Observer-based Fault Detection and Diagnosis for Mechanical Transmission Systems with Sensorless Variable Speed Drives

Observer based approaches are commonly embedded in sensorless variable speed drives for the purpose of speed control. It estimates system variables to produce errors or residual signals in conjunction with corresponding measurements. The residual signals then are relied to tune control parameters to maintain operational performance even if there are considerable disturbances such as noises and component faults. Obviously, this control strategy outcomes robust control performances. However, it may produce adverse consequences to the system when faults progress to high severity. To prevent the occurrences of such consequences, this research proposes the utilisation of residual signals as detection features to raise alerts for incipient faults. Based on a gear transmission system with a sensorless variable speed drive (VSD), observers for speed, flux and torque are developed for examining their residuals under two mechanical faults: tooth breakage with different degrees of severities and shortage of lubricant at different levels are investigated. It shows that power residual signals can be based on to indicate different faults, showing that the observer based approaches are effective for monitoring VSD based mechanical systems. Moreover, it also shows that these two types fault can be separated based on the dynamic components in the voltage signals

Fault detection and diagnosis of a plastic film extrusion process

This paper presents a new approach to the design of a model-based fault detection and diagnosis system for application to a plastic film extrusion process. The design constructs a residual generator via parity relations. A multi-objective optimisation problem must be solved in order for the residual to be sensitive to faults but insensitive to disturbances and modelling errors. In this paper, we exploit a genetic algorithm for solving this multi-objective optimisation problem and the resulting fault detection and diagnosis system is applied to a first-principles model of a plastic film extrusion process. Simulation results demonstrate that various types of faults can be detected and diagnosed successfully

Model-based sensor supervision inland navigation networks: Cuinchy-Fontinettes case study

In recent years, inland navigation networks benefit from the innovation of the instrumentation and SCADA systems. These data acquisition and control systems lead to the improvement of the manage- ment of these networks. Moreover, they allow the implementation of more accurate automatic control to guarantee the navigation requirements. However, sensors and actuators are subject to faults due to the strong effects of the environment, aging, etc. Thus, before implementing automatic control strate- gies that rely on the fault-free mode, it is necessary to design a fault diagnosis scheme. This fault diagnosis scheme has to detect and isolate possible faults in the system to guarantee fault-free data and the efficiency of the automatic control algorithms. Moreover, the proposed supervision scheme could predict future incipient faults that are necessary to perform predictive maintenance of the equipment. In this paper, a general architecture of sensor fault detection and isolation using model-based approaches will be proposed for inland navigation networks. The proposed approach will be particularized for the Cuinchy-Fontinettes reach located in the north of France. The preliminary results show the effectiveness of the proposed fault diagnosis methodologies using a realistic simulator and fault scenarios.In recent years, inland navigation networks bene¿t from the innovation of the instrumentation and SCADA systems. These data acquisition and control systems lead to the improvement of the management of these networks. Moreover, they allow the implementation of more accurate automatic control to guarantee the navigation requirements. However, sensors and actuators are subject to faults due to the strong effects of the environment, aging, etc. Thus, before implementing automatic control strategies that rely on the fault-free mode, it is necessary to design a fault diagnosis scheme. This fault diagnosis scheme has to detect and isolate possible faults in the system to guarantee fault-free data and the efficiency of the automatic control algorithms. Moreover, the proposed supervision scheme could predict future incipient faults that are necessary to perform predictive maintenance of the equipment. In this paper, a general architecture of sensor fault detection and isolation using model-based approaches will be proposed for inland navigation networks. The proposed approach will be particularized for the Cuinchy-Fontinettes reach located in the north of France. The preliminary results show the effectiveness of the proposed fault diagnosis methodologies using a realistic simulator and fault scenarios.Peer ReviewedPostprint (author's final draft

Robust fault detection for vehicle lateral dynamics: Azonotope-based set-membership approach

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksIn this work, a model-based fault detection layoutfor vehicle lateral dynamics system is presented. The majorfocus in this study is on the handling of model uncertainties andunknown inputs. In fact, the vehicle lateral model is affectedby several parameter variations such as longitudinal velocity,cornering stiffnesses coefficients and unknown inputs like windgust disturbances. Cornering stiffness parameters variation isconsidered to be unknown but bounded with known compactset. Their effect is addressed by generating intervals for theresiduals based on the zonotope representation of all possiblevalues. The developed fault detection procedure has been testedusing real driving data acquired from a prototype vehicle.Index Terms— Robust fault detection, interval models,zonotopes, set-membership, switched uncertain systems, LMIs,input-to-state stability, arbitrary switching.Peer ReviewedPostprint (author's final draft

Robust Fault Detection of Switched Linear Systems with State Delays

This correspondence deals with the problem of robust fault detection for discrete-time switched systems with state delays under an arbitrary switching signal. The fault detection filter is used as the residual generator, in which the filter parameters are dependent on the system mode. Attention is focused on designing the robust fault detection filter such that, for unknown inputs, control inputs, and model uncertainties, the estimation error between the residuals and faults is minimized. The problem of robust fault detection is converted into an H infin-filtering problem. By a switched Lyapunov functional approach, a sufficient condition for the solvability of this problem is established in terms of linear matrix inequalities. A numerical example is provided to demonstrate the effectiveness of the proposed method

Hybrid fault detection using kalman filter and neural network for quadrotor micro aerial vehicle

This thesis introduces the application of time-domain Hybrid Fault Detection (HFD) methods for application in a quadrotor Micro Aerial Vehicle (MAV). The application aims to solve one of the main problems of the quadrotor, which is its inability to reach the exact target location that the user intended. The problem may be due to a faulty signal happened in the sensor or the actuator side or both, causing the quadrotor unable to complete the task given. Among the reasons of the faulty signal are the occurrences of signal in quadrotor, in the sensor or actuator side, as well as possible communication problem. When actuator fault, sensor fault or both faults occur, the controllers cannot function well and hence its performance reduce. At the initial control design stage of quadrotor, it is usually designed under the assumption that no faults would occur in quadrotors. The Faulty Detection (FD) method is therefore crucial to ensure quadrotor system can work properly and efficiently. The proposed method for the fault detection in this study uses hybrid technique which combines the extended kalman filter and artificial neural network (ANN). Two classes of approaches are analysed: the fault system identification approach ANN and the observer-based approach using the extended kalman filter. The extended kalman filter recognizes data from the sensors of the system and indicates the residuals of the system in the sensor reading. Residuals prediction is based on the fault magnitude and the time occurrence of fault. The information will then be fed to ANN, which consists of a bank of parameter estimation that generates the failure state. ANN is an algorithm that is used to determine the fault condition and determine its severity in the quadrotor system. ANN is designed based on nonlinear autoregressive network with exogenous inputs (NARX) scheme so that it can be trained to generate output based on the simulation behaviours of the quadrotor. The different fault locations are used as input vectors for training an artificial neural network (ANN). The result of the residual signal before filtration and after filtration showed that Kalman-ANN is able to identify single fault as well as multiple faults. For all individual faults including the multiple fault detection, the accuracy of the detection is 78.89 percent. It can be conclude that the newly proposed hybrid FD method in this thesis is able to accurately detect the location fault, for both the sensor and actuator faults simultaneous in the quadrotor

A failure diagnosis system based on a neural network classifier for the space shuttle main engine

A conceptual design of a model based failure detection and diagnosis system is developed for the space shuttle main engine. This design relies on the accurate and reliable identification of the parameters of the highly nonlinear and very complex engine. The design approach is presented in some detail and results for a failed valve are presented. These preliminary results verify that the developed parameter identification technique together with a neural network classifier can be used for this purpose