Track & trackside An asset to monitoring
Nick McCormick of the National Physical Laboratory,Daniel Jonas from Atkins and Bryce Lane from Omnicom Engineering explain the digital imaging for condition asset management (DIFCAM) project
T
he aim of the DIFCAM project was to develop a world-class capability in the use of optical techniques to rapidly monitor and assess asset condition.
The project was supported by the UK Technology Strategy Board and RSSB in the Accelerating Innovation in the Rail Industry programme.
This was achieved by using a technology platform approach which could be used for multiple applications. But the prime initial demonstrator has been developed for examination of the interior of rail tunnels, so it could reduce and eventually eliminate the need for costly and risky track access reliant on subjective human visual inspections. Rail tunnel examination was identified as a good demonstrator for this type of technology as it is a current, high-cost problem, with a clear, identified market need and an accessible partner/customer base. A consortium was formed that had the core capabilities to address the technology development required and demonstrate it for tunnel examination. Additionally other uses in other sectors outside rail were also investigated. Current best practice for tunnel examination is shown in Fig 1 and is highly reliant on visual inspection augmented with tactile inspection, usually involving tapping or hammering. It is a highly subjective manual process that is slow to carry out, often in unpleasant surroundings.
Measurements
The types of defects to be considered had previously been identified in a report created for Network Rail by the National Physical Laboratory (NPL).
These include: • cross sectional deformation including bulging
• lining face loss or lining thickness loss • mortar loss in joints in brick linings. • water ingress • fractures and cold joints • missing brick or masonry units • debris on track bed
Appropriate levels of accuracy required to measure the progression of each of these defects were also defined. For the demonstrator a road rail vehicle (RRV), [Fig 2], was used to carry a high resolution camera array to image the interior of the tunnel, a laser scanner to measure the shape and an inertial measurement unit (IMU) to measure the orientation of the RRV during measurement.
RRV in a tunnel and so a high resolution tachometer/odometer was developed to allow repeatable measurements to be undertaken.
The camera array comprised 11 computer synchronised 24 mega pixel cameras and four high-power flash guns that produced overlapping images that recorded the entire interior of the tunnel and the track four-foot and track cess with sub-millimetre resolution. A rotating laser scanner was fitted to the front of the RRV to produce a point cloud measurement of the interior shape of the tunnel.
During trials RRV speeds of 1 m/s were routinely used, these were mainly dictated by the hardware used for imaging and shape measurement required to give the appropriate resolutions for the initial measurement specification. The main feature of the DIFCAM system is that it relies on a comparison of one measurement run with another, maybe taken months or years apart. Correlating the images from different runs identifies any changes or movement
Fig 2. The RRV used to make the measurements, showing the camera array and laser scanner
Fig 1. Tunnel examination relies on tactile and visual inspection techniques
Unlike measurements on surface rail lines, GPS systems cannot be used for measurement of the position of the
in the tunnel wall appearance. A similar process is used with the shape measurement data from the laser scanner to identify changes in shape from run to run. This is enabled by accurately measuring the position and orientation of the RRV during each run.
February 2014 Page 75
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54 |
Page 55 |
Page 56 |
Page 57 |
Page 58 |
Page 59 |
Page 60 |
Page 61 |
Page 62 |
Page 63 |
Page 64 |
Page 65 |
Page 66 |
Page 67 |
Page 68 |
Page 69 |
Page 70 |
Page 71 |
Page 72 |
Page 73 |
Page 74 |
Page 75 |
Page 76 |
Page 77 |
Page 78 |
Page 79 |
Page 80 |
Page 81 |
Page 82 |
Page 83 |
Page 84 |
Page 85 |
Page 86 |
Page 87 |
Page 88 |
Page 89 |
Page 90 |
Page 91 |
Page 92 |
Page 93 |
Page 94 |
Page 95 |
Page 96 |
Page 97 |
Page 98 |
Page 99 |
Page 100 |
Page 101 |
Page 102 |
Page 103 |
Page 104 |
Page 105 |
Page 106 |
Page 107 |
Page 108 |
Page 109 |
Page 110 |
Page 111 |
Page 112 |
Page 113 |
Page 114 |
Page 115 |
Page 116 |
Page 117 |
Page 118 |
Page 119 |
Page 120 |
Page 121 |
Page 122 |
Page 123 |
Page 124 |
Page 125 |
Page 126 |
Page 127 |
Page 128 |
Page 129 |
Page 130 |
Page 131 |
Page 132 |
Page 133 |
Page 134 |
Page 135 |
Page 136 |
Page 137 |
Page 138 |
Page 139 |
Page 140 |
Page 141 |
Page 142 |
Page 143 |
Page 144 |
Page 145 |
Page 146 |
Page 147 |
Page 148 |
Page 149 |
Page 150 |
Page 151 |
Page 152 |
Page 153 |
Page 154 |
Page 155 |
Page 156 |
Page 157 |
Page 158 |
Page 159 |
Page 160 |
Page 161 |
Page 162 |
Page 163 |
Page 164