On-board Condition Monitoring System Research Articles

State-of-the-Art Wayside Condition Monitoring Systems for Railway Wheels: A Comprehensive Review

In recent decades, there has been a constant demand for faster, longer, and safer railway networks. This also brings challenges for condition monitoring systems in modern railway vehicles. More specifically, critical parts of railway vehicles like wheels degrade over time due to various operational and environmental reasons. Different dynamic effects such as skidding/sliding over the track and the presence of contamination between wheel-rail cause various wheel defects. Faulty wheels ultimately lead to the derailment of railway vehicles. To avoid worst situations like railway derailments, various research has been conducted for developing efficient condition monitoring systems for railway wheels. In addition, there have been some commercial condition monitoring products that can be deployed with railway vehicles. These systems incorporate various sensors such as strain gauges and vision sensors to collect data for diagnosis and prognosis. Various methods have been explored but yet there is a broad research gap in terms of developing advanced onboard condition monitoring systems. With the progress in technology, advanced systems with Machine Learning/Deep Learning methods can provide more efficient and robust condition monitoring of dynamic railway systems. Considering the need for advancement in condition monitoring systems for railway vehicles, a comprehensive review of existing condition monitoring systems for railway wheels is conducted in this paper. The review is aimed at understanding the feasibility and potential of new methods for modern railways. This paper provides a detailed overview of studies on the existing wayside systems and reports their advantages and disadvantages concerning its recently emerging counterpart on-board monitoring systems. Data acquisition systems and analysis methods are critically reviewed which could assist in developing more efficient and reliable condition monitoring systems for railway wheels. This article also reviews the current progress of wayside systems and their limitations. The article is targeted at the researchers and engineers working in this domain, who can pave the way for developing advanced and cost-effective condition monitoring systems for railway wheels using modern technologies.

Maintenance Action Recommendation Using Collaborative Filtering

The problem we were trying to solve in 2013 PHM Society Conference Data Challenge competition 1 is closely related to remote monitoring and diagnostics in industrial applications. This data was generated from an industrial piece of equipment with a sensor network to measure several parameters and an onboard condition monitoring system. The measured data goes through a control logic in order to monitor the equipment’s operating regime. At any time instant when some of these parameters meet a specific condition, the control system generates an unique event id/code. Each case is described by a set of event codes which characterize the atypical operating condition of the equipment. Some of these cases with specific event code combinations may be operationally significant and could be indicative of “Problem Types”, some of which are assumed to be known to the subject matter experts. As a response to these problems, domain experts recommend appropriate diagnostic measures (or maintenance actions) depending on the problem types. The goal of this data competition is to build an automated system that can recommend particular maintenance action(s) to mitigate these problem(s).

On-board condition monitoring of rail axle bearings using vibration data

Premature failure of rail axle bearings generates a significant increase in train operating costs and can also affect train safety. In order to solve this problem a commercial on-board condition monitoring system has been fitted on numerous passenger trains, providing the operator with real-time information on bearing health. This new technology detects bearing damage effectively and promptly. The project of which this paper is a part aims to determine a relationship between vibrations and bearing damage. Filtering tools have been developed to parameterise extra vibrations due to bearing damage, and to compare vibrations produced by different bearings. Bearings, which failed in-service, have been analysed with surface profilometry, to quantify the damage. This paper highlights a requirement for a better understanding of the damage mechanisms involved in bearing failure and a more detailed characterization of the damage.

Wind Turbine Loads Induced by Terrain and Wakes: An Experimental Study through Vibration Analysis and Computational Fluid Dynamics

A wind turbine is a very well-known archetype of energy conversion system working at non-stationary regimes. Despite this, a deep mechanical comprehension of wind turbines operating in complicated conditions is still challenging, especially as regards the analysis of experimental data. In particular, wind turbines in complex terrain represent a very valuable testing ground because of the possible combination of wake effects among nearby turbines and flow accelerations caused by the terrain morphology. For these reasons, in this work, a cluster of four full-scale wind turbines from a very complex site is studied. The object of investigation is vibrations, at the level of the structure (tower) and drive-train. Data collected by the on-board condition monitoring system are analyzed and interpreted in light of the knowledge of wind conditions and operating parameters collected by the Supervisory Control And Data Acquisition (SCADA). A free flow Computational Fluid Dynamics (CFD) simulation is also performed, and it allows one to better interpret the vibration analysis. The main outcome is the interpretation of how wakes and flow turbulences appear in the vibration signals, both at the structural level and at the drive-train level. Therefore, this wind to gear approach builds a connection between flow phenomena and mechanical phenomena in the form of vibrations, representing a precious tool for assessing loads in different working conditions.

Estimation of Bogie Performance Criteria Through On-Board Condition Monitoring


 
 
 In this paper, bogie performance criteria are reviewed and it is shown that a real-time, on-board condition monitoring system can efficiently monitor these criteria to improve failure mode detection in freight rail operations. Although the dynamics of rail car bogie performance are well understood in the industry, this topic has recently received renewed attention through impending regulatory changes. These changes seek to extend empty rail car performance criteria to include loaded rail cars as well. Currently, the monitoring of bogie performance is primarily accomplished by wayside detection systems in North America. These systems are only sparsely deployed in the track network and do not offer the ability to monitor bogies continuously. The lack of these elements leads to unexpected downtimes resulting in costly reactive maintenance and lengthy periods of time before an adequate performance history can be established. This paper reviews performance criteria which critically influence bogie performance and proposes a vibration based condition monitoring strategy to estimate system component deterioration and their contribution to the development of bogie hunting. The strategy addresses both sensing techniques and monitoring algorithms to maximize the efficiency of the monitoring solution. In particular it is proposed that understanding the relation of different hunting modes to car body oscillations can be used for a deeper understanding of the rail car condition which current technologies are not able to provide.
 
 

Condition Monitoring System for Light Rail Vehicle and Track

Condition monitoring and fault detection systems are becoming increasingly important in rail vehicles maintenance and operation, ensuring safety and reliability improvement. Light rail systems are not the main target for this trend, because of low operational speed and lower safety factors. Nevertheless public transport operators begin to pay a closer attention to the technical state monitoring of vehicle and track, in order to reduce maintenance cost and increase safety and ride comfort for passengers, which is an important challenge for public transport competitiveness in XXI century. The paper describes the main concept of the innovative on-board condition monitoring system for light rail vehicle and track. Functional requirements, assumptions and procedures are described, as well as the on-board data acquisition unit with necessary transducers, which number, function and technical parameters were optimized during the research phase of the project. The prototype of the presented system is now being tested in normal operating conditions.