Time-varying and anti-disturbance fault diagnosis for a class of nonlinear systems

Abstract

The diagnosis algorithm of time-varying failures is considered for a class of nonlinear systems that are affected by external disturbances. By combining the adaptive control theory and the approach of state observer, an anti-disturbance fault diagnosis algorithm has been proposed. When the external disturbances and the internal failures exist simultaneously, the designed fault diagnosis algorithm is able to give specific estimated values of states and failures, respectively, rather than just give a fault warning. The asymptotical stability of the state observer in diagnosis algorithm is guaranteed by setting the adaptive adjusting law of the time-varying failure vector. In addition, a theoretically rigorous proof based on Lyapunov's stability theory has been given. Three experiments have been implemented to evaluate the effectiveness of fault diagnosis algorithm. The experimental results have demonstrated that the performance is satisfactory in both estimation accuracy and convergence speed.

State prediction for the actuators of civil aircraft based on a fusion framework of relevance vector machine and autoregressive integrated moving average

“…In Figure 3, a i represents hyper parameter, and s 2 stands for the variance of Gaussian distribution which have been defined in formula (6).…”

“…5 However, because the actual industrial systems are generally complicated and cannot be modeled precisely, the model-based approaches have significant limitations when adopted for practical prediction. 6,7 Comparatively, the data-driven technique can avert the dependence on the system's analytical model, so that it can extract necessary information from huge amounts of recorded process data without modeling, which is then used for real-time state prediction.…”

State observer for linear system with unknown input disturbance and sampled and delayed output with measurement noise

“…Remark 1. It is worth mentioning that the high-order disturbance is a particular case of Type I disturbance and has been investigated widely, for instance as constant disturbance if S = 0 and H = 1 as discussed in Su et al (2016) and Han (2009) and as the r th polynomial disturbance if S = [ left left O false( r − 1 false) × 1 left I r − 1 left1 0 left O 1 × false( r − 1 false) ] and H = [ left left 1 left O 1 × false( r − 1 false) ] as considered in Su et al (2015), Guo et al (2015) and Guo et al (2016).…”

Fault Diagnosis for Actuators in a Class of Nonlinear Systems Based on an Adaptive Fault Detection Observer

“…The main contributions of this paper lie in three aspects. Firstly, different from the existing papers, the fault diagnosis algorithm in this paper is not based on ideal analytical models; in other words, the random measurement noise and external disturbances are taken into account simultaneously during the whole development process [19]. The designed fault diagnosis algorithm can successfully separate the measurement noise generated by the sensors, the unknown external disturbances, and the failures when they exist at the same time.…”