Ömer Faruk Eker

A Simple State-Based Prognostic Model for Railway Turnout Systems

The importance of railway transportation has been increasing in the world. Considering the current and future estimates of high cargo and passenger transportation volume in railways, prevention or reduction of delays due to any failure is becoming ever more crucial. Railway turnout systems are one of the most critical pieces of equipment in railway infrastructure. When incipient failures occur, they mostly progress slowly from the fault-free to the failure state. Although studies focusing on the identification of possible failures in railway turnout systems exist in literature, neither the detection nor forecasting of failure progression has been reported. This paper presents a simple state-based prognostic (SSBP) method that aims to detect and forecast failure progression in electromechanical systems. The method is compared with Hidden-Markov-Model-based methods on real data collected from a railway turnout system. Obtaining statistically sufficient failure progression samples is difficult, considering that the natural progression of failures in electromechanical systems may take years. In addition, validating the classification model is difficult when the degradation is not observable. Data collection and model validation strategies for failure progression are also presented.

Failure prediction on railway turnouts using time delay neural networks

Turnout systems on railways play critical role on reliability of railway infrastructure. Identification and prediction of failures on mechanical systems have been attracting researchers and industry in recent years. Condition based maintenance focuses on failure identification and prediction using sensory information collected real-time from sensors embedded on electro-mechanical systems. This paper presents neural network based failure prediction algorithm on railway turnouts.

State-Based Prognostics with State Duration Information

Failure prediction (i.e. prognostics) is critical for effective maintenance because it greatly impacts the competitiveness of organizations through its direct connection with operating and support costs, system availability, and operational safety. In recent years, research has focused on state-based prognostics that forecast future progression by first identifying the current state. The duration spent in a state is a factor that influences the expected time to be spent in that state in the future; however, previous works on state-based prognostics have ignored the effect of duration. Hidden Markov Models are the most famous state-based prognostics methods in the literature with practicality problems such as computational complexity, requirement of excessive data, and dependency on initialization. This paper presents a new, simple and easy to implement state-based prognostic method using state duration information. The presented method is applied to two real systems (railway turnout systems and drill bits), and the results are compared with the existing methods presented in the literature. The results show that the presented method outperforms the existing methods. Copyright

Comparison of sensors and methodologies for effective prognostics on railway turnout systems

Railway turnout systems are one of the most important components of a railway’s infrastructure. Their geographically distributed nature makes failure detection, forecasting and maintenance planning extremely important. Prognostics, forecasting the time to failure in order to achieve effective maintenance planning, has attracted increasing attention from industry and researchers in recent years. The prognostic approach has great potential to achieve reduced costs and increased availability. However, the applicability of any engineering model requires economic and practical justifications. This paper presents an analysis of different prognostic methods for railway turnout systems. Five different sensors, installed in a real turnout system used on Turkish State Railways, are individually analysed by applying various prognostic methods. This paper aims to guide practitioners on the application of prognostics and health management technologies to railway turnout systems by discussing the advantages and disadvantages of using different sensors and prognostic methods.

Multi-Domain Modeling and Simulation of an Aircraft System for Advanced Vehicle-Level Reasoning Research and Development

In this paper, we describe a simulation based health monitoring system test-bed for aircraft systems. The purpose of the test-bed is to provide a technology neutral basis for implementing and evaluation of reasoning systems on vehicle level and software architecture in support of the safety and maintenance process. This simulation test-bed will provide the sub-system level results and data which can be fed to the VLRS to generate vehicle level reasoning to achieve broader level diagnoses. This paper describes real-time system architecture and concept of operations for the aircraft major sub-systems. The four main components in the real-time test-bed are the aircraft sub-systems (e.g. battery, fuel, engine, generator, heating and lighting system) simulation model, fault insertion unit, health monitoring data processing and user interface. In this paper, we adopted a component based modelling paradigm for the implementation of the virtual aircraft systems. All of the fault injections are currently implemented via software. The fault insertion unit allows for the repeatable injection of faults into the system. The simulation test-bed has been tested with many different faults which were undetected on system level to process and detect on the vehicle level reasoning. This article also shows how one system fault can affect the overall health of the vehicle.

Health index and behaviour based vehicle level reasoning system

The diagnostics and prognostics are vital parts of the integrated vehicle health management technology. In todays aircraft the diagnostic and prognostic systems play a crucial part in aircraft safety while reducing the operating and maintenance costs. Aircraft are very complex in their design and require consistent monitoring of systems to establish the overall vehicle health status. Many diagnostic systems utilize advanced algorithms (e.g. Bayesian belief networks or neural networks) which usually operate at system or sub-system level. The subsystem reasoners collect the input from components and sensors to process the data and provide the diagnostic/detection results to the flight advisory unit. Generally, a diagnostic system processes the sensor data and provides results; however the behaviors of the machine are not accounted by the diagnostics system. This article provides a novel vehicle level reasoning system, where the health index and system behaviors are also considered with subsystem generated results. Each sub-systems health index is also calculated and passed to the VLRS.

Prognostics of crack propagation in structures using time delay neural network

In todays IVHM system, diagnostics and prognostic play a crucial part in the system safety while reducing the operating and maintenance costs. Structural health management is a vital part of IVHM as arguably structures are the biggest and most costly part of the system, thus the failure of the structure could lead to catastrophic results. The failure of a structure is usually caused by cracks or fractures, to identify the cracks and their growth would be desirable for the SHM. While detection of cracks and the prediction of crack growth is a daunting task, demarcation of the crack is essential to prevent failures. This article presents a technique for the prognostic of crack propagation through aluminium by utilising a time delay neural network algorithm. The Virkler dataset has been used and the remaining useful life has been calculated.