliability Engineering & System Safety 93, Nr. 11 (November 2008): 1730–39. http://dx.doi.org/10.1016/j.ress.2007.12.004.

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Zhang, Zhou Suo, Minghui Shen, Wenzhi Lv und Zheng Jia He. „Multi-Fault Classifier Based on Support Vector Machine and Its Application“. Key Engineering Materials 293-294 (September 2005): 483–92. http://dx.doi.org/10.4028/www.scientific.net/kem.293-294.483.

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Aiming at problem on limiting development of machinery fault intelligent diagnosis due to needing many fault data samples, this paper improves a multi-classification algorithm of support vector machine, and a multi-fault classifier based on the algorithm is constructed. Training the multi-fault classifier only needs a small quantity of fault data samples in time domain, and does not need signal preprocessing of extracting signal features. The multi-fault classifier has been applied to fault diagnosis of steam turbine generator, and the results show that it has such simple algorithm, online fault classification and excellent capability of fault classification as advantages.

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Jiménez, Laura, und C. Verde. „Multi-Fault Discrimination with Fault Model and Periodic Residual“. IFAC Proceedings Volumes 45, Nr. 20 (Januar 2012): 49–54. http://dx.doi.org/10.3182/20120829-3-mx-2028.00087.

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4

Yan, Hehua, Jinbiao Tan, Yixiong Luo, Shiyong Wang und Jiafu Wan. „Multi-Condition Intelligent Fault Diagnosis Based on Tree-Structured Labels and Hierarchical Multi-Granularity Diagnostic Network“. Machines 12, Nr. 12 (06.12.2024): 891. https://doi.org/10.3390/machines12120891.

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The aim of this study is to improve the cross-condition domain adaptability of bearing fault diagnosis models and their diagnostic performance under previously unknown conditions. Thus, this paper proposes a multi-condition adaptive bearing fault diagnosis method based on multi-granularity data annotation. A tree-structured labeling scheme is introduced to allow for multi-granularity fault annotation. A hierarchical multi-granularity diagnostic network is designed to automatically learn multi-level fault information from condition data using feature extractors of varying granularity, allowing for the extraction of shared fault information across conditions. Additionally, a multi-granularity fault loss function is developed to help the deep network learn tree-structured labels, improving intra-class compactness and reducing hierarchical similarity between classes. Two experimental cases demonstrate that the proposed method exhibits robust cross-condition domain adaptability and performs better in unseen conditions than state-of-the-art methods.

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Wen, Weigang, Jingqi Qin, Xiangru Xu, Kaifu Mi und Meng Zhou. „A Model-Driven Approach to Extract Multi-Source Fault Features of a Screw Pump“. Processes 12, Nr. 11 (17.11.2024): 2571. http://dx.doi.org/10.3390/pr12112571.

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Screw pumps’ faulty working conditions affect the stability of oil production. At project sites, different sensors are used simultaneously to collect multi-dimensional signals; the data fault labels and location are not clear, and how to comprehensively use multi-source information in effective fault feature extraction has become an urgent issue. Existing diagnostic methods use a single signal or part of a signal and do not fully utilize the acquired signal, which makes it difficult to achieve the required accuracy of diagnostic results. This paper focuses on the model-driven approach to extract multi-source fault features of screw pumps. Firstly, it constructs a fault data model (FDM) by analyzing the fault mechanism of the screw pump. Secondly, it uses the FDM to select an effective data set. Thirdly, it constructs a multi-dimensional fault feature extraction model (MDFEM) to extract featured signal features and data features, for which we also comprehensively used multi-source signals in effective fault feature extraction, while other traditional methods only use one or two signals. Finally, after feature selection, unsupervised fault diagnosis was achieved by using the k-means method. After experimental verification, the method can comprehensively use multi-source information to construct an effective data set and extract multi-dimensional, effective fault features for screw pump fault diagnosis.

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Meng, Hongbing. „Soft Fault Detection Algorithms for Multi-Parallel Data Streams Under the Cloud Computing“. Journal of Advanced Computational Intelligence and Intelligent Informatics 22, Nr. 7 (20.11.2018): 1114–19. http://dx.doi.org/10.20965/jaciii.2018.p1114.

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In the fault detection of multi-parallel data streams, the error probability of traditional methods is large, which cannot effectively meet the soft fault detection for multi-parallel data stream, causing the problem of low detection efficiency. A soft fault detection algorithm based on adaptive multi-parallel data stream is proposed. The soft fault feature in the data stream is extracted, and the adaptive soft fault detection algorithm is used to detect the fault of the multi-parallel data stream, which can overcome the disadvantages of traditional methods, effectively improve the efficiency, safety and the accuracy. Experimental results showed that the proposed method can effectively improve the efficiency of fault detection.

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Li-jia, LIU, HU Jian-wang und SUN Hui-xian. „Fault Diagnosis Reasoning Algorithm Based on Multi-signal Model“. MATEC Web of Conferences 173 (2018): 03022. http://dx.doi.org/10.1051/matecconf/201817303022.

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The multi-signal model is modeled in the fault space and combines the structural model and the dependent model of the system. The modeling work is easy to implement in different layers and is very suitable for fault modeling and fault diagnosis of the Command and control system. After the model is established, how to meet the requirements of fault coverage and fault isolation rate, fault diagnosis reasoning algorithm is particularly important. The system dependency matrix is obtained by establishing a multi-signal model. Based on this, the fault diagnosis reasoning algorithm is studied. The fault diagnosis of Apollo spacecraft before launch is taken as an example to verify the effectiveness of fault diagnosis inference algorithm based on multi-signal model.

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Wang, Yang, Rui-Qian Sun und Lin-Feng Gou. „Two-Stage Hyperelliptic Kalman Filter-Based Hybrid Fault Observer for Aeroengine Actuator under Multi-Source Uncertainty“. Aerospace 11, Nr. 9 (08.09.2024): 736. http://dx.doi.org/10.3390/aerospace11090736.

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An aeroengine faces multi-source uncertainty consisting of aeroengine epistemic uncertainty and the control system stochastic uncertainty during operation. This paper investigates actuator fault estimation under multi-source uncertainty to enhance the fault diagnosis capability of aero-engine control systems in complex environments. With the polynomial chaos expansion-based discrete stochastic model quantification, the optimal filter under multi-source uncertainty, the Hyperelliptic Kalman Filter, is proposed. Meanwhile, by treating actuator fault as unknown input, the Two-stage Hyperelliptic Kalman Filter (TSHeKF) is also proposed to achieve optimal fault estimation under multi-source uncertainty. However, considering that the biases of the model are often fixed for the individual, the TSHeKF-based fault estimation is robust and leads to inevitable conservativeness. By adding the additional estimation of the unknown deviation in state function caused by probabilistic system parameters, the hybrid fault observer (HFO) is proposed based on the TSHeKF and realizes conservativeness-reduced estimation for actuator fault under multi-source uncertainty. Numerical simulations show the effectiveness and optimality of the proposed HFO in state estimation, output prediction, and fault estimation for both single and multi-fault modes, when considering multi-source uncertainty. Furthermore, Monte Carlo experiments have demonstrated that the HFO-based optimal fault estimation is less conservative and more accurate than the Two-stage Kalman Filter and TSHeKF, providing better safety and more reliable aeroengine operation assurance.

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Yan, Wen, Haiyan Tu, Peng Qin und Tao Zhao. „Interval Type-II Fuzzy Fault-Tolerant Control for Constrained Uncertain 2-DOF Robotic Multi-Agent Systems with Active Fault Detection“. Sensors 23, Nr. 10 (17.05.2023): 4836. http://dx.doi.org/10.3390/s23104836.

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This study proposed a novel adaptive interval Type-II fuzzy fault-tolerant control for constrained uncertain 2-DOF robotic multi-agent systems with an active fault-detection algorithm. This control method can realize the predefined-accuracy stability of multi-agent systems under input saturation constraint, complex actuator failure and high-order uncertainties. Firstly, a novel active fault-detection algorithm based on pulse-wave function was proposed to detect the failure time of multi-agent systems. To the best of our knowledge, this was the first time that an active fault-detection strategy had been used in multi-agent systems. Then, a switching strategy based on active fault detection was presented to design the active fault-tolerant control algorithm of the multi-agent system. In the end, based on the interval type-II fuzzy approximated system, a novel adaptive fuzzy fault-tolerant controller was proposed for multi-agent systems to deal with system uncertainties and redundant control inputs. Compared with other relevant fault-detection and fault-tolerant control methods, the proposed method can achieve predefinition of stable accuracy with smoother control input. The theoretical result was verified by simulation.

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Wang, Dazhi, Yi Ning und Cuiling Zhang. „An Effective Ground Fault Location Scheme Using Unsynchronized Data for Multi-Terminal Lines“. Energies 11, Nr. 11 (30.10.2018): 2957. http://dx.doi.org/10.3390/en11112957.

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Traveling-wave-based methods perform poorly for the fault location of multi-terminal lines as a result of the limitation introduced by being a highly branched structure. The requirement for multi-terminal time synchronization is also a drawback and needs to be improved. In this paper, an effective fault location method for use on multi-terminal lines is proposed, and it does not require the data from each terminal to be synchronized. The method is based on the arrival time differences in the ground and aerial mode waves detected at each terminal. First, fault section identification rules for a three-terminal line are proposed. Then, a multi-terminal topological structure in this paper will be deemed as one consisting of multiple three-terminal lines. Thus, a whole scheme to identify any fault section in a multi-terminal line is presented. Consequently, the fault distance is calculated using the fault distance ratios in the corresponding fault section. The advantage of the proposed scheme is that complete coverage of multi-terminal lines fault location can still be achieved once some synchronized devices are out of operation. To evaluate the performance of the proposed method, many fault cases under different conditions are implemented. The simulation results show that the proposed method can identify the fault section correctly and locate the fault more accurately and reliably than existing methods.

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Pan, Qiang, und Chao Yang. „Fault Diagnosis of Analog Circuit Based on Multi-Test Points and Multi-Feature Information“. Applied Mechanics and Materials 313-314 (März 2013): 277–80. http://dx.doi.org/10.4028/www.scientific.net/amm.313-314.277.

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In the process of use BP neural network to fault diagnosis of analog circuits, the network input which represents fault signature was very important. A given new method which base on multi-points and multi-feature information is taken to construct the original sample set. With this method to construct the original fault signature set, then as the input of BP neural network and train the network. Simulation results show that, the network train with sample set which constructed by this method use in fault diagnosis of analog circuits is better accuracy than traditional methods. Proved the feasibility of this method in fault diagnosis of analog circuits, and offer a new method for fault diagnosis of analog circuits.

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Liu, Xiao Hua, und Song Qing Li. „The Research of Intelligent Fault Diagnosis Model on Multi-Information Fusion“. Applied Mechanics and Materials 40-41 (November 2010): 637–42. http://dx.doi.org/10.4028/www.scientific.net/amm.40-41.637.

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From the intelligent fault diagnosis system requirements, this article analyzes the relationship between the fault diagnosis and the multi-information fusion basing on the summing up the multi-sensor information fusion technology, and studies the hierarchical structure of multi-sensor information fusion system and the content of integration, and establishes an intelligent fault diagnosis model with the multi-information fusion, which provides strong support for large-scale equipments, system monitoring and fault diagnosis in production process.

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Zhang, Ke, Tianhao Gao und Huaitao Shi. „Bearing fault diagnosis method based on multi-source heterogeneous information fusion“. Measurement Science and Technology 33, Nr. 7 (31.03.2022): 075901. http://dx.doi.org/10.1088/1361-6501/ac5deb.

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Abstract Bearing fault diagnosis is a critical component of the mechanical equipment monitoring system. In the complex and harsh environment in which bearings operate, the fault diagnosis approach of multi-source information fusion can extract fault features more stably and extensively than the traditional single-source fault diagnosis method. However, most existing multi-source fusion methods are in infancy, and there are a number of pressing issues to address, such as subjective elements having a significant impact, excessive data redundancy, fuzzy multi-source signal fusion strategy, and insufficient accuracy. As a result, a new multi-source fusion fault diagnosis method is proposed in this paper. First, the residual pyramid algorithm is utilized to fuse the acoustic and vibration signals of multiple spatial positions respectively and then form two fused acoustic and vibration signals. Second, two improved 2D-CNN are used to extract the fault features contained in the above two signals separately to form a multi-source fault feature set. Third, an AdaBoost algorithm with a dynamic deletion mechanism is designed to fuse multi-source fault feature sets and produce the fault diagnosis findings. Finally, six different experimental data sets are used to test the performance of the model. The results reveal that the model has better generalization, higher and more stable fault diagnostic accuracy, and stronger anti-interference capacity.

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Wang, Ling, und A. D. Hope. „FAULT DIAGNOSIS: Bearing fault diagnosis using multi-layer neural networks“. Insight - Non-Destructive Testing and Condition Monitoring 46, Nr. 8 (August 2004): 451–55. http://dx.doi.org/10.1784/insi.46.8.451.39377.

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Kim, Chong Hee. „Differential fault analysis of ARIA in multi-byte fault models“. Journal of Systems and Software 85, Nr. 9 (September 2012): 2096–103. http://dx.doi.org/10.1016/j.jss.2012.04.009.

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Yang, Yongsheng, Qi Zhang, Minzhen Wang, Xinheng Wang und Entie Qi. „Fault Location Method of Multi-Terminal Transmission Line Based on Fault Branch Judgment Matrix“. Applied Sciences 13, Nr. 2 (15.01.2023): 1174. http://dx.doi.org/10.3390/app13021174.

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Aiming at the difficulty of fault location of multi-source transmission lines, this paper proposes a fault location method for multi-terminal transmission lines based on a fault branch judgment matrix. The fault traveling wave signal is decomposed by Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), and the IMFs sensitive components that can characterize the fault characteristics of the target signals are selected by constructing a correlation-rearrangement entropy function. The arrival time of fault signals at the endpoint has been accurately calibrated by combining them with the Teager Energy Operator (TEO). To eliminate the influence of wave velocity and fault time on the location results, this paper proposes a two-terminal location method based on the line mode component to improve the location accuracy. On this basis, combined with the fault branch judgment matrix, the accurate location of multi-terminal transmission line faults is realized. This method has been shown to have high accuracy in detecting traveling wave heads, accurately judging fault branches, and producing a small error in fault location results. Compared with the existing multi-terminal transmission line fault location algorithm, it has obvious advantages and meets the needs of actual working conditions.

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Gao, Lei, Jing Li, Jin Long Zou, Fa Yu Sun und Zhi Yong Lei. „Multi-Device Health Management Based on Elman Neural Network“. Applied Mechanics and Materials 433-435 (Oktober 2013): 774–77. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.774.

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For fault forecast and information exchange problems in multi-device health management, the paper proposed a multi-device and multi-parameter fault forecast technology based on Internet of Things, and improved fault prediction algorithm based on Elman neural network feedback. Therefore, it can implement approximating nonlinear functions by arbitrary accuracy, and use the feedback reference to historical data. Thereby, it can provide early detection, isolation, management and forecasting for component failure warning, the initial issuance of the fault condition and ancillary component failure and other states multi-device health management. It also improves self-learning and adaptive capacity of the system, and effectively improves the robustness of the fault prediction system.

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Yang, Pu, Mengyang Xu, Dejie Li, Zhangxi Liu und Yi Huang. „Distributed fault tolerant consensus control for multi-agent system with actuator fault based on adaptive observer“. Transactions of the Institute of Measurement and Control 41, Nr. 15 (17.06.2019): 4207–17. http://dx.doi.org/10.1177/0142331219853075.

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This paper mainly investigates the consensus control problem of multi-agent system with actuator fault under the leader-follower topology structure. For actuator fault, the additive and multiplicative fault are also considered in this paper. It is worth noting here that for actuator fault we only consider follower agent. For other factors that can affect system functionality, the external bounded disturbance and nonlinear factor are considered for all agents. The main paper organization structure is as follows. Firstly, by constructing nonlinear adaptive fault observer, the estimated values of actuator multiplicative fault and the unknown upper bound of actuator additive fault are respectively obtained. Subsequently, based on the estimated value of fault observer, a distributed fault-tolerant consensus control protocol is proposed to solve the consensus implementation problem of multi-agent system in the case of actuator fault. Finally, the effectiveness of fault-tolerant consensus control algorithm is proved by a simulation example of multi-aircraft system.

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Yang, Liu, Hanxin Chen, Yao Ke, Lang Huang, Qi Wang, Yuzhuo Miao und Li Zeng. „A novel time–frequency–space method with parallel factor theory for big data analysis in condition monitoring of complex system“. International Journal of Advanced Robotic Systems 17, Nr. 2 (01.03.2020): 172988142091694. http://dx.doi.org/10.1177/1729881420916948.

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The spatial information of the signal is neglected by the conventional frequency/time decompositions such as the fast Fourier transformation, principal component analysis, and independent component analysis. Framing of the data being as a three-way array indexed by channel, frequency, and time allows the application of parallel factor analysis, which is known as a unique multi-way decomposition. The parallel factor analysis was used to decompose the wavelet transformed ongoing diagnostic channel–frequency–time signal and each atom is trilinearly decomposed into spatial, spectral, and temporal signature. The time–frequency–space characteristics of the single-source fault signal was extracted from the multi-source dynamic feature recognition of mechanical nonlinear multi-failure mode and the corresponding relationship between the nonlinear variable, multi-fault mode, and multi-source fault features in time, frequency, and space was obtained. In this article, a new method for the multi-fault condition monitoring of slurry pump based on parallel factor analysis and continuous wavelet transform was developed to meet the requirements of automatic monitoring and fault diagnosis of industrial process production lines. The multi-scale parallel factorization theory was studied and a three-dimensional time–frequency–space model reconstruction algorithm for multi-source feature factors that improves the accuracy of mechanical fault detection and intelligent levels was proposed.

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YU, Guangwei, und Li YAN. „A novel bearing fault diagnosis method based on multi-scale transfer symbolic dynamic entropy and support vector machine“. Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 41, Nr. 2 (April 2023): 344–53. http://dx.doi.org/10.1051/jnwpu/20234120344.

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In view of the problem that the generalization ability of traditional data-driven fault diagnosis model declines or even fails in mechanical system diagnosis, a fault diagnosis method based on multi-scale transfer symbolic dynamic entropy and support vector machine is proposed based on the idea of transfer learning. Firstly, multi-scale symbolic dynamic entropy is used to extract fault features from measured vibration signals. And then a feature projection technique based on transfer learning is proposed, which reduces the data distribution difference. Secondly, the parameters of the multi-scale transfer symbol dynamic entropy method are optimized to improve the final fault identification rate. Then, the support vector machine can implement the fault identification. Finally, through the test of bearing fault experimental signals, the rolling bearing diagnosis method based on multi-scale transfer symbol dynamic entropy can effectively improve the generalization ability of data-driven model and realize accurate identification of different fault types of rolling bearing under a small number of samples.

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Guo, Jie, Changchun Chi und Diancheng Yao. „Multi-classification fault diagnosis of AC series arc based on wavelet decomposition and probabilistic neural network“. Journal of Physics: Conference Series 2797, Nr. 1 (01.07.2024): 012051. http://dx.doi.org/10.1088/1742-6596/2797/1/012051.

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Abstract AC series arc is difficult to detect because of its complex characteristics, and it can easily cause electrical fire or explosion and other accidents. In order to solve the problem that multi-class loads can not be easily recognized when an arc fault occurs, this paper presents a method for AC series arc multi-classification fault diagnosis based on wavelet decomposition and probabilistic neural network (PNN). According to the standard, the experiment platform of fault arc is designed in this paper, and the current waveform data of fault arc and normal operation under different load types are collected. The multi-dimensional characteristics of the high-frequency current waveform are extracted by processing the detail coefficients obtained by wavelet decomposition. According to the load type and working state, multi-classification data sets are built and input into PNN to train and test the fault diagnosis model. The proposed method can not only accurately detect the fault arc, but also effectively identify the fault load line, which has important reference significance for AC series arc fault diagnosis.

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Liu, Cai Qin, Er Ling Cao und Sheng Qiang Wu. „A New Fault Diagnosis Method of Adaptive Demodulated Resonance Technique Based on Wavelet Packet in Multi-Information Domains“. Advanced Materials Research 562-564 (August 2012): 1598–601. http://dx.doi.org/10.4028/www.scientific.net/amr.562-564.1598.

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Fault information is incomplete while using a single information domain fault feature parameters to construct fault feature vector, and demodulated resonance technique have to predetermine resonant frequency and fixed center frequency also has its shortcomings , in order to solve these problems, a new fault diagnosis method is proposed of adaptive demodulated resonance technique based on wavelet packet in multi-information domains. The fault feature vector extracted from multi- information domains is described, signal processing flow of envelope demodulation based on denoising and filtering of wavelet packet is analyzed, the fault diagnosis method of adaptive demodulated resonance technique based on wavelet packet is given, and the method is applied to fault diagnosis of axial piston hydraulic pump. Experiment results show that multi-domain feature vector increases the completeness of the fault information, it is able to obtain good diagnosis effect, and the new fault diagnosis method is able to identify known and unknown faults resonance frequency automatically, the frequency range is narrow, the rate of diagnosis is high.

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Cao, Yuyan, Ting Li, Yang Li und Xinmin Wang. „Heterogeneous Multi-Agent-Based Fault Diagnosis Scheme for Actuation System“. Actuators 11, Nr. 4 (18.04.2022): 113. http://dx.doi.org/10.3390/act11040113.

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In this paper, a fault diagnosis method of a heterogeneous multi-agent is proposed that realizes the rapid and accurate fault diagnosis of a redundant multi-type actuation system of large aircraft. Firstly, the multi-agent model of a large aircraft actuation system is established, the composition of the actuation system and the relationship between each multi-agent are clarified and three different types of actuator mathematical models are established. Secondly, a fault detection and isolation (FDI) model is established and transformed into an optimization problem according to different performance index requirements. Aiming at the optimization problem, combined with the principle of linear matrix inequality (LMI), the fault diagnosis algorithm of a heterogeneous multi-agent system is designed. Moreover, the threshold judgment method based on the error signal is presented. Finally, the three actuator models of the aileron actuation system of large aircraft are combined to complete the fault diagnosis of a heterogeneous multi-agent system under the given model interference and model fault. The obtained results demonstrate and validate that the proposed method can accurately and effectively diagnose the faults of the actuator and its associated actuators.

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Chen, Wenxian, Kuangchi Sun, Xinxin Li, Yanan Xiao, Jiangshu Xiang und Hanling Mao. „Adaptive Multi-Channel Residual Shrinkage Networks for the Diagnosis of Multi-Fault Gearbox“. Applied Sciences 13, Nr. 3 (29.01.2023): 1714. http://dx.doi.org/10.3390/app13031714.

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Intelligent fault diagnosis is a hot research topic in machinery and equipment health monitoring. However, most intelligent fault diagnosis models have good performance in single fault mode, but poor performance in multiple fault modes. In real industrial scenarios, the interference of noise also makes it difficult for intelligent diagnostic models to extract fault features. To solve these problems, an adaptive multi-channel residual shrinkage network (AMC-RSN) is proposed in this paper. First, a channel attention mechanism module is constructed in the residual block and a soft thresholding function is introduced for noise reduction. Then, an adaptive multi-channel network is constructed to fuse the feature information of each channel in order to extract as many features as possible. Finally, the Meta-ACON activation function is used before the fully connected layer to decide whether to activate the neurons by the model outputs. The method was implemented in gearbox fault diagnosis, and the experimental results show that AMC-RSN has better diagnostic results than other networks under various faults and strong noises.

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Straube, Bernd, Wolfgang Vermeiren und Volker Spenke. „Multi-level hierarchical analogue fault simulation“. Microelectronics Journal 33, Nr. 10 (Oktober 2002): 815–21. http://dx.doi.org/10.1016/s0026-2692(02)00099-x.

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Krishna Kumar, R., SK Sinha und LM Patnaik. „A fault-tolerant multi-transputer architecture“. Microprocessors and Microsystems 17, Nr. 2 (Januar 1993): 75–81. http://dx.doi.org/10.1016/0141-9331(93)90074-h.

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Du, Bi Qiang, und Gui Ji Tang. „Multi-Fractal Vibration Signal Fault Diagnosis“. Applied Mechanics and Materials 105-107 (September 2011): 652–55. http://dx.doi.org/10.4028/www.scientific.net/amm.105-107.652.

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Dhimish, Mahmoud, Violeta Holmes, Bruce Mehrdadi und Mark Dales. „Multi‐layer photovoltaic fault detection algorithm“. High Voltage 2, Nr. 4 (Dezember 2017): 244–52. http://dx.doi.org/10.1049/hve.2017.0044.

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Ghedini, Cinara, Carlos Ribeiro und Lorenzo Sabattini. „Toward fault-tolerant multi-robot networks“. Networks 70, Nr. 4 (13.10.2017): 388–400. http://dx.doi.org/10.1002/net.21784.

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Zhang, Shu Qing, Yu Zhu He, Jin Min Zhang und Yu Chun Zhao. „Multi-Fractal Based Fault Diagnosis Method of Rotating Machinery“. Applied Mechanics and Materials 130-134 (Oktober 2011): 571–74. http://dx.doi.org/10.4028/www.scientific.net/amm.130-134.571.

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Aiming at complex features of the fault rotating machinery such as nonstationary and nonlinearity, a new method for fault diagnosis based on multi-fractal was introduced. The vibration signals firstly are analyzed by multi-fractal theory and have multi-fractal characteristics. Then the area of multi-fractal spectrum S and the entropy of multi-fractal spectrum Hm were extracted as new criterions to diagnose machinery faults. Results of experimental analysis indicate that the method is effective and it provides a new way in fault diagnosis of rotating machinery.

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Subbarao, Kamesh, und Arun T. Vemuri. „Extrinsic curvature-based fault isolation for multi-input—multi-output systems with non-linear fault models“. Transactions of the Institute of Measurement and Control 31, Nr. 3-4 (Juni 2009): 259–74. http://dx.doi.org/10.1177/0142331208092028.

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Abdelrhman, Ahmed M., M. Salman Leong, Lim Meng Hee und Wai Keng Ngui. „Application of Wavelet Analysis in Blade Faults Diagnosis for Multi-Stages Rotor System“. Applied Mechanics and Materials 393 (September 2013): 959–64. http://dx.doi.org/10.4028/www.scientific.net/amm.393.959.

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Blade fault is one of the most common faults in turbomachinery. In this article, a rotor system which consists of multiple stages of blades was developed. A variety of blade fault conditions were investigated and its vibration responses were measured. The feasibility of wavelet analysis for multi-stages blade fault diagnosis was tested using simulated signals as well as experimental data. The use of wavelet analysis as the tool to detect multi stages blade faults was studied. Some probable solutions to improve multi stages blade fault diagnosis by wavelet analysis were also suggested.

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ZHOU, Quan, Lan LIU, Hao CHENG und Mian FU. „Research on Multi-element Fusion of Equipment Fault Monitoring Based on Evidence Theory“. E3S Web of Conferences 179 (2020): 02001. http://dx.doi.org/10.1051/e3sconf/202017902001.

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The multi-information fusion method of health monitoring based on evidence theory is used to study the problem of equipment fault diagnosis. The multi-information of fault monitoring is fused by the evidence theory and the reliability of the relevant evidence can be judged according to the ambiguity and uncertainty of the fault monitoring signal. Also it can determine the importance and reliability of the evidence from different sources. The data from multi-information fusion has higher reliability and accuracy which provides more reliable data for fault diagnosis.

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Li, Ya, Hai Rui Wang und Lin Wu. „Multi-Agent Based Design for Distributed Fault Diagnosis Systems“. Advanced Materials Research 179-180 (Januar 2011): 1266–71. http://dx.doi.org/10.4028/www.scientific.net/amr.179-180.1266.

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A Multi-Agent based distributed fault diagnosis reference model is important for high speed and automation. Fault diagnosis and maintenance are vital aspects in industrial process, in this sense, fault diagnosis systems should support decision-making tools, new diagnosis approaches and techniques, updating of the devices. In this paper a Multi-Agent based fault diagnosis reference model is presented which combines the existing models and Multi-Agent. This model is based on a generic framework using Multi-Agent System; in this sense, the fault diagnosis problem is viewed like a feedback control process and the actions are related to the decision-making in the scheduling of the preventive maintenance task and the running of preventive and corrective specific maintenance tasks. As a result, a particular prototype design based on the JADE platform is obtained. This new model is compared to some important existing models and applied to a real investigation. This Multi-Agent based model provides a comprehensive treatment of the fault management problem, it can be used in any level of industrial environments and it can be integrated with the supervision applications without modifying the current automation architecture.

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Li, Jiejia, Tianhao Gao und Xinyang Ji. „Multi-model and multi-level aluminum electrolytic fault diagnosis method“. Transactions of the Institute of Measurement and Control 41, Nr. 15 (10.07.2019): 4409–23. http://dx.doi.org/10.1177/0142331219859786.

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A multi-model and multi-level aluminum electrolytic fault prediction method is proposed. In this method, it innovatively uses the image recognition technology to predict aluminum electrolytic faults, and superimposes the chaotic neural network model to form a dual-model parallel fault prediction system for aluminum electrolysis, which can obtain more faults information from different angles. Then, it designs the decision fusion layer, which combines the prediction results of the above two models to output the final prediction results and enhances the credibility of the prediction results. In addition, the data processing stage also uses principal component analysis (PCA) to extract the main features of fault information, which reduces the data dimension and speeds up the processing. Experimental results suggest that the proposed algorithm can predict faults in an effective manner, and outperform other algorithms in terms of accuracy, sensitivity and stability.

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Hu, Jinglu, Kotaro Hirasawa und Kousuke Kumamaru. „Neurofuzzy Approach to Fault Detection of Nonlinear Systems“. Journal of Advanced Computational Intelligence and Intelligent Informatics 3, Nr. 6 (20.12.1999): 524–31. http://dx.doi.org/10.20965/jaciii.1999.p0524.

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This paper proposes a neurofuzzy approach to fault detection in linear systems. The system diagnosed is described by using a neurofuzzy model called LimNet that consists of a linear model and multiple local linear models with interpolation of a "fuzzy basis function". Fault detection is considered in two cases: when faults occur in the linear model part, a KDI-based robust fault detection is applied, where a multi-local-model part is treated as error due to nonlinear undermodeling; when faults occur in the multi-local-model part, a multi-model based fault detection method is developed, in which the identified LimNet is interpreted as several local ARMAX models, and KDI is used as an index to discriminate between each local model and its reference. This paper mainly concentrates discussions on multi-model based fault detection.

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Fei, Cheng-Wei, Yat-Sze Choy, Guang-Chen Bai und Wen-Zhong Tang. „Multi-feature entropy distance approach with vibration and acoustic emission signals for process feature recognition of rolling element bearing faults“. Structural Health Monitoring 17, Nr. 2 (24.01.2017): 156–68. http://dx.doi.org/10.1177/1475921716687167.

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To accurately reveal rolling bearing operating status, multi-feature entropy distance method was proposed for the process character analysis and diagnosis of rolling bearing faults by the integration of four information entropies in time domain, frequency domain and time–frequency domain and two kinds of signals including vibration signals and acoustic emission signals. The multi-feature entropy distance method was investigated and the basic thought of rolling bearing fault diagnosis with multi-feature entropy distance method was given. Through rotor simulation test rig, the vibration and acoustic emission signals of six rolling bearing faults (ball fault, inner race fault, outer race fault, inner ball faults, inner–outer faults and normal) are gained under different rotational speeds. In the view of the multi-feature entropy distance method, the process diagnosis of rolling bearing faults was implemented. The analytical results show that multi-feature entropy distance fully reflects the process feature of rolling bearing faults with the change of rotating speed; the multi-feature entropy distance with vibration and acoustic emission signals better reports signal features than single type of signal (vibration or acoustic emission signal) in rolling bearing fault diagnosis; the proposed multi-feature entropy distance method holds high diagnostic precision and strong robustness (anti-noise capacity). This study provides a novel and useful methodology for the process feature extraction and fault diagnosis of rolling element bearings and other rotating machinery.

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Chen, Pei, Huanguo Chen, Wenhua Chen, Jun Pan und Jianmin Li. „An improved EKF based on excitation equivalent conversion for EHA multi-factor fault diagnosis“. Advances in Mechanical Engineering 14, Nr. 10 (Oktober 2022): 168781322211312. http://dx.doi.org/10.1177/16878132221131292.

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Electro-hydrostatic actuator (EHA), as an emerging power-by-wire (PBW) actuation mechanism with high energy efficiency and fast responsiveness, has been widely used in modern flight control systems. As a pivotal component, the fault diagnosis of EHA is necessary to ensure the reliability of the aircraft. Although researchers have proposed many effective fault diagnosis techniques at present, most of them can only deal with single-factor faults effectively. Recent studies on multi-state reliability and competing failure show that complicated systems such as EHA are more prone to multi-factor failures than single-factor failures. Therefore, an improved EKF based on excitation equivalent conversion is proposed in this paper to achieve the multi-factor fault diagnosis of EHA. First, the existing fault diagnosis methods for EHA and their limitations in multi-factor fault diagnosis are discussed. Then, multi-parameters estimation and observability, the key issues to achieve multi-factor fault diagnosis, are analyzed. Based on the structural characteristics and observability analysis of the second-order system, excitation equivalent conversion is introduced to establish additional available equation about the unknown state parameters to realize the multi-parameter estimation when system is unobservable. Finally, simulation and prototype test experiments have been performed, and the results demonstrate the efficacy of the proposed method, which outperforms that of the traditional single-factor failure analysis methods by comparison.

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Yang, Su Fei, und Xiao Pin Wu. „The Application of Multi-Agent Technology in the Fault Diagnosis System of Motor Armature“. Applied Mechanics and Materials 84-85 (August 2011): 106–9. http://dx.doi.org/10.4028/www.scientific.net/amm.84-85.106.

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Insufficient welding or bad welding of motor armature is one of the main faults of motor armature. With the help of the application of multi-agent technology in the fault diagnosis system of motor armature to detect insufficient welding or bad welding, the intelligent description of the various faults has been enhanced, also, the capacity of fault diagnosis of the system have been greatly optimized and improved. In this paper, we first analyses the characteristics of the fault of motor armature with insufficient welding or bad welding, by which the characteristics of the fault are identified. Next, we introduces agent and multi-agent system. Finally, the building of fault diagnosis system based on multi-agent technology is discussed.

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Khurshid, Shozab, Avinash K. Shrivastava und Javaid Iqbal. „Generalised multi release framework for fault determination with fault reduction factor“. International Journal of Information and Computer Security 17, Nr. 1/2 (2022): 164. http://dx.doi.org/10.1504/ijics.2022.121296.

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41

SARAT CHANDRA BABU, N., V. C. PRASAD, S. P. VENU MADHAVA RAO und K. LAL KISHORE. „MULTI-FREQUENCY APPROACH TO FAULT DICTIONARY OF LINEAR ANALOG FAULT DIAGNOSIS“. Journal of Circuits, Systems and Computers 17, Nr. 05 (Oktober 2008): 905–28. http://dx.doi.org/10.1142/s0218126608004605.

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An efficient method to eliminate redundant frequencies present in one of the existing multi-frequency methods for analog fault diagnosis is proposed in this paper. First the two-dimensional fault dictionary is constructed where entries are gain signatures of all faults and frequencies. The faults belonging to the same quantization levels are numbered sequentially and a frequency that has an ambiguity set with the highest number faults is eliminated after verifying that there are no repetitions after the deletion of this frequency. In this manner, all frequencies are examined for deletion. Finally the test frequencies, which cannot be deleted, remain resulting in a minimal set of test frequencies of a network to isolate a given set of faults. Another method proposes a technique, which generates more number of frequencies to isolate all the faults, if the test frequencies generated using the existing methods are not sufficient.

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Iqbal, Javaid, A. K. Shrivastava und Shozab Khurshid. „Generalized Multi Release Framework for Fault Determination with Fault reduction Factor“. International Journal of Information and Computer Security 1, Nr. 1 (2020): 1. http://dx.doi.org/10.1504/ijics.2020.10034037.

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43

Zhang, Min, Ruiqi Wang, Zhenyu Cai und Wenming Cheng. „Phase partition and identification based on kernel entropy component analysis and multi-class support vector machines-fireworks algorithm for multi-phase batch process fault diagnosis“. Transactions of the Institute of Measurement and Control 42, Nr. 12 (24.03.2020): 2324–37. http://dx.doi.org/10.1177/0142331220910885.

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For the characteristics of nonlinear and multi-phase in the batch process, a self-adaptive multi-phase batch process fault diagnosis method is proposed in this paper. Firstly, kernel entropy component analysis (KECA) method is used to achieve multi-phase partition adaptively, which makes the process data mapped into the high-dimensional feature space and then constructs the core entropy and the angular structure similarity. Then a multi-phase KECA failure monitoring model is developed by using the angular structure similarity as the statistic, which is based on the partitioned phases and the effective failure features by the KECA feature extraction method. A multi-phase batch process fault diagnosis method, which applies the multi-class support vector machines (MSVM) and fireworks algorithm (FWA), is proposed to recognize each sub-phase fault diagnosis automatically. The effectiveness and advantages of the proposed multi-phase fault diagnosis method are illustrated with a case study on a fed-batch penicillin fermentation process.

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Xian, Xiaoyu, Haichuan Tang, Yin Tian, Qi Liu und Yuming Fan. „Performance Analysis of Different Machine Learning Algorithms for Identifying and Classifying the Failures of Traction Motors“. Journal of Physics: Conference Series 2095, Nr. 1 (01.11.2021): 012058. http://dx.doi.org/10.1088/1742-6596/2095/1/012058.

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Abstract This paper addresses electric motor fault diagnosis using supervised machine learning classification. A total of 15 distinct fault types are classified and multilabel strategies are used to classify concurrent faults. we explored, developed, and compared the performance of different types of binary (fault/non-fault), multi-class (fault type) and multi-label (single fault versus combination fault) classifiers. To evaluate the effectiveness of fault identification and classification, we used different supervised machine learning methods, including Random forest classification, support vector machine and neural network classification. Through experiment, we compared these methods over 4 classification regimes and finally summarize the most suitable machine learning algorithms for different aspects of health diagnosis in traction motors area.

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Yang, Dong. „Research on fault diagnosis of hot die forging multi-station feeding manipulator“. Journal of Physics: Conference Series 2383, Nr. 1 (01.12.2022): 012062. http://dx.doi.org/10.1088/1742-6596/2383/1/012062.

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In order to improve the efficiency of fault diagnosis of hot die forging multi-station feeding manipulator, according to the fault characteristics of the feeding manipulator, an expert system structure based on fault tree analysis and binary decision graph was designed, and a diagnosis method based on expert system was proposed. Firstly, according to the fault causes of multi-station feeding manipulator, the fault tree is established. Secondly, the binary decision graph is used to analyse the fault tree, acquire the diagnosis expert knowledge, and establish the knowledge base of fault diagnosis. Then, the binary decision graph is used to quantitatively analyse the fault tree, the importance of the minimum cut set is solved, and the rule priority is determined, so as to solve the rule conflict problem and improve the efficiency of fault diagnosis. Finally, a case study is conducted. The results prove the validity and feasibility of the fault diagnosis method proposed in this paper.

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Ma, Jie, und Jianan Xu. „Fault Prediction Algorithm for Multiple Mode Process Based on Reconstruction Technique“. Mathematical Problems in Engineering 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/348729.

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In the framework of fault reconstruction technique, this paper studies the problems of multiple mode process fault detection, fault estimation, and fault prediction systematically based on multi-PCA model. First, a multi-PCA model is used for fault detection in steady state process under different conditions, while a weighted algorithm is applied to transition process. Then, describe the faults quantitatively and use the optimization method to derive the fault amplitude under the sense of fault reconstruction. Fault amplitude drifts under different conditions even if the same fault occurs. To solve the above problem, consistent estimation algorithm of fault amplitude under different conditions has been studied. Last, employ the support vector machine (SVM) to predict the trend of the fault amplitude. Effectiveness of the algorithms proposed in this paper has been verified using Tennessee Eastman process as the study object.

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Zheng, Peihao, Guoxian Dou und Liang Gao. „Fault diagnosis model of main equipment in substation based on multi-mode time-frequency combination“. Journal of Physics: Conference Series 2592, Nr. 1 (01.09.2023): 012079. http://dx.doi.org/10.1088/1742-6596/2592/1/012079.

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Abstract The conventional fault diagnosis method of the main equipment in substations has strong limitations and the fault diagnosis accuracy is low, so this paper introduces the multi-mode time-frequency combination method and designs the fault diagnosis model of the main equipment in substations. First, the transmission mode of fault information of main transformer equipment is analyzed and the fault source is diagnosed. Secondly, the UHF partial discharge method is used to detect the partial discharge signal of the main substation equipment. On this basis, the main equipment fault diagnosis model of the substation is established based on the multi-mode time-frequency combination, and the equipment fault degree is obtained to complete the fault diagnosis. According to the experimental results, the proposed fault diagnosis model is highly feasible, and the accuracy of an insulation fault, overheating fault and discharge fault diagnosis is more than 98%.

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Yu, Gang, und Jian Kang. „A New Method for Multi-Fault Diagnosis of Rotating Machinery Based on the Mixture Alpha Stable Distribution Model“. Advanced Materials Research 977 (Juni 2014): 349–52. http://dx.doi.org/10.4028/www.scientific.net/amr.977.349.

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As one of the most important type of machinery, rotating machinery may malfunction due to various reasons. Sometimes the fault is a single one, but sometimes it maybe in multi-fault condition, this paper mainly focus on the latter. First, the paper gives a brief introduction of the study on multi-fault, then it introduces the mixture of Alpha stable distribution model, besides, it gives the model parameters estimation algorithm in detail, at last we use the SOM net to complete pattern recognition. The results prove that this modeling method is effective in multi-fault diagnosis in rotating machinery.

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Guo, Bao Liang, Zhi Shan Duan, Jian Xiao Zheng und Li Chen Shi. „Research on Vibrating Machine Rolling Bearing Multi-Point Pitting Corrosion Fault Diagnosis“. Applied Mechanics and Materials 251 (Dezember 2012): 318–22. http://dx.doi.org/10.4028/www.scientific.net/amm.251.318.

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Aiming at the discrimination problem of the multi-point pitting corrosion fault for the vibrating machine rolling bearing, the vibration model of the multi-point pitting corrosion fault was built for the vibrating machine rolling bearing with inner ring and outer ring based on Hertz contact theory and the discrimination criterion of the multi-point pitting corrosion fault was proposed. At the same time, the theoretical model was simulated and the experiment was finished in the vibrating sieve. The experiment results are consistent with the theoretical analysis results. The results show that there is the obvious difference from the fault characteristic spectrum of the rolling bearing multi-point pitting corrosion for the vibrating machine and the rotating machinery. There is an obvious amplitude modulation phenomenon for the outer ring pitting corrosion fault of the vibrating machine rolling bearing, but there is not the amplitude modulation phenomenon for the outer ring pitting corrosion fault of the ideal rotating machinery rolling bearing. There is a slight amplitude modulation phenomenon for the inner ring pitting corrosion fault of the vibrating machine rolling bearing and but there is the obvious amplitude modulation phenomenon for the inner ring pitting corrosion fault of the ideal rotating machinery rolling bearing.

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Gu, Yufeng, Yongji Zhang, Mingrui Yang und Chengshan Li. „Motor On-Line Fault Diagnosis Method Research Based on 1D-CNN and Multi-Sensor Information“. Applied Sciences 13, Nr. 7 (25.03.2023): 4192. http://dx.doi.org/10.3390/app13074192.

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The motor is the primary impetus source of most mechanical equipment, and its failure will cause substantial economic losses and safety problems. Therefore, it is necessary to study online fault diagnosis techniques for motors, given the problems caused by shallow learning models or single-sensor fault analysis in previous motor fault diagnosis techniques, such as blurred fault features, inaccurate identification, and time and manpower consumption. In this paper, we proposed a model for motor fault diagnosis based on deep learning and multi-sensor information fusion. Firstly, a correlation adaptive weighting method is proposed in this paper, and it is used to integrate the collected multi-source homogeneous sensor information into multi-source heterogeneous sensor information through the data layer fusion. Secondly, the 1D-CNN is used to carry out feature extraction, feature layer fusion, and fault classification of multi-source heterogeneous information of the motor. Finally, the data of seven states (one healthy and six faulty) of the motor are collected by the motor drive test bench to realize the model’s training, testing, and verification. The experimental results show that the fault diagnosis accuracy of the model is 99.3%. Thus, this method has important practical implications for improving the accuracy of motor fault diagnosis further.

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