EIGHT billion passengers and more than a billion tonnes of freight travelled on India’s 62,594km network in 2017. And while the railway remains a national icon, the system is in need of modernisation, with safety issues and major corridors facing severe capacity constraints. The Ministry of Railways wants to revitalise and expand the network, with plans to invest up to $US 120bn over the next five years.
Work to improve existing infrastructure includes enhanced monitoring of asset condition. In 2017, the ministry approved a contract to implement condition monitoring of in-service rolling stock. The system focusses on detecting and distributing information on bearing and wheel defects, while possessing the capability to integrate other advanced condition monitoring techniques, including vehicle imaging data.
By employing advanced engineering and software techniques, condition monitoring can substantially enhance railway safety by enabling a shift from time-based to needs-based maintenance. This should improve operational availability and efficiency while reducing maintenance costs. By foreseeing issues with rolling stock, such as wheel bearing faults, it is possible to inform maintenance actions in good time, improving the overall effectiveness of this process.
Wabtec subsidiary Track IQ, Australia, is installing wheel bearing and tread defect detection systems at 20 sites on key railway routes across India. These comprise the supplier’s Wheel Condition Monitor (WCM), Railway Bearing Acoustic Monitor (RailBam) and the FleetOne Trending Database solutions, which are used to integrate data from multiple condition monitoring sensor systems.
WCM provides information on wheel tread and loading conditions to help improve wheel life, bogie maintenance and safety. The low-cost system incorporates both wheel impact load detector (Wild) and weigh-in-motion (Wim) capabilities, and can reliably detect a range of wheel tread defect and loading problems, including: wheel flats, spalls, shelling, out-of-roundness, high wheel impact loads, vehicle-axle overloading, poor vehicle/bogie loading (imbalance) and wheel unloading.
WCM’s modular sensors clamp to the rail with no requirement for track structure modifications while they offer the benefit of straightforward maintenance and quick installation often without interfering with normal traffic.
Routine track maintenance - such as tamping - is normally done with the sensors left in-situ. However, railway maintenance staff can remove the sensor from the rail if needed.
India is one of 13 countries which have installed WCMs to date and the system is demonstrating good performance even in demanding arctic, desert and tropical climates.
A supplemental system to WCM is an automatic vehicle identification (AVI) system, which typically consists of tag readers that capture RFID tags mounted on passing rolling stock. The system assigns data efficiently to rolling stock components for trending and alerting.
Another is RailBam, which monitors the acoustic signature of each axle bearing passing the system at line speed to accurately and reliably identify the presence of rolling surface defects in bearings while automatically ranking the severity of the bearing defect.
Experience with the more than 120 RailBam systems currently installed shows that it is possible to greatly reduce the number of hot bearing alarm (HBA) events by detecting bearing defects well in advance of defect heat generation.
Use of the RailBam system and FleetOne enables tracking of the bearing condition and can lead to the optimised timing of a vehicle’s removal from service. This can improve maintenance planning, and in many cases, Track IQ’s customers have been able to extend bearing life.
Sound characteristics
RailBam’s principle of operation is based on analysing sound characteristics emitted by bearing faults. A bearing fault excites a structural response in the bearing and samples the sound radiating from the housing. Proprietary signal processing techniques can isolate the bearing fault signal from other noise such as wheel noise, enabling accurate fault identification and classification.
RailBam is applicable to package and axlebox bearings of all load classes and all bearing manufacturers. If new bearing types enter service, it is possible to update the system’s configuration to correctly analyse data for these bearings.
In addition, the RailBam has an autonomous self-monitoring capability to ensure that field equipment is functioning correctly. Component failures or warnings are identified during a system self-check, and system alerts can be sent to the system maintainer by email. This allows remote system monitoring and provides maintenance technicians with an understanding of the equipment’s status before they go to the site, thus reducing time in the field.
RailBam is designed for remote use in extreme conditions, and therefore requires minimal inspection and maintenance. RailBam systems have been installed and are operational in hot arid zones such as Australia and the United Arab Emirates, as well as in the extremely cold and snowy environments of Canada, Norway and China.
In 2017, Track IQ acquired Imaging Technologies, which enabled the supplier to boost its capabilities to accurately measure wheel profile, brake and brake shoe condition.
The acquisition enabled the integration of Wheel Profile Monitor (WPM) into the product suite, which is an effective tool alongside WCM for wheel maintenance management. WPM is installed on the track and measures the wheel profile for every train wheel that passes the installation site at steady operating speeds. The system firstly captures images of the wheels and processes them with advanced machine vision algorithms to measure key wheel parameters, including: flange height and width; tread hollowing; back-to-back dimension; inner and outer rim thickness; wheel diameter and differential; and wheel profile trace.
The WPM system consists of Image Capture Units (ICU), which are able to capture the entire profile of the wheel head. The ICUs use a high shutter speed rate controlled by high-speed strobe lighting. The cameras and strobe lights are synchronised and triggered by digital packets, which means that the images are never misaligned. The system monitors the movement of each axle, bogie, and wagon to ensure precise triggering.
WPM utilises “true images” for profile measurement, which enables verification of the measurements through manual auditing, and provides higher measurement accuracy because unusual train movements do not distort the actual situation. In addition, WPM does not require frequent calibrations which could reduce the system’s benefits, which has been accredited by the Association of American Railroads to Six Sigma for Gauge Reliability and Repeatability.
Another possible expansion to Indian Railways’ suite of technologies is Brake Inspection Monitor (BIM), which is installed on the wayside and inspects the brakes of trains passing the site. BIM uses image recognition techniques to measure the remaining material in each brake pad and then calculates a replacement window based on the historical wear rate.
The wayside hardware consists of cameras, capture units, flash units, wheel sensors, and AEI tag readers. Components are connected to the Integrated Video Capture Module (IVCM) using copper cables protected by a flexible armoured conduit.
Using a high-quality captured image, BIM is capable of measuring and monitoring: pad thickness; pad wear rates; sticking brakes; presence of the brake key; and identification of missing brake pads.
BIM can save railways a significant outlay on brake pads by extracting the full working life from pads without compromising safety. For example, one heavy-haul customer reported that it would often have to change up to 25% of the pads installed on wagons during routine inspections. Following the introduction of the Brake Pad Examiner, this figure went down to less than 5%, thereby delivering considerable savings in labour and materials.
FleetOne is a multi-sensor trending database product that extends the capability of the wayside monitoring hardware interface. The database integrates a range of wayside monitoring equipment data into a single system and facilitates vehicle monitoring and data mining via key vehicle metrics, which are delivered through a web-based application. Instead of each condition monitoring device providing its own method to access the data, the software is a standardised portal that allows for the import of third-party wayside sensor products thus providing a single data interface. As a result, the system provides a single interface for users to work with all condition-monitoring information associated with a user’s rolling stock.
Leveraging this consolidation of data, FleetOne provides a number of customisable reports on the condition of the vehicle fleet. It also provides a powerful “search engine” which enables users to compose, save, edit and run ad-hoc searches on the database. This allows users to experiment with and determine their own condition-based maintenance rules.
In addition, FleetOne can integrate with vehicle maintenance management systems, such as SAP and Maximo, in order to automatically generate work orders based on maintenance rules configured by a system administrator within the search engine. This is also able to import information on completed maintenance activities for inclusion in the generated reports.
FleetOne is built as an expandable, modular system that interfaces with other vendor’s wayside sensors. Each of these sensor systems is supported by installing an optional module, which are entirely independent of the overall system. In this way, the software serves as a high-level data integrator that does not interfere with or restrict the standalone operation of each of the systems in any way.
Condition monitoring information for each train passing a wayside monitoring location is measured, analysed and transferred to a data control centre operated by Track IQ in Delhi. This data is used to prioritise vehicle maintenance and early success justifies Indian Railway’s vision to expand the number of installed systems threefold to sites around the country within the next few years. This will further boost the safety and efficiency of the network.
