## 1 Introduction  
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 also plays an important role in handling and braking. Due to a poor working environment and complex operating conditions, failures often occur during in dump truck suspension systems in operation, which have a huge impact on the efficiency and reliability of fleet operations, and also endanger property and driver safety [2]. Therefore, methods for condition monitoring of the vehicle suspension system have attracted much attention [3; 4; 5; 6]. There are two main types of fault diagnosis algorithms: data-driven methods and model-based methods. If the diagnosis process does not require a physical model and instead depends on measured data, it is referred to as a data-driven fault diagnosis method [7]; for example, independent component analysis (ICA), and principle component analysis (PCA). Furthermore, many methods are published to be applied in the fault diagnosis of suspension systems. [PERSON] et al. combine k-means clustering with Fisher discriminant analysis (FDA) to conduct fault diagnoses of suspension systems [8]. Likewise, canonical vitiate analysis (CVA) and dynamical principle components analysis (DPCA) are applied to detect early faults [9]. Moreover, 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]. 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]. [PERSON] 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]. [PERSON] 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. [PERSON] 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. [PERSON] 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.