Fault Diagnosis Subsystem Research Articles

Diagnosing injection-production system faults in the same well using the rough set-LVQ neural network.

This study proposed a reverse calculation model of the unique rod pump injection and production system structures in the same well to diagnose and resolve defects, after which dynamometer diagrams of the system production and injection pumps were drawn. The invariant moment feature method was applied to identify seven such characteristics in the injection pump power graph, establishing a downhole system for fault diagnosis in rod pump injection and production systems in the same well using Rough Set(RS)-Learning Vector Quantization(LVQ). On the premise of keeping the classification ability unchanged, the Self-Organizing Map(SOM) neural network was used to discretize the original feature data, while RS theory was employed for attribute reduction. After establishing the LVQ fault diagnosis subsystem, the reduced decision table was entered for learning and training. The test results confirmed the efficacy and accuracy of this method in diagnosing downhole faults in rod pump injection-production systems in the same well. After comparing the test results with the actual working conditions, it can be seen that the rod pump injection-production diagnosis system based on RS-LVQ designed in this paper has a recognition rate of 91.3% for fault types, strong recognition ability, short diagnosis time, and A certain practicality. However, the research object of fault diagnosis in this paper is a single fault, and the actual downhole fault situation is complex, and there may be two or more fault types at the same time, which has certain limitations.

Nonlinear servocompensator for fault-tolerant control of a wave energy converter

This paper addresses the problem of controlling a wave energy converter (WEC) susceptible to faults in its braking subsystems, characterized through nonlinear damping. By considering the necessity of robust trajectory tracking related to the sea waves for maximizing the converted energy, one aims to preserve such a trajectory in the presence of faults to avoid physical damage in the structure of the WEC. To achieve this objective, this paper proposes a fault-tolerant control (FTC) that combines two systems: (i) a novel nonlinear servocompensator (NSC) and (ii) a fault diagnosis subsystem (FD). The NSC is based on a variable structure control that generalizes the internal model principle for robust tracking. The reference signal is computed from real-time measurements of the irregular sea waves. The FD subsystem estimates the faults related to the wear of the brakes via an unknown input observer. Due to its independent performance from the FD, the global scheme can be considered as a passive FTC. By considering the faulty model of a WEC based on the Archimedes wave swing prototype, theoretical formulation and the convergence proof are given for the NSC and the FD. The performance of the proposed design is verified with numerical simulations of the WEC with the incidence of irregular sea waves under different fault scenarios in the upper and lower brakes.

Nonlinear robust fault-tolerant control for turbofan engines with actuator faults

This paper investigates nonlinear robust fault-tolerant control for turbofan engines with actuator faults. The nonlinear robust fault-tolerant control method is proposed in this paper, which can guarantee the fault tolerance and robustness of turbofan engines when faults and disturbances occur. The design method is implemented in three steps: Firstly, the dynamic effects caused by actuator faults on turbofan engines are analyzed and then the multivariable polynomial state-space model of turbofan engines with actuator faults is proposed. Secondly, appropriate design parameters are chosen according to fault information. Thirdly, the nonlinear robust fault-tolerant control law for turbofan engines with actuator faults is designed by solving an optimization problem. In addition, stability and [Formula: see text] gain-like for turbofan engines with bounded disturbances and actuator faults are guaranteed by the proposed nonlinear robust fault-tolerant control method. The distinctive characteristic of the paper is that the nonlinear robust fault-tolerant control method for turbofan engines against actuator faults is proposed which can relax the accuracy requirements of the fault diagnosis subsystem and improve the fault-tolerant performance of the closed-loop control system. Simulations show that the nonlinear robust fault-tolerant control method has better control performances including transient responses, disturbance rejection, and fault tolerance for the turbofan engine with actuator faults.

Power transformer condition assessment based on online monitor with SOFC chromatographic detector

Power transformer is vital for energy conversion in power system, the condition assessment of which based on dissolved gas analysis (DGA) is used worldwide. In this paper, an online monitor based on solid oxide fuel cell (SOFC) detector is designed to address the poor stability, low sensitivity of other online-monitoring system. The system is composed of a gas measurement subsystem, a central controlling unit, and an intelligent fault diagnosis subsystem. A portable gas chromatography module developed using SOFC detector. The quantificational algorithm is derived, on basis of which calibration can be skipped. Due to the essential role of the chromatography column as well as gas the detector’s temperature, proportional-integral-differential (PID) controller is implemented in the controlling unit. To evaluate the effectiveness of the developed online monitor, the quantitative analysis and engineering application have been performed; then, based on the measured results, the RVM-ANFIS algorithm is adopted to diagnosis the latent transformers faults. Using the developed online monitor, the results indicate that five characteristic gases, H2, CH4, C2H4, C2H6, and C2H2 can be measured accurately, and incipient faults can be detected, validate the effectiveness of this system.

Street Lamp Fault Diagnosis System Based on Extreme Learning Machine

In view of the construction of urban lighting system needs a lot of manpower deployment, especially for its fault diagnosis problem management. This paper proposes a fault model detection and diagnosis subsystem based on the extreme learning machine for street lamps system. The subsystem is part of the event rule response system which is based on the complex event processing technology framework. The system can handle a large amount of sensor data, perform filtering, carry out complex data processing and decision making. The experimental results show that the proposed street lamp fault diagnosis system based on extreme learning machine can diagnose the street lamp fault effectively and respond to it automatically.

Review on fiber-optic sensing in health monitoring of power grids

Fiber-optic sensing technology is best adapted to health monitoring and evaluation of power grids because of its immunity of electromagnetic interference, capabilities of multiplexing and distributed sensing, and tolerance to harsh environments. We review key fiber-optic sensing technologies, including fiber Bragg gratings, fiber-optic interferometers, optical time domain reflectometries, and their applications in three main parts of power grids, transformers, power towers, and overhead transmission lines, during the past 20 years. In particular, optical fiber composite overhead ground wire and optical phase conductor applied in power grids are the areas of great potential to go further. The perspectives of an intelligent fault diagnosis subsystem for power grids based on a fiber-optic sensing network are discussed, and related on-going work is described. The review shall be of benefit to both engineers and researchers in power grids and fiber-optic sensing.

Design and Implementation of Association Rules in Fault Diagnosis of High-Speed Railway in China

With the rapid development of China's high-speed railway network and the increasement of it’s operating mileage, the requirement for the fault diagnosis and safety of high-speed railway has been increasing significantly. In order to enhance the safety and reduce the accident rates, it is essential to analyze the factors that closely related to safety and accident of high-speed railway. Compared with the traditional fault diagnosis method, the association rule algorithm of data mining technology has the advantage of dealing with large scale fault data. In the present study, the parallel apriori algorithm based on MapReduce algorithm was proposed. Furthermore, a subsystem of association analysis regarding fault diagnosis, which was based on mining association rules and integrated with the parallel association rules algorithm, was designed by taking advantage of the sparsity characteristic of the fault data. The function of the designed subsystem was validated by actual operating data collected. The results show that the proposed fault diagnosis subsystem based distributed parallel association rules can be used in the fault diagnosis of China’s high-speed railway.

A residual based adaptive unscented Kalman filter for fault recovery in attitude determination system of microsatellites

This paper presents an adaptive unscented Kalman filter (AUKF) to recover the satellite attitude in a fault detection and diagnosis (FDD) subsystem of microsatellites. The FDD subsystem includes a filter and an estimator with residual generators, hypothesis tests for fault detections and a reference logic table for fault isolations and fault recovery. The recovery process is based on the monitoring of mean and variance values of each attitude sensor behaviors from residual vectors. In the case of normal work, the residual vectors should be in the form of Gaussian white noise with zero mean and fixed variance. When the hypothesis tests for the residual vectors detect something unusual by comparing the mean and variance values with dynamic thresholds, the AUKF with real-time updated measurement noise covariance matrix will be used to recover the sensor faults. The scheme developed in this paper resolves the problem of the heavy and complex calculations during residual generations and therefore the delay in the isolation process is reduced. The numerical simulations for TSUBAME, a demonstration microsatellite of Tokyo Institute of Technology, are conducted and analyzed to demonstrate the working of the AUKF and FDD subsystem.

Abrupt and incipient fault detection and compensation for a 4-tank system benchmark

Fault detection and compensation play a key role in enhancing the high demand for performance and security in technological systems. This paper proposes an active fault-tolerant control scheme that detects and compensates for actuator faults in a 4-tank system benchmark. The faults are modeled as a drastic gain loss in actuators (pumps), which could lead to a large loss in the nominal performance. The model-based approach uses a recursive least squares parameter estimation algorithm to form a fault detection and diagnosis subsystem and utilizes a parametric eigenstructure assignment method to reconfigure the state feedback controller. The designed controller is simulated for the nonlinear system, and the results demonstrate promising performance increases in faulty cases in comparison with the nonfault-tolerant controller.

Active Fault Tolerant Control of a Remotely Operated Vehicle Propulsion System

This paper presents an active fault tolerant control of a Remotely Operated Vehicle (ROV) propulsion system. An open frame ROV usually requires multi-thruster system to generate various motions. A general framework of active fault tolerant control contains the fault detection and diagnosis (FDD) subsystem and controller re-design subsystem. The FDD subsystem consists of fault detection, isolation and estimation while fault accommodation is chosen as the controller re-design method. In harsh operating environment, thrusters are liable to fault. Therefore, each thruster is monitored by a sensor module called thruster monitoring unit (TMU). As part of FDD subsystem, this module generates information regarding the status of thruster in form of armature voltage and current load feedback. In the case of thruster failure, it is possible to exploit the excessive number of available thruster by re-allocating the required control inputi.e. forces and moments acting on the vehicle. This re-allocation procedure is done by fault accommodation subsystem. A Takagi-Sugeno (T-S) fuzzy method is used to accommodate the thruster faults by penalizing the affected thruster and allowing others to continue the operation. An experimental result is presented by demonstrating the effect a blocked ducting and power supply malfunction. The fault accommodation using T-S fuzzy method is proved to be reliable, fast and efficient in handling and accommodating the thruster fault.

Holographic Spectrum in the Remote Rotating Machinery Fault Diagnosis

In this paper, a remote real-time online rotating machinery monitoring system, which was developed and tested in the Industrial application, is presented including its remote monitoring sub-system, fault diagnosis sub-system. First, the system architecture is introduced. Next, a web-based remote monitoring system is designed, which allows researches and engineers to remotely monitor the health of the machine from any remote geographic location through the Internet. Finally, the holographic spectrum technology in this system is discussed as well for the purpose of providing accurate and reliable diagnosis of the machine states and accident prevention.

An Active Fault-Tolerant Control Method Ofunmanned Underwater Vehicles with Continuous and Uncertain Faults

This paper introduces a novel thruster fault diagnosis and accommodation system for open-frame underwater vehicles with abrupt faults. The proposed system consists of two subsystems: a fault diagnosis subsystem and a fault accommodation sub-system. In the fault diagnosis subsystem a ICMAC(Improved Credit Assignment Cerebellar Model Articulation Controllers) neural network is used to realize the on-line fault identification and the weighting matrix computation. The fault accommodation subsystem uses a control algorithm based on weighted pseudo-inverse to find the solution of the control allocation problem. To illustrate the proposed method effective, simulation example, under multi-uncertain abrupt faults, is given in the paper.

Thruster fault diagnosis and accommodation for open-frame underwater vehicles

This paper introduces a novel thruster fault diagnosis and accommodation system (FDAS) for open-frame underwater vehicles. Basically, the FDAS is a control allocator, but this primary function is enhanced with the ability of automatic thruster fault detection and accommodation. The proposed FDAS consists of two subsystems: a fault diagnosis subsystem (FDS) and a fault accommodation subsystem (FAS). The FDS uses fault detector units (FDUs), associated with each thruster, to monitor their state. Robust and reliable FDUs are based on integration of self-organising maps and fuzzy logic clustering methods. These units are able to detect internal and external faulty states of thrusters. The FAS uses information provided by the FDS to accommodate faults and perform an appropriate control reallocation. A control energy cost function is used as the optimisation criteria. The FAS uses weighted pseudo-inverse to find the solution of the control allocation problem, which minimise this criteria. Two approximations (truncation or scaling) can be used to ensure feasibility of the solution. The proposed FDS is evaluated with data obtained during test trials. The feasible region concept, related with the problem of thruster velocity saturation, is developed in order to provide geometrical interpretation of the control allocation problem. The proposed FDAS is implemented as a Simulink model (ROV simulator), in order to evaluate its performance in different faulty situations.

Fault Diagnosis in an Expert System for Health Services Management in the Tropics

An integrated large-scale expert system called Health-2000, for the management of health services in regions where tropical diseases are endemic, has been designed. This system combines knowledge and databases, the contents of which are operated upon by an inference engine, to produce usable information. The system allows a host of applications, ranging from medical diagnosis to fault detection and preventive maintenance of biomedical equipment. The theoretical background and approach used in the development of the fault diagnosis and equipment maintenance sub-system of Health-2000 is presented. Model-based knowledge acquisition, and an extension of the Failure Modes, Effects and Criticality Analysis are two methodologies applied to build its knowledge bases. The inference engine which supports backward and forward chaining, operates on numerical and non-numerical facts, and uses fuzzy logic to handle vague and uncertain knowledge. Fault isolation proceeds in a top-down fashion, from equipment sub-system, to modules and components.

Fault detection in electromagnetic suspension systems with state estimation methods

A procedure is presented for the design of a fault diagnosis and isolation (FDI) subsystem for electromagnetic suspension systems. This subsystem is based on the Luenberger observer and logical analysis. Using the position sensor output, the observer estimates the state variables. One critical feedback variable is the vertical acceleration of the suspended magnet. The fault of the accelerometer is detected by comparing its output with the estimated state variable. At the same time, a redundant accelerometer takes over to sustain stability. Experimental results illustrate the feasibility of this method. >