RailBAM® is Track IQ's Bearing Acoustic Monitor. RailBAM® is a proven and reliable system for detecting the presence of Axle bearing defects at line speed.
RailBAM® uses signal processing techniques to isolate sound samples from each bearing and then determine the presence of a fault - including fault severity and type. Repeat samples are trended over time, to look at repeatability and the behaviour over time.
RailBAM® enables a predictive maintenance approach through early detection of bearing damage, typically months ahead of thermal detection methods.
WCM® is a cost-effective wheel impact load detector (WILD) and weigh-in-motion (WIM) system. It provides data for improving wheel life, bogie maintenance, and safety.
WCM® detects and trends wheel tread defects – such as flats, spalls, out-of-roundness, and shelling. This protects the track from high impact loads and allows optimisation of maintenance activities.
WCM® detects overload and poor loading (imbalance) conditions at vehicle, bogie and axle level.
Track IQ's machine vision based Wheel Profile Monitor provides wheel profile measurements with high accuracy.
The system uses an array of high speed cameras to capture detailed images every wheel passing the system. These images are then processed to generate accurate flange, rim, hollowing and other wheel profile measurements.
The system provides easy validation and calibration methods which minimises maintenance and ensures reliability of the system.
Our Brake Inspection Monitor captures images of brake pads and analyses these images to measure thickness. This enables just-in-time replacement of worn brake pads.
By trending the brake pad thickness over time, the system can assist users to predict the replacement window and hence improve maintenance practices.
In addition, the system outputs alerts upon "non-detection" of brake pad components - such as missing securing keys.
The BGM wayside bogie geometry inspection system assesses the tracking performance of bogies and identifies any tracking issues present in the fleet.
The system uses an array of sensors to measure the position of each wheel & axle as it passes through the array. From these measurements, Angle of Attack (AOA), Inter-Axle misalignment, Hunting and tracking position are derived.
This information enables maintainers to proactively service bogies with poor tracking and reduce the incidence of rapid wheel wear caused by excessive friction between the wheel and rail.
FVIS is Track IQ's terminology for our umbrella system that includes a suite of cameras inspecting a range of rolling stock components using machine vision techniques.
FVIS is a modular suite, sharing common components, that include imaging equipment for: couplers & yokes, bearing end caps, bogie side imaging, draft-packs and undercarriage pin inspection.
Once images are acquired, image recognition techniques are applied to examine images for anomalies. In this way alerts are generated based on condition - such as missing bolts, damaged springs or pins.
Train Noise Monitor (TNM) is ideal for long-term environmental noise monitoring, identification of noisy rolling stock, and assessment of railway noise mitigation treatments.
TNM uses a high precision microphone to record noise levels and statistics during each train pass-by. FleetONE provides a user-friendly interface for noise data analysis, alerting, and reporting.
TNM matches noise levels to rolling stock by using a camera or automatic vehicle identification (AVI) system consisting of RFID tag readers and wheel sensors.
FleetONE is an advanced data management system that provides the interface to work with data produced by condition monitoring systems.
FleetONE provides a suite of tools that can be used for identifying rolling stock defects. It enables trend analysis and provides automation through a set of configurable reports.
FleetONE supports Track IQ's equipment, but also integrates with a wide range of devices from other vendors. This allows our customers to work with a single interface to manage condition monitoring information for rolling stock.