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

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).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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