scholarly journals Diagnostics of the drive shaft bearing based on vibrations in the high-frequency range as a part of the vehicle's self-diagnostic system

2021 ◽  
Vol 24 (1) ◽  
pp. 70-79
Author(s):  
Tomasz Nowakowski ◽  
Paweł Komorski
Keyword(s):  
Automotive Industry ◽  
Diagnostic System ◽  
Diagnostic Algorithm ◽  
Vibration Signal ◽  
Drive Shaft ◽  
Diagnostic Parameter ◽  
Limit Values ◽  
Statistical Verification ◽  
Bearing Damage ◽  
Learning Analysis

Currently, one of the trends in the automotive industry is to make vehicles as autonomous as possible. In particular, this concerns the implementation of complex and innovative selfdiagnostic systems for cars. This paper proposes a new diagnostic algorithm that evaluates the performance of the drive shaft bearings of a road vehicle during use. The diagnostic parameter was selected based on vibration measurements and machine learning analysis results. The analyses included the use of more than a dozen time domain features of vibration signal in different frequency ranges. Upper limit values and down limit values of the diagnostic parameter were determined, based on which the vehicle user will receive information about impending wear and total bearing damage. Additionally, statistical verification of the developed model and validation of the results were performed.

A Detection and Warning System for Unintended Acceleration

2015 ◽  
Author(s):  
Hongtao Yu ◽  
Reza Langari
Keyword(s):  
Mathematical Models ◽  
Diagnostic System ◽  
Warning System ◽  
Diagnostic Algorithm ◽  
Data Driven ◽  
Simulation Results ◽  
Major Factors

This paper presents a data-driven method to detect vehicle problems related to unintended acceleration (UA). A diagnostic system is formulated by analyzing several specific vehicle events such as acceleration peaks and generating corresponding mathematical models. The diagnostic algorithm was implemented in the Simulink/dSpace environment for validation. Major factors that affect vehicles’ acceleration (e.g., changes of road grades and gear shifting) were included in the simulation. UA errors were added randomly when human drivers drove virtual cars. The simulation results show that the algorithm succeeds in detecting abnormal acceleration.

scholarly journals Recognition of combustion process irregularities in small volume displacement diesel engines with the use of non-dimensional characteristics of the vibration signal

2017 ◽  
Vol 169 (2) ◽  
pp. 18-23
Author(s):  
Jerzy MERKISZ ◽  
Marek WALIGÓRSKI
Keyword(s):  
Urban Areas ◽  
Small Volume ◽  
Fuel Injection ◽  
Combustion Process ◽  
Diagnostic Algorithm ◽  
Vibration Signal ◽  
Technical Object ◽  
Operation Process ◽  
Short Time ◽  
Direct Fuel Injection

The subject of the considerations described in the paper is the problem of early detection of abnormalities and damages during operation process of the turbo diesel engine with small volume displacement and direct fuel injection, which is used in modern LDV vehicles dedicated especially for urban areas, in the context of present and future requirements for a technical object diagnostics, taking into account the criteria of optimizing overall efficiency, toxic compound emission and safety of the object in real conditions of its operation. The paper presents the results of empirical research of vibroacoustic signal application to the diagnostic evaluation of correctness of short-time engine main processes. The evaluation of the combustion process variability from structural and operational abnormalities by using dimensionless estimates of a vibration process was conducted, and functional characteristics necessary to built the diagnostic algorithm in accordance with the requirements of on-board diagnostics were obtained.

Research of Method to Diagnosing Complex Fault of Compressor in Pipeline Station

2006 ◽  
Author(s):  
Zhao-Hui Wang ◽  
Lai-Bin Zhang ◽  
Wei Liang ◽  
Lixiang Duan
Keyword(s):  
Fractal Dimension ◽  
Diagnostic System ◽  
Normal Fault ◽  
Vibration Signal ◽  
Good Tool ◽  
Fractal Method ◽  
Fault Features ◽  
Complex Fault ◽  
Integrity Management ◽  
Oil Gas

The compressor is dynamical equipment in pipeline station to deliver oil gas, it’s fault can result big accident such as stopping delivery and producing economic losing, and some fault of compressor are very complex due to the compressor’s complicated structure. Many compressor have carried simple diagnostic system, which can only diagnose normal fault, are not effective for diagnosing complex fault because these fault attributes are not obvious. This paper has researched the method to diagnose complex fault, by collecting the compressor’s vibration signals, using wavelet noise reduction technique and the fractal dimension method to process the vibration signal, which can abstract the non-obvious characteristics of complex fault effectively. The basic principle of fractal method applied in fault diagnosis is described. The result implies that the fractal dimension of good compressor is 4.4, and the fractal dimension of faulty compressor is 5.36, and fractal dimension of compressor with complex faults is 5.42. It is illustrated that this method is very effective for describing the fault features and diagnosing the complex fault of complex. This method can diagnose and predict the complex fault with a high correctness, and has been used in the Shanxi-Beijing pipeline station successfully, Which provide a good tool for pipeline’s Safety and Integrity Management.

scholarly journals Fault Diagnosis for Rolling Element Bearings Based on Feature Space Reconstruction and Multiscale Permutation Entropy

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 519 ◽  
Author(s):  
Weibo Zhang ◽  
Jianzhong Zhou
Keyword(s):  
Diagnostic Algorithm ◽  
Feature Space ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Classification Performance ◽  
Fault Classification ◽  
Permutation Entropy ◽  
Support Vector ◽  
Intrinsic Mode Functions

Aimed at distinguishing different fault categories of severity of rolling bearings, a novel method based on feature space reconstruction and multiscale permutation entropy is proposed in the study. Firstly, the ensemble empirical mode decomposition algorithm (EEMD) was employed to adaptively decompose the vibration signal into multiple intrinsic mode functions (IMFs), and the representative IMFs which contained rich fault information were selected to reconstruct a feature vector space. Secondly, the multiscale permutation entropy (MPE) was used to calculate the complexity of reconstructed feature space. Finally, the value of multiscale permutation entropy was presented to a support vector machine for fault classification. The proposed diagnostic algorithm was applied to three groups of rolling bearing experiments. The experimental results indicate that the proposed method has better classification performance and robustness than other traditional methods.

The Reliability Life Test and Analysis of Wiper Motor for Automobiles

2006 ◽  
Vol 326-328 ◽  
pp. 605-608
Author(s):  
Wae Gyeong Shin ◽  
Soo Hong Lee ◽  
Young Sik Song
Keyword(s):  
Automotive Industry ◽  
Test Procedure ◽  
Experimental Result ◽  
Visual Comfort ◽  
Life Test ◽  
Test Items ◽  
Weibull Plot ◽  
Efficient Test ◽  
Automotive Parts ◽  
Bearing Damage

Reliability of automotive parts has been one of the most interesting fields in the automotive industry. Especially wiper motor for automobiles is important part because of improving visual comfort for the driver and the passengers. Therefore, we have dealt with the reliability test procedure of wiper motor. The failures such as wear-out of brush, bearing damage, motor coil burnout are caused by the electrical and mechanical operating for low and high actions of wiper motor. In this research, we have qualitatively selected the efficient test items through the analysis of the life and potential failures of wiper motor. So, wiper motor itself requires an estimation of life causing failure of brush wear out in order to operate the system safely. We have tested six wiper motors. The result is established by employing the weibull plot. We validated the life of wiper motor to the experimental result.

Damage Identification of a Gear Transmission Using Vibration Signatures

2003 ◽  
Vol 125 (2) ◽  
pp. 394-403 ◽  
Author(s):  
F. K. Choy ◽  
D. H. Mugler ◽  
J. Zhou
Keyword(s):  
Wavelet Transform ◽  
Damage Identification ◽  
Gear Tooth ◽  
Diagnostic System ◽  
Vibration Signal ◽  
Transmission System ◽  
Gear Transmission ◽  
Signature Analysis ◽  
Gear Transmission System ◽  
Identification And Quantification

Important advancements in preventive maintenance of rotor-craft gear transmission systems are currently being sought for the development of an accurate machine health diagnostic system. Such a diagnostic system would use vibration or acoustic signals from the gear transmission system for (1) rapid on-line evaluation of gear wear or damage status, and (2) prediction of remaining gear life. Such health diagnostic capabilities would be essential for effective machine event/life management and advance warning before critical component failures. This paper demonstrates the use of vibration signature analysis procedures for health monitoring and diagnostics of a gear transmission system. The procedures used in this paper include (i) the numerical simulation of the dynamics of a gear transmission system with single and multiple tooth damage, (ii) the application of the Wigner-Ville Distribution (WVD) and the Wavelet transform in damage identification and quantification of damaged tooth based on the numerically generated vibration signal, and (iii) the application of both WVD and the Wavelet transform on experimental data at various stage of gear failure obtained from an accelerated gear damage test rig. This paper demonstrates that the developed signature analysis procedure can successfully detect faulty gears in both numerically simulated and experimental tested transmission system. General conclusions on identification and quantification of gear tooth damage are drawn based on the results of this study.

Study on Unified Chaotic System-Based Wind Turbine Blade Fault Diagnostic System

2015 ◽  
Vol 25 (03) ◽  
pp. 1550042 ◽  
Author(s):  
Ying-Che Kuo ◽  
Chin-Tsung Hsieh ◽  
Her-Terng Yau ◽  
Yu-Chung Li
Keyword(s):  
Wind Turbine ◽  
Turbine Blade ◽  
Chaotic System ◽  
Wind Turbines ◽  
Diagnostic System ◽  
Vibration Signal ◽  
Wind Turbine Blade ◽  
Operating Conditions ◽  
Unified Chaotic System ◽  
Fault Diagnostic

At present, vibration signals are processed and analyzed mostly in the frequency domain. The spectrum clearly shows the signal structure and the specific characteristic frequency band is analyzed, but the number of calculations required is huge, resulting in delays. Therefore, this study uses the characteristics of a nonlinear system to load the complete vibration signal to the unified chaotic system, applying the dynamic error to analyze the wind turbine vibration signal, and adopting extenics theory for artificial intelligent fault diagnosis of the analysis signal. Hence, a fault diagnostor has been developed for wind turbine rotating blades. This study simulates three wind turbine blade states, namely stress rupture, screw loosening and blade loss, and validates the methods. The experimental results prove that the unified chaotic system used in this paper has a significant effect on vibration signal analysis. Thus, the operating conditions of wind turbines can be quickly known from this fault diagnostic system, and the maintenance schedule can be arranged before the faults worsen, making the management and implementation of wind turbines smoother, so as to reduce many unnecessary costs.

A Study on Multi Fault Diagnostics of Smart Unmanned Aerial Vehicle Propulsion System Using Data Sorting and Neural Networks

2008 ◽  
Author(s):  
Changduk Kong ◽  
Seonghee Kho ◽  
Jayoung Ki ◽  
Changho Lee
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Diagnostic System ◽  
Diagnostic Algorithm ◽  
Propulsion System ◽  
Performance Simulation ◽  
Network Algorithms ◽  
The Neural Network ◽  
Aerial Vehicle ◽  
Using Data

The types and severities of most engine faults are so complex that it is not easy for a conventional model based fault diagnosis approach like the GPA (Gas Path Analysis) method be used to monitor all engine fault conditions. This study therefore discusses on the newly proposed diagnostic algorithm for isolating and effectively identifying the faulted components of the smart UAV propulsion system, that has been developed by KARI (Korea Aerospace Research Institute) based on the fuzzy logic and the neural network algorithms. The diagnosis procedure of the proposed diagnostic system has the following steps. First obtaining database of fault patterns through performance simulation, followed by training the database using the FFBP networks. The third step involve analyzing the trend of the measured parameters due to fault patterns, linked to this is the fourth step that involve isolating the faulty components using fuzzy logic, and finally the magnitudes of the detected faults are obtained by the trained neural networks. The analysis showed that the detected faults had almost same degradation values to those of the implanted fault pattern, confirming that the proposed diagnostic system can be used to effectively detect the engine faults.

Defect Diagnostics of Power Plant Gas Turbine Using Hybrid SVM-ANN Method

2012 ◽  
Author(s):  
Sangmyeong Lee ◽  
Sanghun Lee ◽  
Juchang Lim ◽  
Sangbin Lee
Keyword(s):  
Power Plant ◽  
Gas Turbine ◽  
Hybrid Approach ◽  
Diagnostic System ◽  
Diagnostic Algorithm ◽  
Estimation Accuracy ◽  
Support Vector ◽  
Defect Diagnostics ◽  
Efficiency Estimation ◽  
Artificial Neural Network Ann

A hybrid method of an artificial neural network (ANN) combined with a support vector machine (SVM) has been developed for the defect diagnostic system applied to the power plant gas turbine. This method has been suggested to overcome the demerits of the general ANN with the local minima problem and low classification accuracy in case of many nonlinear data. This hybrid approach takes advantage of the reduction of learning data and converging time without any loss of estimation accuracy therefore it has been applied for the power plant monitoring system in order to detect fails and status of compressors and turbines in detail. The results have shown the suggested defect diagnostic algorithm has reliable and suitable efficiency estimation accuracy.

Development of Technical Diagnostic System for Internal Combustion Engines

2019 ◽  
Vol 16 (11) ◽  
pp. 4569-4572
Author(s):  
Lenar Ajratovich Galiullin ◽  
Rustam Asgatovich Valiev ◽  
Ilnar Ajratovich Galiullin
Keyword(s):  
World War I ◽  
Automotive Industry ◽  
Automobile Industry ◽  
Internal Combustion Engines ◽  
Diagnostic System ◽  
Internal Combustion ◽  
Economic Life ◽  
Combustion Engines ◽  
Diagnostic Systems ◽  
Leading Role

This article describes methods of development of technical diagnostic systems for internal combustion engines. The automotive industry plays a leading role in the economy of any state. The history of the development of the global automotive industry is closely linked with the development of many branches of engineering. So, by the beginning of the 20th century, the automobile industry began to consume half of the steel and iron produced, three-quarters of rubber and leather, a third part of nickel and aluminum, and a seventh part of wood and copper. Autobuilding came in first place in terms of production among other branches of engineering, began to have a serious impact on the economic life of states. By the beginning of World War I, the car park on the globe was about 2 million. Of these, 1.3 million were in the USA, 245 thousand in England, 100 thousand in France, 57 thousand in Austria-Hungary, 12 thousand—to Italy, 10 thousand—to the Russia.

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