The validation of an ACS-SSI based online condition monitoring for railway vehicle suspension systems using a SIMPACK model

Liu, Fulong; Gu, Fengshou; Ball, Andrew; Zhao, Yunshi; Peng, Bo

doi:10.23919/iconac.2017.8082030

Cited by 5 publications

(5 citation statements)

References 14 publications

(12 reference statements)

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“…Based on this theory, a method employing the correlation signals and combined with the framework of covariance driven SSI (Cov-SSI), denoted as ACS-SSI, was proposed in [19,20]. e main steps of ACS-SSI are listed as follows [19][20][21]26]:…”

Section: Cos-ssi Methodsmentioning

confidence: 99%

“…For instance, empirical mode decomposition (EMD) was combined with SSI to extract modal parameters of civil structure from nonstationary signals [18]. What is more, a preprocess step was conducted in [19][20][21] by averaging the obtained correlation signals, which is denoted as average correlation signal-based SSI (ACS-SSI). e effectiveness of ACS-SSI has been proved by the experimental study of the extraction of the modal parameters of a chassis frame of a heavy-duty dump vehicle under normal operational condition.…”

Section: Introductionmentioning

confidence: 99%

“…e average step will thus enhance the contents with regular or periodic components by suppressing the irregular random contents in different data records; the regular and periodic components contain the information of modal parameters [19,20]. e effectiveness of this method has been proved in [19][20][21]26]. However, the amplitudes of the correlation signals can be spread in an extremely wide dynamic range due to the strong nonstationary characteristics of the responses, whereas the ACS-SSI method helps in averaging all correlation signals at one time and therefore, takes less account to the signals with low amplitudes.…”

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confidence: 99%

See 2 more Smart Citations

An Introduction of a Robust OMA Method: CoS‐SSI and Its Performance Evaluation through the Simulation and a Case Study

Liu

et al. 2019

Shock and Vibration

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Operational modal analysis (OMA) is a powerful vibration analysis tool and widely used for structural health monitoring (SHM) of various system systems such as vehicles and civil structures. Most of the current OMA methods such as pick-picking, frequency domain decomposition, natural excitation technique, stochastic subspace identification (SSI), and so on are under the assumption of white noise excitation and system linearity. However, this assumption can be desecrated by inherent system nonlinearities and variable operating conditions, which often degrades the performance of these OMA methods in that the modal identification results show high fluctuations. To overcome this deficiency, an improved OMA method based on SSI has been proposed in this paper to make it suitable for systems with strong nonstationary vibration responses and nonlinearity. This novel method is denoted as correlation signal subset-based SSI (CoS-SSI) as it divides correlation signals from the system responses into several subsets based on their magnitudes; then, the average correlation signals with respective to each subset are taken into as the inputs of the SSI method. The performance of CoS-SSI was evaluated by a simulation case and was validated through an experimental study in a further step. The results indicate that CoS-SSI method is effective in handling nonstationary signals with low signal to noise ratio (SNR) to accurately identify modal parameters from a fairly complex system, which demonstrates the potential of this method to be employed for SHM.

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Section: Cos-ssi Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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An Introduction of a Robust OMA Method: CoS‐SSI and Its Performance Evaluation through the Simulation and a Case Study

Liu

et al. 2019

Shock and Vibration

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“…It was found that the experimental result matched the analytical model with errors of 12.5%, 5.3% and 6.1% for the bounce, pitch, and roll frequencies, respectively. Referring to table 1, the cost of the sensor and DAQ system is very high, and the experiment was conducted to monitor the state of a single component (spring) in the suspension system [5].…”

Section: Introductionmentioning

confidence: 99%

Reducing cost with MEMS sensor and improving performance of classifier using probabilistic voting method

Balaji,

Sugumaran

2023

Meas. Sci. Technol.

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When developing a solution for fault diagnosis, cost is a key factor that must be considered to ensure a practical and feasible final product. In an effort to reduce costs, the authors conducted a feasibility study using a low-cost MEMS accelerometer in conjunction with an Arduino Uno. To verify the performance of this setup, a study was carried out using a MEMS sensor and the Arduino Uno as the data acquisition system in fault diagnosis applications. The maximum sampling rate of the sensor and Arduino Uno was used to set the data acquisition parameters, However optimal sample length studies were lacking. Therefore, this study aimed to determine the effect of sample length on fault classification by varying it from 50 to 5000 to identify the optimal value for this particular application.After finalizing the parameters, the vibration signal was acquired using the MEMS sensor and Arduino Uno. The resulting classification accuracy using decision tree was 90.2%, a satisfactory result that can be further improved for industrial applications. To enhance the accuracy of decision tree classifiers, the authors proposed a novel approach using probabilistic voting method.To demonstrate the feasibility of utilizing MEMS sensors for the fault diagnosis of suspension systems in automobiles, a case study was conducted using a vibration signal as a typical diagnostic scenario. The study also determined the optimal sample length required for this application. Additionally, a novel method, the Probabilistic Voting Method, was proposed to enhance performance. Revolutionizing the monitoring system, the utilization of an Arduino Uno and MEMS sensor (ADXL335) drastically cut down costs, while achieving a remarkable classification accuracy of 90.2%. The implementation of the Probabilistic Voting Method further elevated the accuracy to an astounding 98.5%, setting a new benchmark in the field.

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“…With the rapid development of high-speed railway transportation in recent years, modern railway vehicle safety and ride comfort [1][2] becomes an essential subject in the context of vehicle system dynamics research, and Multibody System (MBS) dynamics modelling and simulation [3][4] are frequently used in the vehicle system vibration analysis and suspension optimization.…”

Section: Introductionmentioning

confidence: 99%

Multibody system dynamics modelling and simulation of a high-speed train for its suspension optimization

Wu,

Liu,

et al. 2023

Vib. proced.

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Multibody system (MBS) dynamics modelling and simulation of high-speed train system is essential and meaningful in the context of train dynamics research and product design. In this work, MBS modelling of a CRH high-speed train is carried out, in the modelling process, nonlinear geometry of wheel-rail contact, nonlinear characteristics of the suspensions and nonlinear railroad irregularities are fully considered. With the obtained MBS model, simulation is performed and demonstrates that critical speed of the CRH train is 378 km/h, vertical ride comfort of the CRH train is in “Excellent” level and lateral ride comfort is in “Good” level, in addition, RMS derailment coefficient of the train is less than 0.5 which means curving performance of the train is good. Agreement of simulation result with actual test or running data validates the established MBS model, thus, the MBS model developed in this study is accurate and would be useful in the next step vibration analysis and suspension optimization of the CRH train.

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The validation of an ACS-SSI based online condition monitoring for railway vehicle suspension systems using a SIMPACK model

Cited by 5 publications

References 14 publications

An Introduction of a Robust OMA Method: CoS‐SSI and Its Performance Evaluation through the Simulation and a Case Study

An Introduction of a Robust OMA Method: CoS‐SSI and Its Performance Evaluation through the Simulation and a Case Study

Reducing cost with MEMS sensor and improving performance of classifier using probabilistic voting method

Multibody system dynamics modelling and simulation of a high-speed train for its suspension optimization

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