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Gas pipeline leakages do not only represent loss of valuable non-renewable resources but also a potential source of environmental pollution and fire disaster, thus making it very important to have a quick and accurate leak awareness scheme in pipeline systems. In a novel approach of providing industry with more reliable, friendly and cost effective technique in handling pipeline leakage especially in countries were pipeline vandalism has been widely reported. This require urgent detection and intervention, the potential of an Artificial Neural Network (ANN) in detecting and locating leaks from everyday flow line measurements was explored in this research paper. Pipeline responses under zero (no) leak condition, and realistic different leak conditions were simulated on a gas pipeline using OLGA simulator. Simulated leaks were seen to be characterized with unique flow rate, velocity, pressure and temperature signatures that were identifiable by the neural network. The network during training was able to learn the correlation between these signatures and the leak parameters and then use the learning in predicting and locating leaks from new signature data. The network predictions showed final RMSE values of 6.14 and 0.977 for the single and multiple leaks model respectively. The correlations between the actual and predicted leak sizes gave R values of 0.95 and 0.60 for the single and multiple leaks respectively while those of the actual and predicted leak locations were 0.97 and 0.96. The system was able to locate 90% of the induced leaks to a distance that is less than 10 m away from the actual leak locations while 62% of the ANN predicted leak sizes differed from the actual value by less than 50% of the actual leak size and the remaining 38% differed by about 65 -400% the actual sizes. This thus implies that the set has a better precision in predicting leak locations than predicting leak sizes. Based on obtained results, it can be said that with sufficiently large number of measurements, neural networks are of great potential in predicting and locating leaks. Results also showed that leak detectability tends to improve with increasing leak size and increasing distance from the source and the relatively low correlation obtained for the multiple leaks model can be attributed to the partial masking of smaller downstream leaks by larger upstream leaks.These characteristics of leaks are predominant to the countries with cases of vandalism, and could be a potential approach in handling pipe leaks.

Vibro-impact drilling has shown huge potential of delivering better rate of penetration, improved tools lifespan and better borehole stability. However, being resonantly instigated, the technique requires a continuous and quantitative characterisation of drill-bit encountered rock materials in order to maintain optimal drilling performance. The present paper introduces a non-conventional method for downhole rock characterisation using measurable impact dynamics and machine learning algorithms. An impacting system that mimics bit-rock impact actions is employed in this present study, and various multistable responses of the system have been simulated and investigated. Features from measurable drill-bit acceleration signals were integrated with operated system parameters and machine learning methods to develop intelligent models capable of quantitatively characterising downhole rock strength. Multilayer perceptron, support vector regression and Gaussian process regression networks have been explored. Based on the performance analysis, the multilayer perceptron networks showed the highest potential for the real-time quantitative rock characterisation using considered acceleration features.

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To reduce potential trauma to the intestine caused by the rigid shell while also optimising its progression efficiency, an elastomer coating was applied to a self-propelled capsule robot for small-bowel endoscopy. The robot is self-propelled by its periodically excited inner mass interacting with the main body of the capsule in the presence of intestinal resistance. This work explored the dynamic responses of the capsule with different elastomer coatings (i.e., different elastic moduli and thicknesses) in the lumen of the small intestine through a three-dimensional finite element analysis. The driving parameters of the robot, including the amplitude, frequency and duty cycle of a square-wave excitation, were further tested to reveal the dynamics of this soft robot. By analysing numerical results, the proposed finite element model can provide quantitative predictions on the contact pressure, resistance force and robot-intestine dynamics under different elastomer coatings. It was found that the softer the elastomer coating is, the lesser the contact pressure between the robot and the intestine, thus implying lesser trauma. The findings of this work can provide design guidelines and an evaluation means for robotic engineers who are developing soft medical robots for bowel examinations as well as clinical practitioners working on capsule endoscopy.