Early Warning and/or Continous Risk Assessment of Rockfalls in Deep South African Mines

Brink, A. B. A.; Roberts, Mkc

doi:10.36487/acg_repo/711_32

Cited by 1 publication

(1 citation statement)

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“…Current techniques depend on a worker tapping the roof with a bar and listening to the sound to determine if it is loose, known as “roof sounding” [ 43 ]. A more high-tech solution employs the use of a vibration sensor, which is similarly tapped on the roof to identify loose rock [ 44 ]. However, both require workers enter the newly created area.…”

Section: Discussionmentioning

confidence: 99%

Non-Contact Vibro-Acoustic Object Recognition Using Laser Doppler Vibrometry and Convolutional Neural Networks

Darwish

Halkon²,

Oberst³

2022

Sensors

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Laser Doppler vibrometers (LDVs) have been widely adopted due to their large number of benefits in comparison to traditional contacting vibration transducers. Their high sensitivity, among other unique characteristics, has also led to their use as optical microphones, where the measurement of object vibration in the vicinity of a sound source can act as a microphone. Recent work enabling full correction of LDV measurement in the presence of sensor head vibration unlocks new potential applications, including integration within autonomous vehicles (AVs). In this paper, the common AV challenge of object classification is addressed by presenting and evaluating a novel, non-contact vibro-acoustic object recognition technique. This technique utilises a custom set-up involving a synchronised loudspeaker and scanning LDV to simultaneously remotely solicit and record responses to a periodic chirp excitation in various objects. The 864 recorded signals per object were pre-processed into spectrograms of various forms, which were used to train a ResNet-18 neural network via transfer learning to accurately recognise the objects based only on their vibro-acoustic characteristics. A five-fold cross-validation optimisation approach is described, through which the effects of data set size and pre-processing type on classification accuracy are assessed. A further assessment of the ability of the CNN to classify never-before-seen objects belonging to groups of similar objects on which it has been trained is then described. In both scenarios, the CNN was able to obtain excellent classification accuracy of over 99.7%. The work described here demonstrates the significant promise of such an approach as a viable non-contact object recognition technique suitable for various machine automation tasks, for example, defect detection in production lines or even loose rock identification in underground mines.

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Section: Discussionmentioning

confidence: 99%