Hierarchical multi‐innovation generalised extended stochastic gradient methods for multivariable equation‐error autoregressive moving average systems

Xu, Ling; Ding, Feng; Lu, Xian; Wan, Lijuan; Sheng, Jie

doi:10.1049/iet-cta.2019.0731

Cited by 61 publications

(40 citation statements)

References 82 publications

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“…Additionally, more fault modes, such as sensor fault, are expected to be incorporated into the established model. The proposed method in this paper can combine the iterative schemes [ 48 , 49 , 50 , 51 , 52 , 53 ] and recursive schemes [ 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 ] to study the parameter identification problems of linear and nonlinear stochastic systems with colored noises [ 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 ] and to present highly efficient fault detection methods that can also be applied to the literature.…”

Section: Discussionmentioning

confidence: 99%

Fault Detection and Identification Method for Quadcopter Based on Airframe Vibration Signals

Zhang

Zhao

Wang

et al. 2021

Sensors

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Quadcopters are widely used in a variety of military and civilian mission scenarios. Real-time online detection of the abnormal state of the quadcopter is vital to the safety of aircraft. Existing data-driven fault detection methods generally usually require numerous sensors to collect data. However, quadcopter airframe space is limited. A large number of sensors cannot be loaded, meaning that it is difficult to use additional sensors to capture fault signals for quadcopters. In this paper, without additional sensors, a Fault Detection and Identification (FDI) method for quadcopter blades based on airframe vibration signals is proposed using the airborne acceleration sensor. This method integrates multi-axis data information and effectively detects and identifies quadcopter blade faults through Long and Short-Term Memory (LSTM) network models. Through flight experiments, the quadcopter triaxial accelerometer data are collected for airframe vibration signals at first. Then, the wavelet packet decomposition method is employed to extract data features, and the standard deviations of the wavelet packet coefficients are employed to form the feature vector. Finally, the LSTM-based FDI model is constructed for quadcopter blade FDI. The results show that the method can effectively detect and identify quadcopter blade faults with a better FDI performance and a higher model accuracy compared with the Back Propagation (BP) neural network-based FDI model.

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Section: Discussionmentioning

confidence: 99%

Fault Detection and Identification Method for Quadcopter Based on Airframe Vibration Signals

Zhang

Zhao

Wang

et al. 2021

Sensors

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“…A commonly used coordination method is to replace the unknown vectors in (50) and in (51) with their estimates, respectively. According to the iterative search principle, let k = 1, 2, 3, … denote the iterations index, and̂k and̂k be the estimates of and at iteration k, respectively, that is,…”

Section: Coordinationmentioning

confidence: 99%

“…Replacing s (Lh), (Lh) and in (50) with their estimateŝs ,k (Lh),̂k(Lh) and̂k −1 , and replacing n (Lh), (Lh), and in (51) with their estimateŝn ,k (Lh),̂k(Lh) and̂k, we havê…”

Section: Coordinationmentioning

confidence: 99%

Maximum likelihood hierarchical least squares‐based iterative identification for dual‐rate stochastic systems

2020

Adaptive Control & Signal

170

104

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For a dual-rate sampled-data stochastic system with additive colored noise, a dual-rate identification model is obtained by using the polynomial transformation technique, which is suitable for the available dual-rate measurement data. Based on the obtained model, a maximum likelihood least squares-based iterative (ML-LSI) algorithm is presented for identifying the parameters of the dual-rate sampled-data stochastic system. In order to improve the computation efficiency of the algorithm, the identification model of a dual-rate sampled-data stochastic system is divided into two subidentification models with smaller dimensions and fewer parameters, and a maximum likelihood hierarchical least squares-based iterative (H-ML-LSI) algorithm is proposed for these subidentification models by using the hierarchical identification principle. The simulation results indicate that the proposed algorithms are effective for identifying dual-rate sampled-data stochastic systems and the H-ML-LSI algorithm has a higher computation efficiency than the ML-LSI algorithm.

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“…Obviously, it is larger than that of the 3S-LSI algorithm. The proposed iterative algorithm for a class of observability canonical bilinear systems in this paper can combine some mathematical tools [39][40][41][42][43] to study the parameter estimation problems of different stochastic systems with colored noises [44][45][46][47][48][49][50][51] and can be applied to other fields [52][53][54][55][56][57] such as signal processing and communication networked systems [58][59][60][61] and transportation communication systems, [62][63][64][65] and engineering systems and so on.…”

Section: The Computational Efficiencymentioning

confidence: 99%

“…Take the length of the finite data L = 2000 and let the maximum iteration k max = 20. Apply the state estimator based LSI algorithm in (32) to (35) and (52) to (56) shown in Figures 2 and 3. Taking the noise variances 2 = 0.10 2 , 2 = 0.30 2 and 2 = 0.50 2 , respectively, the iterative counter k = 1,2, … ,20, the SE-LSI parameter estimates and errors with different noise variances are shown in Table 4, the parameter estimation errors = ||̂k − ||∕|| || × 100% are shown in Figure 4.…”

Section: Examplementioning

confidence: 99%

Three‐stage least squares‐based iterative estimation algorithms for bilinear state‐space systems based on the bilinear state estimator

Liu

Zhang

Ding

et al. 2020

Adaptive Control & Signal

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Because of the product item of the control input and the state vector, the identification of bilinear systems is difficult. This paper considers the combined parameter and state estimation problems of bilinear state-space systems. On the basis of the observability canonical form and the model transformation, an identification model with a linear combination of the system parameters is obtained. Using the hierarchical principle, the identification model is decomposed into three submodels with fewer variables, and a three-stage least squares-based iterative (3S-LSI) algorithm is presented to estimate the system parameters. Furthermore, we derive a state estimator (SE) for estimating the unknown states, and present an SE-3S-LSI algorithm for estimating the unknown parameters and states simultaneously. After that, the least squares-based iterative algorithm is presented as a comparison. By analyzing the estimation results and the calculation amount, these two algorithms can identify the bilinear system effectively but the 3S-LSI algorithm can improve the computational efficiency. The simulation results indicate the effectiveness of the proposed algorithms.

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Hierarchical multi‐innovation generalised extended stochastic gradient methods for multivariable equation‐error autoregressive moving average systems

Cited by 61 publications

References 82 publications

Fault Detection and Identification Method for Quadcopter Based on Airframe Vibration Signals

Fault Detection and Identification Method for Quadcopter Based on Airframe Vibration Signals

Maximum likelihood hierarchical least squares‐based iterative identification for dual‐rate stochastic systems

Three‐stage least squares‐based iterative estimation algorithms for bilinear state‐space systems based on the bilinear state estimator

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