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Intelligent Sound-Based Early Fault Detection System for Vehicles

Computer Systems Science & Engineering 2023年第9期46卷 3175-3190页

作者： Fawad Nasim Sohail Masood Arfan Jaffar Usman Ahmad Muhammad Rashid Superior University LahorePakistan Intelligent Data Visual Computing Research(IDVCR) LahorePakistan National University of Technology IslamabadPakistan

An intelligent sound-based early fault detection system has been proposed for vehicles using machine learning.The system is designed to detect faults in vehicles at an early stage by analyzing the sound emitted by the car.Early detection and correction of defects can improve the efficiency and life of the engine and other mechanical parts.The system uses a microphone to capture the sound emitted by the vehicle and a machine-learning algorithm to analyze the sound and detect faults.A possible fault is determined in the vehicle based on this processed sound.Binary classification is done at the first stage to differentiate between faulty and healthy cars.We collected noisy and normal sound samples of the car engine under normal and different abnormal conditions from multiple workshops and verified the data from experts.We used the time domain,frequency domain,and time-frequency domain features to detect the normal and abnormal conditions of the vehicle correctly.We used abnormal car data to classify it into fifteen other classical vehicle problems.We experimented with various signal processing techniques and presented the comparison results.In the detection and further problem classification,random forest showed the highest results of 97%and 92%with time-frequency features.

关键词： Sound classification signal processing random forest random tree time-frequency domain J48

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The Dynamic Relationship of China’s Financial Sub-Markets:From the Perspective of Risk and Stability

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Social Sciences in China 2022年第2期43卷 180-208页

作者： Liu Bai Zhang Ailian Pan Mengmeng School of Business and Management Jilin University

The development of financial technology has made financial sub-markets increasingly interconnected,and this interdependence magnifies the instability of financial markets and the possibility of risk.The literature documents the relation of financial sub-markets from time domain,but empirical evidence that effectively identify the patterns of co-movement of multiple financial sub-markets from frequency domain is lacking.This study assesses the dynamic relationship among interest rates,stock prices and exchange rates in China from January 2006 to December 2021 using the Wavelet model.Furthermore,we introduce the TVP-VAR-SV model to study whether the dynamic relationship has changed structurally under the impact of COVID-19.We find the following:1) with the deepening of the financial market and the improvement of the informatization level,the frequency of risk transmission among financial sub-markets decreases,and the linkage relationship changes from frequent linkage in the medium term to relatively stable linkage relationship in the long term;2) the relationship between the three variables in short-term fluctuations is more complex,while the relationship between financial variables in long-term fluctuations is more stable;3) after the outbreak of COVID-19,the positive impact of interest rates and stock prices has brought about a larger range of changes in exchange rate volatility,with a longer impact period and a stronger linkage.

关键词： financial sub-market financial risk financial stability time-frequency domain

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Parametric identification of time-varying systems from free vibration using intrinsic chirp component decomposition

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Acta Mechanica Sinica 2020年第1期36卷 188-205页

作者： Sha Wei Shiqian Chen Xingjian Dong Zhike Peng Wenming Zhang School of Mechanics and Engineering Science Shanghai UniversityShanghai200072China Shanghai Institute of Applied Mathematics and Mechanics Shanghai UniversityShanghai200072China Shanghai Key Laboratory of Mechanics in Energy Engineering Shanghai UniversityShanghai200072China State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong UniversityShanghai200240China

time-varying systems are applied extensively in practical applications,and their related parameter identification techniques are of great significance for structural health monitoring of time-varying systems.To improve the identification accuracy for time-varying systems,this study puts forward a novel parameter identification approach in the time-frequency domain using intrinsic chirp component decomposition(ICCD).ICCD is a powerful tool for signal decomposition and parameter extraction,with good signal reconstruction capability in a high-noise environment.To maintain good identification effects for the time-varying system in a noisy environment,the proposed method introduces a redundant Fourier model for the non-stationary signal,including instantaneous frequency(IF)and instantaneous amplitude(IA).The accuracy and effectiveness of the proposed approach are demonstrated by a single-degree-of-freedom system with three types of time-varying parameters,as well as an example of a multi-degree-of-freedom system.The effects of different levels of measured noise on the identified results are also discussed in detail.Numerical results show that the proposed method is very effective in tracking the smooth,periodical,and non-smooth variations of time-varying systems over the entire identification time period even when the response signal is contaminated by intense noise.

关键词： System identification time-varying system time-frequency domain Intrinsic chirp component decomposition

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Enhancing the resolution of seismic data based on the generalized S-transform

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Petroleum Science 2009年第2期6卷 153-157页

作者： Tian Jianhua Song Wei Yang Feizhou State Key Laboratory of Petroleum Resource and Prospecting China University of Petroleum Beijing 102249 China Key Laboratory of CNPC Geophysical Prospecting China University of Petroleum Beijing 102249 China

In this paper, we analyze the seismic signal in the time-frequency domain using the generalized S-transform combined with spectrum modeling. Without assuming that the reflection coefficients are random white noise as in the conventional resolution-enhanced techniques, the wavelet which changes with time and frequency was simulated and eliminated. After using the inverse S-transform for the processed instantaneous spectrum, the signal in the time domain was obtained again with a more balanced spectrum and broader frequency band. The quality of seismic data was improved without additional noise.

关键词： time-frequency domain generalized S-transform spectrum modeling instantaneous spectrum balanced spectrum

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An Improved Flux and Torque Estimation Strategy of Speed Sensorless Induction Motor

An Improved Flux and Torque Estimation Strategy of Speed Sen...

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2009中国控制与决策会议

作者： Pang Peilin,Ding Guangbin Hebei University of Engineering,Handan 056038,China

＜正＞The direct torque control has emerged recently as a popular control technique for high performance induction motor drives by means of machine parameters minimization,algorithm complexity reduction and linearization feedback.The induction motor torque effectively relies on constructing the stator flux which is closely related to the voltage drop on the stator resistance.A new method of stator resistance identification using wavelet network is brought forward to improve the low-speed dynamic performance of induction motor. Considering advantages of wavelet transform in time-frequency domain,the combined information can be obtained from both magnitudes and arguments of complex coefficients.Then the time-varying signal characteristic will be extracted and increases the control precision for direct torque control.The improved orthogonal least squares algorithm is used to complete wavelet network training and parameter initialization, and then the accurate stator flux vector and electromagnetic torque are acquired,optimizing the inverter control strategy.During training phase,the wavelet network not only learns adequate decision functions defined by the weight coefficients,but also looks for parameter space which fits for reliable input signals.The simulations as well as experimental results are shown to illustrate the improved performance of the proposed method, indicating torque ripple reduction and system stability.

关键词： Induction motor control motor drive linearization feedback wavelet network time-frequency domain combined information flux ripple

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Wavelet Filtered Regression as A Novel System Identification Method

Wavelet Filtered Regression as A Novel System Identification...

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2010亚太航空航天技术研讨会

作者： Masaru Naruoka Takuma Hino Takeshi Tsuchiya Department of Aeronautics and Astronautics The University of Tokyo

＜正＞In this paper,a new method of the system identification to acquire flight characteristic of aircraft is proposed.The goal of this study is to expand the usage of flight characteristic of aircraft acquired by system identification,such as in safety mechanisms of defective aircraft. In order to do so,time-frequency information provided by wavelet transform and special filtering are introduced.This will enhance the robustness against disturbances than conventional methods.To evaluate the proposed method,a small unmanned aerial vehicle（UAV） is utilized.Small UAVs are more easily disturbed by wind gust than general aircraft.The effectiveness of the proposed method is highlighted by the success in acuiring flight characteristic of the small UAV.

关键词： Wavelet Transform System Identification Small UAV Stability Derivatives time-frequency domain

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