Investigation into on-road vehicle parameter identification based on subspace methods

Dong, Guangming; Chen, Jin; Zhang, Nong

doi:10.1016/j.jsv.2014.07.012

Cited by 19 publications

(14 citation statements)

References 26 publications

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“…Monte Carlo tests were performed 10 times for each damping ratio, and corresponding mean value of the identification error is shown in Figure 3. Due to the negative influence of high damping ratio on the identification results, the stochastic subspace identification is rarely applied in the case of high damping ratios [30]. The damping ratio of a vehicle's suspension system is higher than that of other mechanical systems, and the highest value is even up to 20-30%.…”

Section: Effect Of Damping On Identification Resultsmentioning

confidence: 99%

“…Chen et al [28,29] used the average correlated stochastic subspace method (ASC-SSI) to identify the operating modal of the frame online, obtained the frame modal parameters under constraint conditions, and matched the relevant components according to the identification result. Dong et al [30] used the stochastic subspace method to identify vehicle modal parameters and calculate the variation in vehicle inertia parameters. This study illustrates that the noise and high damping ratio (20-30%) greatly influences the identification results of the stochastic subspace method.…”

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confidence: 99%

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Failure Identification of Dump Truck Suspension Based on an Average Correlation Stochastic Subspace Identification Algorithm

Liu

Wang

et al. 2018

Applied Sciences

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This paper proposes a fault identification method based on an improved stochastic subspace modal identification algorithm to achieve high-performance fault identification of dump truck suspension. The sensitivity of modal parameters to suspension faults is evaluated, and a fault diagnosis method based on modal energy difference is established. The feasibility of the proposed method is validated by numerical simulation and full-scale vehicle tests. The result shows that the proposed average correlation signal based stochastic subspace identification (ACS-SSI) method can identify the fluctuation of vehicle modal parameters effectively with respect to different spring stiffness and damping ratio conditions, and then fault identification of the suspension system can be realized by the variation of the modal energy difference (MED).Data-driven fault diagnosis methods have the advantage of not requiring a numerical model. However, these models are susceptible to noise and varied operation conditions. Unfortunately, because of the poor working environment and complex operation conditions, the noise is strong and excitation from the road is intensive and varied, so data-driven methods have not been well suited to the failure identification of dump truck suspension to date.By contrast, in model-based fault diagnosis methods, a precise model is needed to be built [12][13][14][15][16][17]. Several model-based fault diagnosis methods are implemented in research; a Kalman-filter based fault diagnosis method with high efficiency has been developed [18], and a distributed-observer based fault detection and an interaction multi-model approach are proposed respectively for the damper and spring [19,20]. However, the difficulty makes it hard to obtain the parameters of a dynamic system for modeling.Identification of modal parameters is an approach to obtain the dynamic characteristic of a system [21,22], and it can acquire accurate dynamic parameters via test signals, such as subspace identification (SI) [23,24]. Moaveni et al.[25] used deterministic subspace identification (DSI) to identify the modal parameters of nonlinear systems, and proposed a method of determining the nonlinear dynamic characteristics based on the identification of deterministic subspace and time-varying modal parameters. Moreover, the output-only identification methods can recognize the mode using the response only [26]. Sarmadi et al. [27] proposed a recursive adaptive stochastic subspace method to monitor the modes of the dynamic system online using wide-area synchronous vector data. Chen et al. [28,29] used the average correlated stochastic subspace method (ASC-SSI) to identify the operating modal of the frame online, obtained the frame modal parameters under constraint conditions, and matched the relevant components according to the identification result. Dong et al. [30] used the stochastic subspace method to identify vehicle modal parameters and calculate the variation in vehicle inertia parameters. This study illustrates that the noise and high damp...

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Section: Effect Of Damping On Identification Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

Failure Identification of Dump Truck Suspension Based on an Average Correlation Stochastic Subspace Identification Algorithm

Liu

Wang

et al. 2018

Applied Sciences

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“…Conventionally, the stabilization diagram is constructed by the increments of system order at a fixed row number. However, recent studies [10,14,15] show that the efficiency and accuracy of identification also depend on the variation of the row number of the Hankel matrix and hence lead to an alternative stabilization diagram that is formed by consecutive increments of the row number at a fixed order and show more effectiveness, compared with the conventional one. However, it needs to specify first which is usually unknown for most applications.…”

Section: Reference-based Stochastic Subspace Identification (Ssi/ref)mentioning

confidence: 99%

“…In comparative studies, the reference-based stochastic subspace identification (SSI/ref) [7] method was deemed to be a more accurate, robust, and efficient identification for OMA [9] and has been intensively explored recently in the field of ambient vibration data based modal identification. Moreover, by considering the road excitations as random inputs, subspace identification methods were investigated to estimate the vehicle handling dynamic model and predict the vehicle handling performances [10,11] using data from road tests. Therefore, this study is also based on this approach to implement the online analysis of frame dynamic responses.…”

Section: Introductionmentioning

confidence: 99%

Characterizing the Dynamic Response of a Chassis Frame in a Heavy-Duty Dump Vehicle Based on an Improved Stochastic System Identification

Chen

Wang

et al. 2015

Shock and Vibration

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This paper presents an online method for the assessment of the dynamic performance of the chassis frame in a heavy-duty dump truck based on a novel stochastic subspace identification (SSI) method. It introduces the use of an average correlation signal as the input data to conventional SSI methods in order to reduce the noisy and nonstationary contents in the vibration signals from the frame, allowing accurate modal properties to be attained for realistically assessing the dynamic behaviour of the frame when the vehicle travels on both bumped and unpaved roads under different operating conditions. The modal results show that the modal properties obtained online are significantly different from the offline ones in that the identifiable modes are less because of the integration of different vehicle systems onto the frame. Moreover, the modal shapes between 7 Hz and 40 Hz clearly indicate the weak section of the structure where earlier fatigues and unsafe operations may occur due to the high relative changes in the modal shapes. In addition, the loaded operations show more modes which cause high deformation on the weak section. These results have verified the performance of the proposed SSI method and provide reliable references for optimizing the construction of the frame.

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“…Physical parameters are essential for the dynamic modeling and analysis of road vehicles [1], but in practice they are very difficult to obtain. Therefore, methods to estimate these parameters are of great interest to researchers as well as to the industry [2][3][4][5]. Physical parameter identification is the second type of dynamical inverse problem.…”

Section: Introductionmentioning

confidence: 99%

A New Physical Parameter Identification Method for Two-Axis On-Road Vehicles: Simulation and Experiment

Zheng

Peng

Zhang

et al. 2015

Shock and Vibration

Self Cite

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A new physical parameter identification method for two-axis on-road vehicle is presented. The modal parameters of vehicle are identified by using the State Variable Method. To make it possible to determine the matricesM,C, andKof the vehicle, a known mass matrixΔMis designed to add into the vehicle in order to increase the number of equations ensuring that the number of equations is more than the one of unknowns. Therefore, the physical parameters of vehicle can be estimated by using the least square method. To validate the presented method, a numerical simulation example and an experiment example are given in this paper. The numerical simulation example shows that the largest of absolute value of percentage error is 1.493%. In the experiment example, a school bus is employed in study for the parameter identification. The simulation result from full-car model with the estimated physical parameters is compared with the test result. The agreement between the simulation and the test proves the effectiveness of the proposed estimation method.

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Investigation into on-road vehicle parameter identification based on subspace methods

Cited by 19 publications

References 26 publications

Failure Identification of Dump Truck Suspension Based on an Average Correlation Stochastic Subspace Identification Algorithm

Failure Identification of Dump Truck Suspension Based on an Average Correlation Stochastic Subspace Identification Algorithm

Characterizing the Dynamic Response of a Chassis Frame in a Heavy-Duty Dump Vehicle Based on an Improved Stochastic System Identification

A New Physical Parameter Identification Method for Two-Axis On-Road Vehicles: Simulation and Experiment

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