Residual Evaluation Method Research Articles

A unified framework of fault detection and diagnosis based on fractional-order chaos system

Sensors play important roles in aircraft electronic system, their faults will bring huge risks to the system safety and reliability. Considering the harsh service environment of the aircraft, a unified sensor fault detection and diagnosis scheme is proposed for the aircraft subject to strong Gaussian noise, strong non-Gaussian noise, and unknown disturbance, which is based on a fractional-order chaos system. To make the fractional-order chaos system can be used for fault detection and fault diagnosis, the structure, system parameters, and fractional-order of the system are adjusted according to the stability of the fractional-order system. The novel dynamics of the adjusted fractional-order nonlinear system are analyzed with multiple methods. Based on that, a residual evaluation method and a threshold computation method for fault detection and diagnosis are designed and formulated. A set of experiments and comparison experiments are made for verifying the effectiveness of the proposed unified fault detection and fault diagnosis scheme.

Sensor fault detection for dynamic point‐the‐bit rotary steerable system via finite‐frequency domain observer and zonotope residual evaluation

In this paper, the sensor fault detection (FD) problem is investigated for the dynamic point-the-bit rotary steerable system (DPRSS), which is known as state-of-the-art oil drilling equipment. Firstly, the DPRSS dynamic is modelled as a linear system with drilling disturbances, which consists of model perturbations, measurement disturbances, and unknown inputs. Secondly, the finite-frequency H − / H ∞ ${H_ - }/{H_\infty }$ unknown input observer is proposed to generate the residual, which is sensitive to sensor faults and robust against drilling disturbances. Subsequently, considering model perturbations and measurement disturbances, the zonotope-based residual evaluation method is presented, and the FD decision-making logic is designed based on the calculated residual bounds. Finally, numerical simulations and prototype experiments are provided to illustrate the effectiveness of the proposed sensor FD mechanism.

Dynamic Event-Triggered Hᵢ/H∞ Optimization Approach to Fault Detection for Unmanned Aerial Vehicles

This paper dealt with the problem of dynamic event-triggered fault detection (FD) for unmanned aerial vehicles (UAVs) in the Hi/H∞ optimization criterion. In order to make use of limited communication resources, a dynamic event-triggered mechanism (ETM) was considered to determine whether system information will be transmitted to the ground station. Note that the transmitted data failing to satisfy a predefined triggered condition were discarded directly. As such, FD performance was affected by not only disturbances and faults, but also dynamic event-triggered transmission errors, namely the errors between the data of non-event time and the current actual system. To solve this problem, a new dynamic event-triggered Hi/H∞ optimization approach was presented, whose contribution lied in the complete decoupling of residuals and event transmission errors under dynamic ETM, which can avoid continuous communication and Zeno phenomenon. Firstly, a dynamic ETM was presented to determine communication time. Then, a variable sampling period model of UAV was established based on the dynamic ETM. On this basis, a dynamic event-triggered residual generator was constructed. In the Hi/H∞ optimization criterion, Riccati recursion was used to compute the optimum solution of a dynamic event-triggered FD filter. Moreover, a new residual evaluation method was proposed, whose effectiveness and feasibility were finally verified by the altitude control system of a UAV.

Construction of Value Chain E-Commerce Model Based on Stationary Wavelet Domain Deep Residual Convolutional Neural Network

This paper mainly analyzes the current situation of e-commerce in domestic SMEs and points out that there are limited initial investment and difficulty in financing in China’s SMEs; e-commerce control is not scientific; e-commerce personnel of SMEs are not of high quality, in the case of improper setting of the e-commerce sector and shortage of talents, rigid management model, and outdated management concepts. By using the loss function and the value chain management theory of the deep learning in the stationary wavelet domain residual learning model, the e-commerce model of SMEs is newly constructed, and the e-commerce department as the core department of the enterprise is proposed. By training the optimal parameters of the deep residual network and comparing the results with other models, the method of this paper has a good effect against the sample. The original loss function based on the residual learning model deep learning is modified to solve the original model fuzzy problem, which improves the effect and has good robustness. Finally, based on the wavelet residual depth residual evaluation method, this paper evaluates the application effect of this model and proposes relevant suggestions for improving this model, including rationalizing and perfecting the external value chain coordination mechanism, establishing the e-commerce value chain sharing center, and promoting integration of e-commerce business, strengthening measures and recommendations in various aspects of e-commerce information construction. At last, taking the business activities of a company as an example, applying the theory described in this paper to specific practice proves the feasibility and practical value of the theory.

Fault detection, isolation and quantification from Gaussian residuals with application to structural damage diagnosis

Despite the general acknowledgment in the Fault Detection and Isolation (FDI) literature that FDI are typically accomplished in two steps, namely residual generation and residual evaluation, the second step is by far less studied than the first one. This paper investigates the residual evaluation method based on the local approach to change detection and on statistical tests. The local approach has the remarkable ability of transforming quite general residuals with unknown or non Gaussian probability distributions into a standard Gaussian framework, thanks to a central limit theorem. In this paper, the ability of the local approach for fault quantification will be exhibited, whereas previously it was only presented for fault detection and isolation. The numerical computation of statistical tests in the Gaussian framework will also be revisited to improve numerical efficiency. An example of vibration-based structural damage diagnosis will be presented to motivate the study and to illustrate the performance of the proposed method.

A hybrid implicit scheme for solving Navier–Stokes equations

SummaryA hybrid implicit scheme is developed in this study to solve 3D compressible Navier–Stokes equations on a hybrid unstructured mesh. An approximate evaluation technique for the residual term at a new time level is presented according to the analysis of the main physical mechanism introduced by the lower–upper symmetric Gauss–Seidel (LU‐SGS) relaxation‐based backward Euler implicit method. The residual evaluation method is computationally inexpensive because it does not require flux calculations for every cell surface. The approximated new time residual can be obtained without considerable computational effort; thus, a point‐implicit scheme with a multigrid property is designed to march a further time step after the previous LU‐SGS‐based backward Euler stage. This step leads to the principal ingredient of the presented two‐stage hybrid implicit scheme.To investigate the convergence behavior and performance of the two‐stage hybrid implicit scheme, 2D and 3D viscous flows around the NACA0012 airfoil, NHLP 2D multi‐airfoil, and DLR‐F6 wing–body–pylon–nacelle configuration are simulated by using different grid types. The convergence histories of the single LU‐SGS‐based backward Euler scheme and the presented two‐stage hybrid implicit scheme are analyzed and compared for each test case. The test results show that the hybrid implicit scheme is more efficient and robust than the single‐stage LU‐SGS scheme. Copyright © 2015 John Wiley & Sons, Ltd.

Adaptive Filtering for Robust Proprioceptive Robot Impact Detection Under Model Uncertainties

In the context of safe human–robot physical interaction, this paper introduces a new method for the detection of dynamic impacts of flexible-joint robot manipulators with their environment. The objective is to detect external impacts applied to the robot using only proprioceptive information with maximal sensitivity. Several model-based detection methods in robotics are based on the difference, called residual, between the estimated and the actual applied torques. Sensitivity of such methods can be limited by model uncertainties that originate either from errors on experimentally identified model parameters, possibly varying with the operating conditions, or the use of simplified models, which results in a residual dependence on the robot’s state. The main contribution of this paper consists of a new adaptive residual evaluation method that takes into account this dependence, which otherwise can lead to a tradeoff between sensitivity and false alarm rate. The proposed approach uses only proprioceptive motor-side measurements and does not require any additional joint position sensors or force/torque sensors. Dynamic effects of a collision on the residual are isolated using bandpass filtering and comparison with a state-dependent dynamic threshold. Adaptive online estimation of filter coefficients avoids the need for extensive experiments for parametric model identification. Experimental evaluation on the CEA backdrivable ASSIST robot arm illustrates the enhancement of the detection sensitivity.

Verified computations to semilinear elliptic boundary value problems on arbitrary polygonal domains

In this paper, a numerical verification method is presented for second-order semilinear elliptic boundary value problems on arbitrary polygonal domains. Based on the Newton-Kantorovich theorem, our method can prove the existence and local uniqueness of the solution in the neighborhood of its approximation. In the treatment of polygonal domains with an arbitrary shape, which gives a singularity of the solution around the re-entrant corner, the computable error estimate of a projection into the finite-dimensional function space plays an essential role. In particular, the lack of smoothness of the solution makes classical error estimates fail on nonconvex domains. By using the Hyper-circle equation, an alternative error estimate of the projection has been proposed. Additionally, a new residual evaluation method based on the mixed finite element method works well. It yields more accurate evaluation than the existing method. The efficiency of our method is shown through illustrative numerical results on several polygonal domains.

Qualitative And Neural Decision For Fault Detection And Isolation

This work focuses on a residual evaluation method that explores the symbolic-numeric integration capability provided by fuzzy sets. Qualitative information obtained from symbolic interpretation of residual responses, such as signs, is used to obtain robust isolation properties. A general framework based on fuzzy systems is proposed so that the reasoning procedure can be designed either from a qualitative analysis of the residual responses or directly by the identification of a recurrent neural network. An application to an electrical motor illustrates the proposed method.

Condition Monitoring of a Sensor Prone to Intermittent Failure

Abstract One way to detect faults or monitor process performance is to use process measurements and model predictions to generate residuals that can then be evaluated. The use of fixed threshold logic is the most straightforward method for residual evaluation. In this paper a variation on the fixed threshold scheme, involving the use of multivariate statistics, is described and applied to operating data from an industrial acid conditioning process. It is shown that the method can be used to monitor the performance of a sensor known to be unreliable.