System Fault Modes Research Articles

A Three-State Space Modeling Method for Aircraft System Reliability Design

Reliability is an inherent attribute of a system through optimal system design. However, during the aircraft system development process, the reliability evaluation and system function design efforts are often disconnected, leading to a divide between reliability experts and system designers in their work schedule. This disconnect results in an inefficient aircraft system reliability optimization process, known as the “two-skin” phenomenon. To address this issue, a three-state space model is proposed. Firstly, an analysis was conducted on the relationship between the system function architecture developed by the system designers and the reliability evaluation performed by the reliability experts. Secondly, based on the principle of function flow, the state of failure was categorized into “physical failure” and “non-physical failure”. Additionally, a new state of “function loss” was introduced as the third state for the system, in addition to the traditional states of “normal” and “faulty”. Thirdly, through the state of “Function loss”, an effective integration of system fault modes and function modes was achieved, leading to an optimized system reliability model. A three-state space modeling method was then developed by transforming the system function architecture into a system reliability model. Finally, this new model was applied to an aircraft’s rudder and fly-by-wire control system. The results demonstrate that the function architecture at the design stage of the system can be accurately transformed into the new three-state space model. The structure aligns closely with the function architecture and can be effectively utilized in quantitative system reliability calculations. In this way, the process of ensuring system reliability can be seamlessly integrated into the system optimization design process. This integration alleviates the issue of disjointed work between reliability experts and system designers, leading to a more streamlined and efficient aircraft system optimization process.

Active Fault-Tolerant Control Based on MPC and Reinforcement Learning for Quadcopter with Actuator Faults

In this paper, we propose an active fault-tolerant control (AFTC) method combining model predictive control (MPC) and reinforcement learning (RL) for the quadcopter with actuator faults. We take a data-based discriminant model as the fault detection and diagnosis (FDD) module indicating a system fault mode based on the state error. With the information of the fault mode and the state error, the RL controller generates auxiliary control signals to correct the system. To configure the MPC controller quickly, we propose an auxiliary signal-based method for estimation of fault parameters and prove its convergence. The AFTC framework reduces requirements for accurate modeling, and avoids the instability of the RL controller under a continuous operation. To validate the effectiveness of the proposed framework, two trajectory tracking simulations with single and multiple faults are carried out. The simulation results show satisfactory performance and verify that the proposed framework is real-time applicable.

Study on Control Technology of VSC-HVDC in AC System Fault Mode

VSC-HVDC is usually associated with the AC system, which will be inevitably affected by AC system.VSC-HVDC mostly operates in the environment of strong current and high voltage, which will lead some parts of internal device to be interfered or damaged.This paper studies the control technology of VSC-HVDC in AC system fault mode to ensure that the device itself is not only perfect but timely rescue the AC system when it is necessary.

Condition Analysis of Steam Turbine Digital Electro-Hydraulic Control System Based on Data Fusion

Aiming at the problem that the system of DEH regulation system is complex and difficult to analyze its condition, a method based on characteristic extraction and information fusion is proposed. By making RCM analysis, the indexes for quantitative risk evaluation of fault modes are determined, and the fuzzy rule base for risk evaluation is built. Using fuzzy inference, the system fault modes are ranked according to their risk level. Then, the condition characteristic parameters are extracted according to the pivot fault modes. Using the extracted characteristic parameters, an information fusion method based on evidence theory is put forward to evaluate the system’s condition. It is shown by the instance that this method is feasible and effective and the condition analysis results can be used as a support for next maintenance decisions.

Fault Analysis and Treatment of CNC Machine Tool Servo System

The intermediate link of nc systems and machine tools is Servo system. Servo system failure is an important part of the failure of CNC machine tools. Firstly, most common servo system fault mode is introduced from actual work; Secondly, according to fault locating method to determine fault location is discussed; Finally, servo system fault diagnosis example and processing method are introduced.

Structural System Reliability Analysis for a Self-Anchored Suspension Bridge

A self-anchored suspension bridge is analyzed for its structural system reliability, and the bridge reliability is relatively high under vehicular load. Through seeking the distinct structural system fault modes the relatively weak structural elements can be identified, which can be the basis on its reasonable design and offer the reference to the analysis for the similar bridge structures. The results verify that the global -Unzipping method and the improved difference equivalent recursion algorithm is efficient and applicable for system reliability evaluation of the complex structures such as self-anchored suspension bridges.

Design of stochastic fault tolerant control for H2 performance

AbstractIn this paper, the controller synthesis problem for fault tolerant control systems (FTCS) with stochastic stability and H2 performance is studied. System faults of random nature are modelled by a Markov chain. Because the real system fault modes are not directly accessible in the context of FTCS, the controller is reconfigured based on the output of a fault detection and identification (FDI) process, which is modelled by another Markov chain. Then state feedback and output feedback control are developed to achieve the mean square stability (MSS) and the H2 performance for both continuous‐time and discrete‐time systems with model uncertainties. Copyright © 2006 John Wiley & Sons, Ltd.