Journal articles: 'Simulation of fault signals'

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Liu, Xiaoyang, Haizhou Huang, and Jiawei Xiang. "A Personalized Diagnosis Method to Detect Faults in a Bearing Based on Acceleration Sensors and an FEM Simulation Driving Support Vector Machine." Sensors 20, no. 2 (January 11, 2020): 420. http://dx.doi.org/10.3390/s20020420.

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Classification of faults in mechanical components using machine learning is a hot topic in the field of science and engineering. Generally, every real-world running mechanical system exhibits personalized vibration behaviors that can be measured with acceleration sensors. However, faulty samples of such systems are difficult to obtain. Therefore, machine learning methods, such as support vector machine (SVM), neural network (NNs), etc., fail to obtain agreeable fault detection results through smart sensors. A personalized diagnosis fault method is proposed to activate the smart sensor networks using finite element method (FEM) simulations. The method includes three steps. Firstly, the cosine similarity updated FEM models with faults are constructed to obtain simulation signals (fault samples). Secondly, every simulation signal is separated into sub-signals to solve the time-domain indexes to generate the faulty training samples. Finally, the measured signals of unknown samples (testing samples) are inserted into the trained SVM to classify faults. The personalized diagnosis method is applied to detect bearing faults of a public bearing dataset. The classification accuracy ratios of six types of faults are 90% and 92.5%, 87.5% and 87.5%, 85%, and 82.5%, respectively. It confirms that the present personalized diagnosis method is effectiveness to detect faults in the absence of fault samples.

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Huo, Linsheng, Gangbing Song, Satish Nagarajaiah, and Hongnan Li. "Semi-active vibration suppression of a space truss structure using a fault tolerant controller." Journal of Vibration and Control 18, no. 10 (October 7, 2011): 1436–53. http://dx.doi.org/10.1177/1077546311421514.

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In recent years, magneto-rheological (MR) dampers have been used to control the response of structures. This paper presents the design and application of an H∞ fault detection and isolation (FDI) filter and fault tolerant controller (FTC) for truss vibration control systems using MR dampers. A linear matrix inequality formulation is used to design a full order robust H∞ filter to estimate faulty input signals. A fault tolerant H∞ controller is designed for the combined system of plant and filter, minimizing the control objective selected in the presence of disturbances and faults. A truss structure with an MR damper is used to validate the FDI and FTC controller design through numerical simulations. The residuals obtained from the filter through simulation clearly identify the fault signals. The simulation results of the proposed FTC controller confirm its effectiveness for vibration suppression of the faulty truss system.

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Zhang, Changfan, Huijun Liao, Xiangfei Li, Jian Sun, and Jing He. "Fault Reconstruction Based on Sliding Mode Observer for Current Sensors of PMSM." Journal of Sensors 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/9307560.

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This paper deals with a method of phase current sensor fault reconstruction for permanent magnet synchronous motor (PMSM) drives. A new state variable is introduced so that an augmented system can be constructed to treat PMSM sensor faults as actuator faults. This method uses the PMSM two-phase stationary reference frame fault model and a sliding mode variable structure observer to reconstruct fault signals. A logic algorithm is built to isolate and identify the faulty sensor for a stator phase current fault after reconstructing the two-phase stationary reference frame fault signals, which allows the phase fault signals to be reconstructed. Simulation results are presented to illustrate the functionality of the theoretical developments.

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Fei, Cheng-Wei, Yat-Sze Choy, Guang-Chen Bai, and 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, no. 2 (January 24, 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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Dou, Chun Hong. "Fault Feature Extraction for Gearboxes Using Empirical Mode Decomposition." Advanced Materials Research 383-390 (November 2011): 1376–80. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.1376.

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The paper uses empirical mode decomposition to extract the fault feature of gearboxes. Traditional techniques fail to process the non-stationary and nonlinear signals. Empirical mode decomposition is a powerful tool for the non-stationary and nonlinear signal analysis and has attracted considerable attention recently. First, a simulation signal is used to measure the performance of the empirical mode decomposition method. Then, the empirical mode decomposition method is applied to analyze the signals captured from the gearbox with multiple faults and successfully extracts the multiple fault information from the collected signals. The results show that empirical mode decomposition could be a helpful method for mechanical fault feature extraction.

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Liu, Shu Lin, You Fu Tang, Ji Cheng Liu, and Ying Hui Liu. "Research of Fault Feature Extraction Based on High Order Cyclic Statistics for Reciprocating Compressor Gas Valves." Applied Mechanics and Materials 44-47 (December 2010): 2094–98. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.2094.

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. This paper proposes an approach of fault feature extraction for reciprocating compressor gas valves based on theory of cyclic statistics. First, the strength and weakness of the third-order cyclic statistics in extracting signal features are investigated by simulation signals. Since vibration signals for reciprocating compressor gas valves are of typical cyclic stationary, a new method of fault feature extraction is then proposed based on the simulation results. The method utilizes the cyclic bi-spectrum to extract fault features for the corresponding frequencies. The results show that the cyclic bi-spectrum characteristics for typical faults of gas valves are apparently different, and that the typical faults of reciprocating compressor gas valves can be diagnosed exactly. So the new method proposed in this paper is effective and feasible.

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Kang, Wei, Guang Jian Chang, and Xin Yong Qiao. "Approach to Diagnose Gear Tooth-Broken Fault Based on Web." Advanced Materials Research 433-440 (January 2012): 2257–62. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.2257.

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Gear box is one of the most important parts of vehicle transmission. Its fault influences the mobility of vehicle. In all the gear faults tooth-broken have the most serious harm. In this paper fault simulation test was taken on armored vehicle, and the vibration signals of gear box were measured. A signal analysis and fault diagnosis system was developed based on wireless web to analyze the signals in time domain and frequency domain. In this way a type of remote diagnosis is realized to diagnosing gear fault.

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Zhang, Dingcheng, Dejie Yu, and Xing Li. "Optimal resonance-based signal sparse decomposition and its application to fault diagnosis of rotating machinery." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 231, no. 24 (November 26, 2016): 4670–83. http://dx.doi.org/10.1177/0954406216671542.

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The fault diagnosis of rotating machinery is quite important for the security and reliability of the overall mechanical equipment. As the main components in rotating machinery, the gear and the bearing are the most vulnerable to faults. In actual working conditions, there are two common types of faults in rotating machinery: the single fault and the compound fault. However, both of them are difficult to detect in the incipient stage because the weak fault characteristic signals are usually submerged by strong background noise, thus increasing the difficulty of the weak fault feature extraction. In this paper, a novel decomposition method, optimal resonance-based signal spares decomposition, is applied for the detection of those two types of faults in the rotating machinery. This method is based on the resonance-based signal spares decomposition, which can nonlinearly decompose vibration signals of rotating machinery into the high and the low resonance components. To extract the weak fault characteristic signals in the presence of strong noise effectively, the genetic algorithm is used to obtain the optimal decomposition parameters. Then, the optimal high and low resonance components, which include the fault characteristic signals of rotating machinery, can be obtained by using the resonance-based signal spares decomposition method with the optimal decomposition parameters. Finally, the high and the low resonance components are subject to the Hilbert transform demodulation analysis; the faults of rotating machinery can be diagnosed based on the obtained envelop spectra. The optimal resonance-based signal spares decomposition method is successfully applied to the analysis of the simulation and experiment vibration signals. The analysis results demonstrate that the proposed method can successfully extract the fault features in rotating machinery.

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Papathanasopoulos, Dimitrios A., Konstantinos N. Giannousakis, Evangelos S. Dermatas, and Epaminondas D. Mitronikas. "Vibration Monitoring for Position Sensor Fault Diagnosis in Brushless DC Motor Drives." Energies 14, no. 8 (April 16, 2021): 2248. http://dx.doi.org/10.3390/en14082248.

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A non-invasive technique for condition monitoring of brushless DC motor drives is proposed in this study for Hall-effect position sensor fault diagnosis. Position sensor faults affect rotor position feedback, resulting in faulty transitions, which in turn cause current fluctuations and mechanical oscillations, derating system performance and threatening life expectancy. The main concept of the proposed technique is to detect the faults using vibration signals, acquired by low-cost piezoelectric sensors. With this aim, the frequency spectrum of the piezoelectric sensor output signal is analyzed both under the healthy and faulty operating conditions to highlight the fault signature. Therefore, the second harmonic component of the vibration signal spectrum is evaluated as a reliable signature for the detection of misalignment faults, while the fourth harmonic component is investigated for the position sensor breakdown fault, considering both single and double sensor faults. As the fault signature is localized at these harmonic components, the Goertzel algorithm is promoted as an efficient tool for the harmonic analysis in a narrow frequency band. Simulation results of the system operation, under healthy and faulty conditions, are presented along with the experimental results, verifying the proposed technique performance in detecting the position sensor faults in a non-invasive manner.

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Zeng, Wen Feng, Yu Min Tian, Bao Hui Zhu, and Dong Li. "The Design of a Maintenance Simulation System for Lasers." Advanced Materials Research 926-930 (May 2014): 1440–43. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.1440.

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The MCU system is used to simulate laser signals and the signals received by avalanche transistors so as to conduct signal detection of actual circuit boards.With establishing a library of fault cases through the computer serial port, the MCU and the fault setting module can be based on to realize the fault simulation of actual circuit boards and conduct the training in fault detection and removal. The designed fault setting software and assessment software can be combined to assess the training effects of fault analysis, detection and removal. The developed prototype passes the test of training, showing good results and high training benefits

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Dedoussis, V., K. Mathioudakis, and K. D. Papailiou. "Numerical Simulation of Blade Fault Signatures From Unsteady Wall Pressure Signals." Journal of Engineering for Gas Turbines and Power 119, no. 2 (April 1, 1997): 362–69. http://dx.doi.org/10.1115/1.2815583.

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A method for establishing signatures of faults in the rotating blades of a gas turbine compressor is presented. The method employs a panel technique for the calculation of the flow field around blade cascades, with disrupted periodicity, a situation encountered when a blade fault has occurred. From this calculation, time signals of the pressure at a location on the casing wall, facing the rotating blades, are constituted. Processing these signals, in combination with “healthy” pressure signals, allows the constitution of fault signatures. The proposed method employs geometric data, as well as data about the operating point of the engine. It gives the possibility of establishing the fault signatures without the need of performing experiments with implanted faults. The successful application of the method is demonstrated by comparison of signatures obtained by simulation to signatures derived from experiments with implanted blade faults, in an industrial gas turbine.

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Huang, Yong, Min You Chen, Jin Qian Zhai, and Hong Yan. "High Impedance Fault Identification Method of Distribution Networks." Advanced Materials Research 516-517 (May 2012): 1785–90. http://dx.doi.org/10.4028/www.scientific.net/amr.516-517.1785.

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Abstract. High impedance fault has always been difficult for distribution network fault identification due to its unobvious fault signature and difficult detection. This paper decomposed the transient signal in multi-scale by utilizing the good localization performance of the wavelet in both time domain and frequency domain, reconstructed the wavelet coefficients under each scale, took the wavelet reconstruction coefficient which was under the scale 3, calculated the size spectrum of each feeder line in timing floating window and identified the circuits in which the faults lined according to the value of the size spectrum. The high impedance fault simulation system was built based upon the study of the various transient signals in power systems, and the high impedance fault simulation analysis of the distribution feeder was undertaken through PSCAD simulation platform using high impedance fault model. Simulation analysis showed that the method could effectively extract the feature of high impedance fault on high impedance fault identification.

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Du, Can Yi, and Fei Fei Yu. "Analysis of Engine Camshaft Bearing Loosening Fault Based-on Model Simulation and Vibration Signal." Advanced Materials Research 694-697 (May 2013): 896–900. http://dx.doi.org/10.4028/www.scientific.net/amr.694-697.896.

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Virtual technology is used for simulation analysis of engine camshaft bearing-loosening fault. Firstly, dynamic model of engine powertrain and its valve-train is established, and then the model parameters could be set to simulate the camshaft bearing loosening fault, so the vibration acceleration signals on engine cylinder head can be obtained by simulation calculation. Then by analyzing and comparing with the vibration signals in the normal state, camshaft bearing-loosening fault features are extracted. The analytical result based-on model simulation and vibration signal is used to guide the actual engine fault diagnosis.

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Li, Yun Gong, and Jin Ping Zhang. "Comparison and Analysis of Two Auditory Models Faced to Mechanical Faults Diagnosis." Advanced Materials Research 430-432 (January 2012): 1081–86. http://dx.doi.org/10.4028/www.scientific.net/amr.430-432.1081.

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Auditory model is a signal analysis system with simulating the mechanism of the human auditory system, and it is not only suitable for speech signal but also vibration signal for mechanical faults diagnosis. In this paper, the work of analysis and comparison for EA and ZCPA auditory model is done. The reason and characteristics of simulation mode of auditory nerve in two auditory model is illustrated. By analyzing vibration signals of different rotor faults, the performances of distinguishing different faults and stability for one kind fault for two models are compared. The results show that ZCPA model is more flexible and stable.

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Saidina Omar, Abdul Malek, Muhammad Khusairi Osman, Mohammad Nizam Ibrahim, Zakaria Hussain, and Ahmad Farid Abidin. "Fault classification on transmission line using LSTM network." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 1 (October 1, 2020): 231. http://dx.doi.org/10.11591/ijeecs.v20.i1.pp231-238.

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Deep Learning has ignited great international attention in modern artificial intelligence techniques. The method has been widely applied in many power system applications and produced promising results. A few attempts have been made to classify fault on transmission lines using various deep learning methods. However, a type of deep learning called Long Short-Term Memory (LSTM) has not been reported in literature. Therefore, this paper presents fault classification on transmission line using LSTM network as a tool to classify different types of faults. In this study, a transmission line model with 400 kV and 100 km distance was modelled. Fault free and 10 types of fault signals are generated from the transmission line model. Fault signals are pre-processed by extracting post-fault current signals. Then, these signals are fed as input to the LSTM network and trained to classify 10 types of faults. The white Gaussian noise of level 20 dB and 30 dB signal to noise ratio (SNR) is also added to the fault current signals to evaluate the immunity of the proposed model. Simulation results show promising classification accuracy of 100%, 99.77% and 99.55% for ideal, 30 dB and 20 dB noise respectively. Results has been compared to four different methods which can be seen that the LSTM leading with the highest classification accuracy. In line with the purpose of the LSTM functions, it can be concluded that the method has a capability to classify fault signals with high accuracy.

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Wang, Yulong, Xiaohong Zhang, Lili Li, Jinyang Du, and Junguo Gao. "Design of Partial Discharge Test Environment for Oil-Filled Submarine Cable Terminals and Ultrasonic Monitoring." Energies 12, no. 24 (December 14, 2019): 4774. http://dx.doi.org/10.3390/en12244774.

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Based on the principle of operating an oil-filled-cable operation and the explanation of the oil-filling process provided in the cable operation and maintenance manual of submarine cables, this study investigated oil-pressure variation caused by gas generated as a result of cable faults. First, a set of oil-filled cables and their terminal oil-filled simulation system were designed in the laboratory, and a typical oil-filled-cable fault model was established according to the common faults of oil-filled cables observed in practice. Thereafter, ultrasonic signals of partial discharge (PD) under different fault models were obtained via validation experiments, which were performed by using oil-filled-cable simulation equipment. Subsequently, the ultrasonic signal mechanism was analyzed; these signals were generated via electric, thermal, and acoustic expansion and contraction, along with electric, mechanical, and acoustic electrostriction. Finally, upon processing the 400 experimental data groups, four practical parameters—maximum amplitude of the ultrasonic signal spectrum, Dmax, maximum frequency of the ultrasonic signals, fmax, average ultrasonic signal energy, Dav, and the ultrasonic signal amplitude coefficient, M—were designed to characterize the ultrasonic signals. These parameters can be used for subsequent pattern recognition. Thus, in this study, the terminal PD of an oil-filled marine cable was monitored.

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Xiao, Huifang, Xiaojun Zhou, and Yimin Shao. "Application of an improved dynamic time synchronous averaging method for fault diagnosis in conditions of speed fluctuation and no tachometer." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 230, no. 14 (July 27, 2015): 2517–31. http://dx.doi.org/10.1177/0954406215597956.

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Time synchronous averaging has been widely used for machinery fault diagnosis. However, it cannot reveal signal characteristics accurately in conditions of speed fluctuation and no tachometer due to the phase accumulation error. In this paper, an improved dynamic-time synchronous averaging method is proposed to extract the periodic feature signal from the fluctuated vibration signal for fault detection when no tachometer signal is available. In this method, empirical mode decomposition, dynamic time warping, and time synchronous averaging are performed on gear vibration signals to detect fault characteristic information. First, empirical mode decomposition is performed on the vibration signal and a series of intrinsic mode functions are produced. The sensitive intrinsic mode functions providing fault-related information are selected and reconstructed and the corresponding envelop signals are equal-space intercepted. Then, the phase accumulation error among the envelop signal segments is estimated by the dynamic time warping, which is further used to compensate the phase accumulation error between the intrinsic mode function segments of the reconstructed signal. Finally, the compensated intrinsic mode function segments are averaged to obtain the feature signal. Simulation analysis shows the advantages of the proposed method in extracting faulty feature signal from speed fluctuation signal without tachometer and identifying gear fault. Experiments with both normal and faulty gear were conducted and the vibration signals were captured. The proposed method is applied to identify the gear damage and the diagnosis results demonstrate its superiority than other methods.

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Wang, Xiaoqian, Dali Sheng, Jinlian Deng, Wei Zhang, Jie Cai, Weisheng Zhao, and Jiawei Xiang. "Kernel Regression Residual Decomposition Method to Detect Rolling Element Bearing Faults." Mathematical Problems in Engineering 2021 (April 28, 2021): 1–10. http://dx.doi.org/10.1155/2021/5523098.

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The raw vibration signal carries a great deal of information representing the mechanical equipment's health conditions. However, in the working condition, the vibration response signals of faulty components are often characterized by the presence of different kinds of impulses, and the corresponding fault features are always immersed in heavy noises. Therefore, signal denoising is one of the most important tasks in the fault detection of mechanical components. As a time-frequency signal processing technique without the support of the strictly mathematical theory, empirical mode decomposition (EMD) has been widely applied to detect faults in mechanical systems. Kernel regression (KR) is a well-known nonparametric mathematical tool to construct a prediction model with good performance. Inspired by the basic idea of EMD, a new kernel regression residual decomposition (KRRD) method is proposed. Nonparametric Nadaraya–Watson KR and a standard deviation (SD) criterion are employed to generate a deep cascading framework including a series of high-frequency terms denoted by residual signals and a final low-frequency term represented by kernel regression signal. The soft thresholding technique is then applied to each residual signal to suppress noises. To illustrate the feasibility and the performance of the KRRD method, a numerical simulation and the faulty rolling element bearings of well-known open access data as well as the experimental investigations of the machinery simulator are performed. The fault detection results show that the proposed method enables the recognition of faults in mechanical systems. It is expected that the KRRD method might have a similar application prospect of EMD.

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Yan, Bingyong, Huifeng Wang, and Huazhong Wang. "Robust Fault Detection for a Class of Uncertain Nonlinear Systems Based on Multiobjective Optimization." Mathematical Problems in Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/705725.

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A robust fault detection scheme for a class of nonlinear systems with uncertainty is proposed. The proposed approach utilizes robust control theory and parameter optimization algorithm to design the gain matrix of fault tracking approximator (FTA) for fault detection. The gain matrix of FTA is designed to minimize the effects of system uncertainty on residual signals while maximizing the effects of system faults on residual signals. The design of the gain matrix of FTA takes into account the robustness of residual signals to system uncertainty and sensitivity of residual signals to system faults simultaneously, which leads to a multiobjective optimization problem. Then, the detectability of system faults is rigorously analyzed by investigating the threshold of residual signals. Finally, simulation results are provided to show the validity and applicability of the proposed approach.

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Bi, Zhan Dong, Yong Chen, Zhi Zhao Peng, and Yu Zhang. "The Demodulating Principle of Autocorrelation-Envelope and its Application to Gearbox Diagnosis." Applied Mechanics and Materials 380-384 (August 2013): 1029–34. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.1029.

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As the most important transmission system of vehicles, the gearbox has a high fault rate, so it is meaningful to evaluate and diagnose its health condition and faults accurately. Autocorrelation -envelope analysis is a fault diagnosis method that can suppress the noise and reserve the periodic components of vibration signals. A conclusion has been deduced: amplitude modulated, frequency modulated, or amplitude& frequency modulated signals can be transformed into amplitude modulated signals with the same modulation frequency through autocorrelation processing. Therefore, the aucorrelation-envelope technique is suitable for extracting the fault features of gearbox from its vibration signal with the coexistence of amplitude modulation and frequency modulation. The simulation results verify the validity of the conclusion and the experiment of vehicle gearbox diagnosis indicates the effectiveness of this method.

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Lv, Si Zhuo, Jun Wen, Si Yu Zhou, and Wen Jia Cao. "S-Transform-Based Classification of Converter Faults in HVDC System by Support Vector Machines." Applied Mechanics and Materials 347-350 (August 2013): 1308–12. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.1308.

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Based on Support Vector Machines (SVM) and S-transform, a novel approach to detect and classify various types of high voltage direct current (HVDC) converter faults is presented. An electro-magnetic transient state simulation software PSCAD/EMTDC was used to set up a simulation model of HVDC system to investigate the typical converter faults. For the good time-frequency characteristic of S-transform, S-transform is applied to obtain useful features of the non-stationary fault signals. Then fault types are identified through the pattern recognition classifier based on SVM classification tree. Numerical results show that the proposed classification method is an effective technique for building up a pattern recognition system for converter fault signals.

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Tian, Jing, Lili Liu, Fengling Zhang, Yanting Ai, Rui Wang, and Chengwei Fei. "Multi-Domain Entropy-Random Forest Method for the Fusion Diagnosis of Inter-Shaft Bearing Faults with Acoustic Emission Signals." Entropy 22, no. 1 (December 31, 2019): 57. http://dx.doi.org/10.3390/e22010057.

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Inter-shaft bearing as a key component of turbomachinery is a major source of catastrophic accidents. Due to the requirement of high sampling frequency and high sensitivity to impact signals, AE (Acoustic Emission) signals are widely applied to monitor and diagnose inter-shaft bearing faults. With respect to the nonstationary and nonlinear of inter-shaft bearing AE signals, this paper presents a novel fault diagnosis method of inter-shaft bearing called the multi-domain entropy-random forest (MDERF) method by fusing multi-domain entropy and random forest. Firstly, the simulation test of inter-shaft bearing faults is conducted to simulate the typical fault modes of inter-shaft bearing and collect the data of AE signals. Secondly, multi-domain entropy is proposed as a feature extraction approach to extract the four entropies of AE signal. Finally, the samples in the built set are divided into two subsets to train and establish the random forest model of bearing fault diagnosis, respectively. The effectiveness and generalization ability of the developed model are verified based on the other experimental data. The proposed fault diagnosis method is validated to hold good generalization ability and high diagnostic accuracy (~0.9375) without over-fitting phenomenon in the fault diagnosis of bearing shaft.

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Ji, Junjie, Jianfeng Qu, Yi Chai, Yuming Zhou, Qiu Tang, and Hao Ren. "An algorithm for sensor fault diagnosis with EEMD-SVM." Transactions of the Institute of Measurement and Control 40, no. 6 (February 1, 2017): 1746–56. http://dx.doi.org/10.1177/0142331217690579.

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Based on ensemble empirical mode decomposition (EEMD) and the support vector machine (SVM), an algorithm used in the sensor fault detection and classification is put forward in this paper. Using this method and through EEMD, the sensor signal is decomposed into several segments, including the original signals, several intrinsic mode functions (IMFs) and the residual signals. Moreover, as features of the sensor fault, their variance, mean, entropy and the slope of the original signal are calculated in accordance with the characteristics of different fault types and the inherent physical meanings of each IMF. Subsequently, the feature vectors are inputted into the SVM, which is used to classify the detection and identification of sensor faults. Finally, the simulation results of the fault diagnosis of a carbon dioxide sensor indicate that this method may not only be effectively applied to fault diagnosis of carbon dioxide sensors but also provides a reference for that of other sensors.

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Yang, Zhuan, and Ai Hua Dong. "Single-Phase-to-Earth Fault Detection for High Voltage Transmission Line Based on Wavelet Analysis." Advanced Materials Research 230-232 (May 2011): 559–63. http://dx.doi.org/10.4028/www.scientific.net/amr.230-232.559.

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The voltage and current signals are transient signals with singularity and mutability when single-phase-to-earth fault occurs in high-voltage transmission line, of which the singular point and mutations parts contain rich fault information. However, wavelet analysis just has the nature of singularity detection and zoom properties, which is very effective in singularity analysis and singular points location. Based on the basic principle of wavelet analysis and signal singularity detection, simulation and detection of singularity of fault voltage and current signals in high voltage transmission line are made by db5 wavelet multi-scale comprehensive analysis method. The singularity detection and calculation program are accomplished in M-file form by programming in MATLAB, the simulation results show that db5 wavelet can detect the singularity of fault signal and determine the fault occurrence and end time fast and accurately. This method enhanced accuracy and reliability of single-phase-to-earth fault detection.

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Swetapadma, Aleena, and Anamika Yadav. "Fuzzy Inference System Approach for Locating Series, Shunt, and Simultaneous Series-Shunt Faults in Double Circuit Transmission Lines." Computational Intelligence and Neuroscience 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/620360.

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Many schemes are reported for shunt fault location estimation, but fault location estimation of series or open conductor faults has not been dealt with so far. The existing numerical relays only detect the open conductor (series) fault and give the indication of the faulty phase(s), but they are unable to locate the series fault. The repair crew needs to patrol the complete line to find the location of series fault. In this paper fuzzy based fault detection/classification and location schemes in time domain are proposed for both series faults, shunt faults, and simultaneous series and shunt faults. The fault simulation studies and fault location algorithm have been developed using Matlab/Simulink. Synchronized phasors of voltage and current signals of both the ends of the line have been used as input to the proposed fuzzy based fault location scheme. Percentage of error in location of series fault is within 1% and shunt fault is 5% for all the tested fault cases. Validation of percentage of error in location estimation is done using Chi square test with both 1% and 5% level of significance.

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Yuan, Zhong Hu, Yang Su, and Xiao Xuan Qi. "Rolling Bearing Fault Diagnosis Research." Applied Mechanics and Materials 155-156 (February 2012): 87–91. http://dx.doi.org/10.4028/www.scientific.net/amm.155-156.87.

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According to the characteristics of the rolling bearing fault, we make the research on fault diagnosis. Time domain signal can not perform the fault feature information well. The power spectrum changes the time domain signals into the frequency signals. It sets up the new data model. It uses the principal component analysis on fault diagnosis. It uses T square statistics and Q statistics methods to make fault diagnosis. Simulation experiment results demonstrate that this method provides a high recognition rate.

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Liao, Zhiqiang, Xuewei Song, Baozhu Jia, and Peng Chen. "Automatic Bearing Fault Feature Extraction Method via PFDIC and DBAS." Mathematical Problems in Engineering 2021 (May 25, 2021): 1–13. http://dx.doi.org/10.1155/2021/6655081.

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Determining the embedded dimension of a singular value decomposition Hankel matrix and selecting the singular values representing the intrinsic information of fault features are challenging tasks. Given these issues, this work presents a singular value decomposition-based automatic fault feature extraction method that uses the probability-frequency density information criterion (PFDIC) and dual beetle antennae search (DBAS). DBAS employs embedded dimension and singular values as dynamic variables and PFDIC as a two-stage objective to optimize the best parameters. The optimization results work for singular value decomposition for bearing fault feature extraction. The extracted fault signals combined with envelope demodulation can efficiently diagnose bearing faults. The superiority and applicability of the proposed method are validated by simulation signals, engineering signals, and comparison experiments. Results demonstrate that the proposed method can sufficiently extract fault features and accurately diagnose faults.

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Zhen, Dong, Zuolu Wang, Haiyang Li, Hao Zhang, Jie Yang, and Fengshou Gu. "An Improved Cyclic Modulation Spectral Analysis Based on the CWT and Its Application on Broken Rotor Bar Fault Diagnosis for Induction Motors." Applied Sciences 9, no. 18 (September 17, 2019): 3902. http://dx.doi.org/10.3390/app9183902.

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Induction motors (IMs) are widely used in many manufacturing processes and industrial applications. The harsh work environment, long-time enduring, and overloads mean that it is subjected to broken rotor bar (BRB) faults. The vibration signal of IMs with BRB faults consists of the reliable modulation information used for fault diagnosis. Cyclostationary analysis has been found to be effective in identifying and extracting fault feature. The estimators of cyclic modulation spectrum (CMS) and fast spectral correlation (FSC) based on the short-time fourier transform (STFT) have higher cyclic frequency resolution, which has proven efficient in demodulating second order cyclostationary (CS2) signals. However, these two estimators have limitations of processing the maximum cyclic frequency αmax that is smaller than Fs/2 (Fs is the sampling frequency) according to Nyquist’s Theorem. In addition, they have lower carrier frequency resolution due to the fixed window size used in STFT. In order to resolve the initial shortcomings of the CMS and FSC methods, in this paper, we extended the analysis of CMS algorithm based on the continuous wavelet transform (CWT), which enlarged the maximum cyclic frequency range to Fs/2 and provides higher carrier frequency resolution because the CWT has the advantage of multi-resolution analysis. The reliability and applicability of the proposed method for fault components localization were validated by CS2 simulation signals. Compared to CMS and FSC methods, the proposed approach shows better performance by analyzing vibration signals between healthy motor and faulty motor with one BRB fault under 0%, 20%, 40%, and 80% load conditions.

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He, Tian, Xian Dong Liu, Ying Chun Shan, and Qiang Pan. "Rolling Element Defect Diagnosis Based on Local Mean Decomposition." Applied Mechanics and Materials 117-119 (October 2011): 33–37. http://dx.doi.org/10.4028/www.scientific.net/amm.117-119.33.

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A method to extract rolling element fault characteristics from fault signal, based on local mean decomposition (LMD) and Fourier transform (FT), is introduced in this study. The LMD’s characteristics are obtained by processing multi-component frequency and amplitude modulation signal, which are usually used to describe the bearing fault signals. Base on the simulation analysis, the envelope spectrum method called LMD-FT is presented to deal with the vibration signals of rolling balling bearing with element fault. The results show that the rolling element defect can be diagnosed by LMD-FT effectively

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Li, Feng, Xinyu Pang, and Zhaojian Yang. "Joint Amplitude and Frequency Demodulation Analysis Based on Variational Mode Decomposition for Multifault Diagnosis of a Multistage Reducer." Shock and Vibration 2018 (October 9, 2018): 1–19. http://dx.doi.org/10.1155/2018/9869561.

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Multistage reducer vibration signals have complicated spectral structures owing to the amplitude and frequency modulations of gear damage-induced vibrations and the multiplicative amplitude modulation effect caused by time-varying vibration transfer paths (in the case of local gear damage) when the multistage reducer contains both planetary and spur gears. Moreover, the difference between the vibration energies of these gears increases the difficulty of fault feature extraction when multiple failures occur in the reducer. As the meshing frequency of each gear group often varies significantly, variational mode decomposition can be performed to decompose the vibration signal according to frequency, enabling separation of the vibration signals of the spur and planetary gears. The common fault features of these gears can be extracted from the spectrum of the amplitude demodulation envelope. To verify the effectiveness of this method, we first analyzed a simulation signal, and then utilized the experimental signals from a laboratory multistage reducer for verification. In the multistage reducer simulation, we considered the amplitude and frequency modulation of the gear damage and transfer paths. In the experimental verification, we processed local faults (broken teeth) and uniform faults (uniform wear) on the sun gear and the spur gear of the planetary gear separately.

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Wang, Xiaowei, Jie Gao, Guobing Song, Qiming Cheng, Xiangxiang Wei, and Yanfang Wei. "Faulty Line Selection Method for Distribution Network Based on Variable Scale Bistable System." Journal of Sensors 2016 (2016): 1–17. http://dx.doi.org/10.1155/2016/7436841.

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Since weak fault signals often lead to the misjudgment and other problems for faulty line selection in small current to ground system, this paper proposes a novel faulty line selection method based on variable scale bistable system (VSBS). Firstly, VSBS is adopted to analyze the transient zero-sequence current (TZSC) with different frequency variety scale ratio and noise intensity, and the results show that VSBS can effectively extract the variation trends of initial stage of TZSC. Secondly, TZSC is input to VSBS for calculation with Runge-Kutta equations, and the output signal is chosen as the characteristic currents. Lastly, correlation coefficients of every line characteristic current are used as the index to a novel faulty line selection criterion. A large number of simulation experiments prove that the proposed method can accurately select the faulty line and extract weak fault signals in the environment with strong noise.

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32

Wang, Hong Chao. "Blind source extraction of rolling bearings' multi-type faults based on self-learned sparse atomics." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 233, no. 13 (February 2, 2019): 4531–42. http://dx.doi.org/10.1177/0954406219827163.

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The feature of rolling element bearings' multi-type faults is very hard to extract using common feature extraction method such as envelope demodulation, and the main reason is that there exists mutual coupling effect when multi-type faults arise in rolling element bearing synchronously. Blind source extraction originating from blind source separation is an effective method for feature extraction of rolling bearings' multi-type faults. However, the extraction result would not be ideal if blind source extraction is used directly due to the above stated mutual coupling effect. Sparse representation is a relative new signal processing method, which could capture the latent fault feature components buried in the vibration signal. So, blind source extraction of rolling element bearings' multi-type faults based on sparse representation is proposed in the paper. Firstly, the self-learned sparse atomics originating from sparse representation is applied to the multi-type faults vibration signals directly and several learned atomics are obtained. Then, the multi-type faults vibration signals are reconstructed based on the obtained learned atomics and sparse multi-type faults vibration signals are obtained. Thirdly, the blind source extraction method is applied to the reconstructed sparse vibration signals. Lastly, envelope demodulation is applied to the blind source extraction results respectively and satisfactory fault feature extraction results are obtained. The feasibility and effectiveness of the proposed method are verified through simulation and experiment.

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33

Fei, Chun Guo, and Bai Li Su. "Overhead Line Fault Location Using Wavelet Packet Decomposition and Support Vector Regression." Advanced Materials Research 516-517 (May 2012): 1396–99. http://dx.doi.org/10.4028/www.scientific.net/amr.516-517.1396.

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This paper realizes the fault location in overhead line by using wavelet packet decomposition (WPD) and support vector regression (SVR). All various types of faults at different locations and various fault inception angles on a 735kV-360km overhead line power system are used. The system only utilizes voltage signals with single-end measurements. WPD is used to extract distinctive features from 1/2 cycle of post fault signals after noises have been eliminated by low pass filter. A SVR is trained with features obtained from WPD and consequently used in precise location of fault on the transmission line. The simulation results show, fault location on transmission line can be determined rapidly and correctly irrespective of fault impedance.

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Pourazad, Hamed, Javad Askari, and Saeed Hosseinnia. "Isolating observer for simultaneous structural-actuator fault detection." International Journal of Intelligent Unmanned Systems 6, no. 3 (July 2, 2018): 118–33. http://dx.doi.org/10.1108/ijius-09-2017-0011.

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Purpose Increasing commercial applications for small unmanned aircraft create growing challenges in providing safe flight conditions. The conventional measures to detect icing are either expensive, energy consuming or heavy. The purpose of this paper is to develop a fault identification and isolation scheme using unknown input observers to detect and isolate actuator and structural faults in simultaneous occurrence. Design/methodology/approach The fault detection scheme is based on a deviation in system parameters due to icing and lock-in-place (LIP), two faults from different categories with similar indications that require different reconfiguration actions. The obtained residual signals are selected to be triggered by desired faults, while insensitive to others. Findings The proposed observer is sensitive to both actuator and structural faults, and distinguishes simultaneous occurrences by insensitivity to LIP in selected residue signals. Simulation results confirm the success of the proposed system in the presence of uncertainty and disturbance. Research limitations/implications The fault detection and isolation scheme proposed here is based on the linear model of a winged aircraft, the Aerosonde. Moreover, the faults are applied to rudder and aileron in simulations, but the design procedure for other models is provided. The designed scheme could be further implemented on a non-linear aircraft model. Practical implications Applying the proposed icing detection scheme increases detection system reliability, since fault isolation enables timely reconfiguration schemes. Originality/value The observers proposed in previous papers detected icing fault but were not insensitive to actuator faults.

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Cui, Hao Yang, Yong Peng Xu, Jun Jie Yang, Jun Dong Zeng, and Zhong Tang. "A Fault Diagnosis Method in VSC-HVDC Simulation System Based on BRBP Neural Networks." Advanced Materials Research 860-863 (December 2013): 2269–74. http://dx.doi.org/10.4028/www.scientific.net/amr.860-863.2269.

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As the feature of faulty signal in high voltage direct current transmission technology based on voltage source converter (VSC-HVDC) system is complicated to extract and its difficult to carry on the fault diagnosis. On the basis of the PSCAD simulation model of VSC-HVDC system, the DC current faulty signal is analyzed. Then, the wavelet analysis method was adopted to extract the eigenvector of faulty signal, and combined with method of Bayesian regularization back-propagation (BRBP) neural networks, the system fault was identified. The simulation results show that the method is more efficiently and more rapidly than the adding momentum BP neural network on the VSC-HVDC system faults diagnosing.

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Myint, Shwe, and Warit Wichakool. "A high frequency reflected current signals-based fault type identification method." Indonesian Journal of Electrical Engineering and Computer Science 17, no. 2 (February 1, 2020): 551. http://dx.doi.org/10.11591/ijeecs.v17.i2.pp551-563.

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The main objective of this paper is to identify fault type and faulted phase focus on the time delay values of reflected phase and modal current signals. The proposed method identifies fault type with the help of amplitude maxima of detail wavelet coefficient of residual current. The time delay values of phase and modal current reflected signals are used to detect faulty phase instead of using threshold values. Using time delay as a fault type identification parameter is achieved to save the overall protection system operating time because time delay is also the main feature of traveling wave fault location method. Moreover, to ensure the applied wavelet filter, the proposed algorithm is tested with the detail information of the three mother wavelets, such as db4, db6 and db8 and chosen the highest classification accuracy. Various disturbance events were tested with changing different possible fault types, faulted-feeders, fault resistances, fault locations and fault inception times on a loop distribution system. The robustness of the proposed faulted phase selection algorithm is performed through MATLAB Simulation.

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37

Huang, Qian, Dong Xiang Jiang, and Liang You Hong. "Application of Hilbert-Huang Transform Method on Fault Diagnosis for Wind Turbine Rotor." Key Engineering Materials 413-414 (June 2009): 159–66. http://dx.doi.org/10.4028/www.scientific.net/kem.413-414.159.

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Many signals of wind turbine faults are non-stationary and have highly complex time-frequency characteristics. Traditional time-frequency analysis method, such as Windowed Fourier Transform method, has no noticeable effect in handing non-stationary signals. Hilbert-Huang Transform (HHT) is a new signal processing method for analyzing the non-stationary mechanical signals. Based on Empirical Mode Decomposition (EMD), the Intrinsic Mode Function (IMF) in HHT can reflect the intrinsic physical characteristics of original data. Moreover, it is a good way to identify the faults involving a breakdown change. First, the principles and advantages of the HHT are presented in detail in this paper. Then, three typical faults of wind turbine rotor, such as rotor imbalance, aerodynamic asymmetry due to blade surface roughness and yaw misalignment are discussed by the HHT. Last, reasonable conclusions are drawn by the comparison between this method and the Wavelet Transform (WT) method with the help of simulation fault signals. The results show the effectiveness of HHT method for diagnosing those faults of wind turbine rotor.

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38

Gao, Fengyang, Guoheng Zhang, Mingming Li, Yunbo Gao, and Shengxian Zhuang. "Inter-turn Fault Identification of Surface-Mounted Permanent Magnet Synchronous Motor Based on Inverter Harmonics." Energies 13, no. 4 (February 18, 2020): 899. http://dx.doi.org/10.3390/en13040899.

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Inter-turn short-circuit faults can lead to further faults in motors. This makes monitoring and identifying such faults particularly important. However, because of interference in their working environment, fault signals can be weak and difficult to detect in permanent magnet synchronous motors. This paper proposes a method for overcoming this by extracting the inverter harmonics as an excitation source and then extracting characteristic of fault measurements from the negative sequence voltage. First of all, a model of permanent magnet synchronous motor faults is established and a fault negative sequence voltage is introduced to calculate the fault indicators. Then the high frequency harmonic excitation in the voltage is extracted. This is injected into the original voltage signal and the high frequency negative sequence component is separated and detected by a second-order generalized integrator. Simulation results show that the proposed method can effectively identify inter-turn short-circuit faults in permanent magnet synchronous motors while remaining highly resistant to interference. The method is especially effective when the severity of the fault is relatively small and the torque is relatively large.

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39

Zaleski, Dariusz, and Romuald Zielonko. "Two-functional µBIST for Testing and Self-Diagnosis of Analog Circuits in Electronic Embedded Systems." ACTA IMEKO 3, no. 4 (December 1, 2014): 10. http://dx.doi.org/10.21014/acta_imeko.v3i4.150.

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The paper concerns the testing of analog circuits and blocks in mixed-signal Electronic Embedded Systems (EESs), using the Built-in Self-Test (BIST) technique. An integrated, two-functional, embedded microtester (µBIST) based on reuse of signal blocks already present in an EES, such as microprocessors, memories, ADCs, DACs, is presented. The novelty of the µBIST solution is its extended functionality. It can perform 2 testing functions: functional testing and fault diagnosis on the level of localization of a faulty element. For functional testing the Complementary Signals (CSs), and for fault diagnosis the Simulation Before Test (SBT) vocabulary techniques have been used. In the fault vocabulary the graphical signatures in the form of identification curves in multidimensional spaces have been applied.

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Chen, Jian, Robert Randall, Ningsheng Feng, Bart Peeters, and Herman Van der Auweraer. "Modelling and diagnosis of big-end bearing knock fault in internal combustion engines." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 228, no. 16 (February 24, 2014): 2973–84. http://dx.doi.org/10.1177/0954406214524743.

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Big-end bearing knock is considered to be one of the common mechanical faults in internal combustion engines (IC engines). In this paper, a model has been built to simulate the effects of oversized clearance in the big-end bearing of an engine. In order to find a relationship between the acceleration response signal and the oversized clearance, the kinematic/kinetic and lubrication characteristics of the big ending bearing were studied. By adjusting the clearance, the impact forces with different levels of bearing knock fault can be simulated. The acceleration on the surface of the engine block was calculated by multiplying the simulated force spectrum by an experimentally measured frequency response function (FRF) in the frequency domain (and then inverse transforming to the time domain). As for experimentally measured vibration signals from bearing knock faults, the signal processing approach used involved calculating the squared envelopes of the simulated acceleration signals. The comparison to the experimental results demonstrated that the simulation model can correctly simulate vibration signals with different stages of bearing knock faults.

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Dong, Zhi Feng, Hui Cheng, Hui Jia Yang, Wei Fu, Ji Wei Chen, Zhi Yuan Shi, and Dong Liang Zhao. "Gearbox Fault Diagnosis Using Vibration Signal with Wavelet De-Noising." Applied Mechanics and Materials 86 (August 2011): 735–38. http://dx.doi.org/10.4028/www.scientific.net/amm.86.735.

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This paper dealt with the gearbox fault diagnosis with vibration signal analysis. The vibration signals from experiment contained a lot of noises which result from motor, gears, bears and box, and were collected through accelerate sensor, data collector and computer. The wavelet de-noising stratification was used to de-noise the vibration signals before the frequency-domain analysis was done. The effects of the simulation signal de-noising was contrasted, and the noise cancellation the power spectrum estimation was carried out. The experimental and analytical results show that the different features are indicated with vibration signal of the normal gearbox and the signal with bolts loosened of the gearbox. The gearbox fault with bolts loosened can be diagnosed by extracting the time-domain fault features of vibration signals.

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Gao, Kangping, Xinxin Xu, Jiabo Li, Shengjie Jiao, and Ning Shi. "Application of multi-layer denoising based on ensemble empirical mode decomposition in extraction of fault feature of rotating machinery." PLOS ONE 16, no. 7 (July 19, 2021): e0254747. http://dx.doi.org/10.1371/journal.pone.0254747.

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Aiming at the problem that the weak features of non-stationary vibration signals are difficult to extract under strong background noise, a multi-layer noise reduction method based on ensemble empirical mode decomposition (EEMD) is proposed. First, the original vibration signal is decomposed by EEMD, and the main intrinsic modal components (IMF) are selected using comprehensive evaluation indicators; the second layer of filtering uses wavelet threshold denoising (WTD) to process the main IMF components. Finally, the virtual noise channel is introduced, and FastICA is used to de-noise and unmix the IMF components processed by the WTD. Next, perform spectral analysis on the separated useful signals to highlight the fault frequency. The feasibility of the proposed method is verified by simulation, and it is applied to the extraction of weak signals of faulty bearings and worn polycrystalline diamond compact bits. The analysis of vibration signals shows that this method can efficiently extract weak fault characteristic information of rotating machinery.

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Zhang, Chao, Zhongxiao Peng, Shuai Chen, Zhixiong Li, and Jianguo Wang. "A gearbox fault diagnosis method based on frequency-modulated empirical mode decomposition and support vector machine." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 232, no. 2 (November 3, 2016): 369–80. http://dx.doi.org/10.1177/0954406216677102.

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During the operation process of a gearbox, the vibration signals can reflect the dynamic states of the gearbox. The feature extraction of the vibration signal will directly influence the accuracy and effectiveness of fault diagnosis. One major challenge associated with the extraction process is the mode mixing, especially under such circumstance of intensive frequency. A novel fault diagnosis method based on frequency-modulated empirical mode decomposition is proposed in this paper. Firstly, several stationary intrinsic mode functions can be obtained after the initial vibration signal is processed using frequency-modulated empirical mode decomposition method. Using the method, the vibration signal feature can be extracted in unworkable region of the empirical mode decomposition. The method has the ability to separate such close frequency components, which overcomes the major drawback of the conventional methods. Numerical simulation results showed the validity of the developed signal processing method. Secondly, energy entropy was calculated to reflect the changes in vibration signals in relation to faults. At last, the energy distribution could serve as eigenvector of support vector machine to recognize the dynamic state and fault type of the gearbox. The analysis results from the gearbox signals demonstrate the effectiveness and veracity of the diagnosis approach.

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Wang, Bing Cheng, and Zhao Hui Ren. "Study on Fault Diagnosis of Rotating Machinery Based on Lyapunov Dimension and Exponent Energy Spectrum." Advanced Materials Research 591-593 (November 2012): 2042–45. http://dx.doi.org/10.4028/www.scientific.net/amr.591-593.2042.

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In connection with the nonlinear dynamic characteristics shown from the performance of fault rotating mechanical system, the authors propose the analysis method of Lyapunov dimension and exponent energy Spectrum to the signal feature of mechanical fault. Using theory of phase space reconstruction, simulating fault signal of rotating machine is reconstructed. In order to reconstruct the phase space which can be adequately reflect the movement characteristics of the system, the time delay and embedding dimension are discussed emphatically, on this basis, calculated the Lyapunov dimension and exponential energy spectrum. From the analysis and calculation on simulation of different fault signals, it shows that under different rotating machinery fault conditions, its Lyapunov dimension and exponential energy are significantly different, which verifies that this two nonlinear feature quantities is effective parameters for fault information

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Li, Guoyan, Fangyi Li, Haohua Liu, and Dehao Dong. "Fault features analysis of a compound planetary gear set with damaged planet gears." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 232, no. 9 (April 27, 2017): 1586–604. http://dx.doi.org/10.1177/0954406217705906.

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The fault properties of compound planetary gear set are much more complicated than the simple planetary gear set. A damaged planet will induce two periodic transient impulses in the raw signals and generates modulation sidebands around the mesh harmonics. This paper aims to investigate the fault properties of a compound planetary gear set in damaged planet conditions. A dynamic model is proposed to simulate the vibration signals. The time interval between the fault-induced close impulses in the time domain is used as a significant feature to locate the faulty planet. Considering the phase relations, the time-varying mesh stiffness is obtained. Then, the fault properties are demonstrated in the simulation, and the theoretical derivations are experimentally verified.

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Zhou, Y., J. Chen, G. M. Dong, W. B. Xiao, and Z. Y. Wang. "Wigner–Ville distribution based on cyclic spectral density and the application in rolling element bearings diagnosis." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 225, no. 12 (August 15, 2011): 2831–47. http://dx.doi.org/10.1177/0954406211413215.

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The vibration signals of rolling element bearings are random cyclostationary when they have faults. Also, statistical properties of the signals change periodically with time. The accurate analysis of time-varying signals is an essential pre-requisite for the fault diagnosis and hence safe operation of rolling element bearings. The Wigner distribution is probably most widely used among the Cohen’s class in order to describe how the spectral content of a signal changes over time. However, the basic nature of such signals causes significant interfering cross-terms, which do not permit a straightforward interpretation of the energy distribution. To overcome this difficulty, the Wigner–Ville distribution (WVD) based on the cyclic spectral density (CSD) is discussed in this article. It is shown that the improved WVD, based on CSD of a long time series, can render the time–frequency distribution less susceptible to noise, and restrain the cross-terms in the time–frequency domain. Simulation and experiment of the rolling element-bearing fault diagnosis are performed, and the results indicate the validity of WVD based on CSD in time–frequency analysis for bearing fault detection.

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47

Hizam, Hashim, and P. A. Crossley. "FAULT LOCATION ON A DISTRIBUTION FEEDER USING FAULT GENERATED HIGH FREQUENCY CURRENT SIGNATURES." ASEAN Journal on Science and Technology for Development 25, no. 2 (November 22, 2017): 191–203. http://dx.doi.org/10.29037/ajstd.242.

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This paper describes how the fault generated travelling waves detected in the current signals at a single location on a distribution feeder can be used for fault location. The method identifies the fault section and the probable location of the fault by comparing the relative distance of each “peak” in the high frequency current signals to the known reflection points in the distribution feeder. The probable fault location is then used within a transient power system simulator that models the actual network. The resulting simulated current waveforms are then cross-correlated against the original signal. If the estimated fault location is correct, the high frequency signatures in the simulated waveform will be similar to those of the measured waveforms and the cross-correlation value will be a high positive value. If the signatures differ, the cross correlation value will be negative or small. The simulation and correlation process is repeated with the next “most likely” fault location until a high degree of correlation is obtained. Simulation studies using PSCAD/EMTDC and analysis using cross-correlation suggest that this method can accurately locate a fault on a distribution feeder using measurements at a single location.

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Reichert, F., and A. Petchanka. "3D CFD Arc Fault Simulation in Gas-Insulated Switchgears." PLASMA PHYSICS AND TECHNOLOGY 6, no. 1 (2019): 35–38. http://dx.doi.org/10.14311/ppt.2019.1.35.

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Arc fault processes can lead to strong damages in gas-insulated switchgears and have to be considered in the development process. In order to reduce test costs, the development of overpressure protection systems can be supported by CFD arc fault simulations. The paper deals with the modelling and simulation of arc fault processes in gas--insulated switchgears. The developed simulation tool takes into account a three-dimensional arc model and the opening of a rupture disc during the arc fault process. The influence of different insulating media as e.g. SF<sub>6</sub>, Air and CO<sub>2</sub> on the arc fault process has been investigated. The simulation model has been validated by measured signals for pressure build-up and arc voltage.

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Huang, Chun Ping. "Simulation on Fault Detection Optimization Model of Large Intelligent Electronic Inverter." Advanced Materials Research 986-987 (July 2014): 1587–90. http://dx.doi.org/10.4028/www.scientific.net/amr.986-987.1587.

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When a fault of power system occurs in grid-connected point, a serious impact is added to electronic inverter in photovoltaic micro-network internal including current, voltage and phase, the appropriate control strategies for completing fault detection of large-scale intelligent electronic inverter, is one of the currently hotspot difficulties. An engine fault diagnosis method based on PSO-SVM is proposed. Particle swarm method is used to search engine fault signals in designated space, so as to obtain optimal particle and provide the basis for an engine fault diagnosis. Support vector machine (SVM) method is applied to classify engine failure signal to complete the engine fault diagnosis. Experimental results show that the proposed algorithm for the engine fault diagnosis, can greatly improve the accuracy of diagnosis, so as to meet the needs of actual production, life, and achieve satisfactory results.

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Shen, Jian, Lun Zhang, and Niaoqing Hu. "Fault Diagnosis of Planet Gear Using Continuous Vibration Separation and Minimum Entropy Deconvolution." Applied Sciences 10, no. 22 (November 13, 2020): 8062. http://dx.doi.org/10.3390/app10228062.

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Planet gear is the most unique dynamic component in planetary gearbox. It rotates around sun gear while rotating around its own central axis, causing modulation effect in monitoring signal. Planetary gear is usually connected to heavy external loads and other transmissions, fault feature of planet gear may be overwhelmed by noises and other signals. Focused on planet gear inside planetary gearbox, a method for fault diagnosis is proposed in this paper based on continuous vibration separation (CVS) and minimum entropy deconvolution (MED). In this method, CVS is designed to separate dynamic responses of planet gear from overall vibration responses of planetary gearbox by overcoming the modulation effect and depressing noises. MED is used for enhancement detection of fault-related impulses. Simulations and experiments are conducted to collect signals for analysis. The proposed method is also compared with vibration separation method (VS). Both simulation and experiment analysis indicate that the proposed planet gear fault diagnosis method is effective. Comparative study indicates that CVS-MED method improves VS by keeping signal periodicity while overcoming modulation effect and depressing noises.

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Journal articles on the topic 'Simulation of fault signals'

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