Abstract
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).
Highlights
The suspension system is the component of a vehicle that connects the vehicle body and the wheel, which supports the car body and isolates the shock and vibration caused by road unevenness [1].The suspension system is crucial for vehicle safety and riding comfort, and plays an important role in handling and braking
Adaptive fuzzy c-means (FCM) clustering has been developed for condition monitoring, and possibilistic c-means (PCM) clustering with fault lines have been designed to isolate the faults [10,11]
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
Summary
The suspension system is the component of a vehicle that connects the vehicle body and the wheel, which supports the car body and isolates the shock and vibration caused by road unevenness [1]. Data-driven fault diagnosis methods have the advantage of not requiring a numerical model These models are susceptible to noise and varied operation conditions. 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. 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.
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