Sensor Fault Detection for Rail Vehicle Suspension Systems with Disturbances and Stochastic Noises

This paper develops a sensor fault detection scheme for rail vehicle passive suspension systems, using a fault detection observer, in the presence of uncertain track regularity and vehicle noises which are modeled as external disturbances and stochastic process signals. To design the fault detection observer, the suspension system states are augmented with the disturbances treated as new states, leading to an augmented and singular system with stochastic noises. Using system output measurements, the observer is designed to generate the needed residual signal for fault detection. Existence conditions for observer design are analyzed and illustrated. In term of the residual signal, both fault detection threshold and fault detectability condition are obtained, to form a systematic detection algorithm. Simulation results on a realistic vehicle system model are presented to illustrate the observer behavior and fault detection performance

A Review in Fault Diagnosis and Health Assessment for Railway Traction Drives

During the last decade, due to the increasing importance of reliability and availability, railway industry is making greater use of fault diagnosis approaches for early fault detection, as well as Condition-based maintenance frameworks. Due to the influence of traction drive in the railway system availability, several research works have been focused on Fault Diagnosis for Railway traction drives. Fault diagnosis approaches have been applied to electric machines, sensors and power electronics. Furthermore, Condition-based maintenance framework seems to reduce corrective and Time-based maintenance works in Railway Systems. However, there is not any publication that summarizes all the research works carried out in Fault diagnosis and Condition-based Maintenance frameworks for Railway Traction Drives. Thus, this review presents the development of Health Assessment and Fault Diagnosis in Railway Traction Drives during the last decade

Feasibility of Applying Mel-Frequency Cepstral Coefficients in a Drive-by Damage Detection Methodology for High-Speed Railway Bridges

In this paper, a drive-by damage detection methodology for high-speed railway (HSR) bridges is addressed, to appraise the application of Mel-frequency cepstral coefficients (MFCC) to extract the Damage Index (DI). A finite element (FEM) 2D VTBI model that incorporates the train, ballasted track and bridge behavior is presented. The formulation includes track irregularities and a damaged condition induced in a specified structure region. The feasibility of applying cepstrum analysis components to the indirect damage detection in HSR by on-board sensors is evaluated by numerical simulations, in which dynamic analyses are performed through a code implemented in MATLAB. Different damage scenarios are simulated, as well as external excitations such as measurement noises and different levels of track irregularities. The results show that MFCC-based DI are highly sensitive regarding damage detection, and robust to the noise. Bridge stiffness can be recognized satisfactorily at high speeds and under different levels of track irregularities. Moreover, the magnitude of DI extracted from MFCC is related to the relative severity of the damage. The results presented in this study should be seen as a first attempt to link cepstrum-based features in an HSR drive-by damage detection approach.info:eu-repo/semantics/publishedVersio

Fault detection and isolation for railway vehicle suspensions

The ability to detect and isolate component faults in a railway suspension system is important for improved train safety and maintenance. An undetected failure in the suspension systems can cause severe wheel-rail wear, reduce ride comfort, worsen passenger safety and increase unexpected maintenance costs. Existing fault detection methods are limited in several respects, such as effectiveness/sensitivity for fault detection, or robustness to external condition changes. This thesis investigates a model-less fault detection and isolation approach using cross correlation and/or relative variance techniques, developed to overcome these limitations. This thesis treats a conventional bogie vehicle with a symmetrical structure. Excited by the track irregularities, the dynamics of the vehicle are studied under the normal conditions, with an emphasis on the vertical and related motions of the bogies and the carbody. Two fault detection schemes employing data processing using data directly from measurement are discussed. One uses cross correlation evaluation of the basic bogie motions to detect component fault; the other takes advantage of the relationship between the relative variances of the suspension accelerations. Finally, the fault isolation schemes are assessed based on the comparison of fault detection performances in different conditions. The proposed approach does not require detailed knowledge of the vehiclelbogie and external track irregularities. The effectiveness of the approach is verified by computer simulations in Matlab/Simulink

Overview of modern contributions in vehicle noise and vibration refinement with special emphasis on diagnostics

U ovom radu prikazana su određena razmatranja vezana za karakteristike buke i vibracija savremenih motornih vozila. Pored naučnog, problematika se razmatra i sa praktičnog aspekta u cilju struktuiranja potrebnih znanja, neophodnih za pravilnu dijagnostiku problema. Takođe se razmatraju napredne analize signala buke i vibracija. Ova sinergija naučnog i praktičnog pristupa predstavlja osnovu za dalja napredna istraživanja.This paper presents certain considerations related to noise, vibration and harshness issues on modern motor vehicles. The first, practical aspect was used toward structuring of the acquired knowledge and relationships, required for proper problem diagnosis. On the other hand, advanced signal analyses are considered. The influence on human body is processed and certain noise and vibration analyzers are presented. This synergy of scientific and applicative approach represents a basis for further research related to this important automotive branch

A novel online data-driven algorithm for detecting UAV navigation sensor faults

The use of Unmanned Aerial Vehicles (UAVs) has increased significantly in recent years. On-board integrated navigation sensors are a key component of UAVs' flight control systems and are essential for flight safety. In order to ensure flight safety, timely and effective navigation sensor fault detection capability is required. In this paper, a novel data-driven Adaptive Neuron Fuzzy Inference System (ANFIS)-based approach is presented for the detection of on-board navigation sensor faults in UAVs. Contrary to the classic UAV sensor fault detection algorithms, based on predefined or modelled faults, the proposed algorithm combines an online data training mechanism with the ANFIS-based decision system. The main advantages of this algorithm are that it allows real-time model-free residual analysis from Kalman Filter (KF) estimates and the ANFIS to build a reliable fault detection system. In addition, it allows fast and accurate detection of faults, which makes it suitable for real-time applications. Experimental results have demonstrated the effectiveness of the proposed fault detection method in terms of accuracy and misdetection rate

Multiple model based real time estimation of wheel-rail contact conditions

The issue of low adhesion between the wheel and the rail has been a problem for thedesign and operation of the railway vehicles. The level of adhesion can be influenced bymany different factors, such as contamination, climate, and vegetation, and it isextremely difficult to predict with certainty. Changes in the adhesion conditions can berapid and short-lived, and values can differ from position to position along a route,depending on the type and degree of contamination. All these factors present asignificant scientific challenge to effectively design a suitable technique to tackle thisproblem. This thesis presents the development of a unique, vehicle based technique forthe real-time estimation of the contact conditions using multiple models to representvariations in the adhesion level and different contact conditions. The proposed solutionexploits the fact that the dynamic behaviour of a railway vehicle is strongly affected bythe nonlinearities and the variations in creep characteristics. The purpose of the proposedscheme is to interpret these variations in the dynamic response of the wheelset,developing useful contact condition information. The proposed system involves the useof a number of carefully selected mathematical models (or estimators) of a rail vehicle tomimic train dynamic behaviours in response to different track conditions. Each of theestimators is tuned to match one particular track condition to give the best results at thespecific design point. Increased estimation errors are expected if the contact condition isnot at or near the chosen operating point. The level of matches/mismatches is reflected inthe estimation errors (or residuals) of the models concerned when compared with the realvehicle (through the measurement output of vehicle mounted inertial sensors). Theoutput residuals from all the models are then assessed using an artificial intelligencedecision-making approach to determine which of the models provides a best match to thepresent operating condition and, thus, provide real-time information about trackconditions

Sensors Fault Diagnosis Trends and Applications

Fault diagnosis has always been a concern for industry. In general, diagnosis in complex systems requires the acquisition of information from sensors and the processing and extracting of required features for the classification or identification of faults. Therefore, fault diagnosis of sensors is clearly important as faulty information from a sensor may lead to misleading conclusions about the whole system. As engineering systems grow in size and complexity, it becomes more and more important to diagnose faulty behavior before it can lead to total failure. In the light of above issues, this book is dedicated to trends and applications in modern-sensor fault diagnosis