The sensing of gravity has emerged as a tool in geophysics applications such as engineering and climate research1–3, including the monitoring of temporal variations in aquifers4 and geodesy5. However, it is impractical to use gravity cartography to resolve metre-scale underground features because of the long measurement times needed for the removal of vibrational noise6. Here we overcome this limitation by realizing a practical quantum gravity gradient sensor. Our design suppresses the effects of micro-seismic and laser noise, thermal and magnetic field variations, and instrument tilt. The instrument achieves a statistical uncertainty of 20 E (1 E = 10−9 s−2) and is used to perform a 0.5-metre-spatial-resolution survey across an 8.5-metre-long line, detecting a 2-metre tunnel with a signal-to-noise ratio of 8. Using a Bayesian inference method, we determine the centre to ±0.19 metres horizontally and the centre depth as (1.89 −0.59/+2.3) metres. The removal of vibrational noise enables improvements in instrument performance to directly translate into reduced measurement time in mapping. The sensor parameters are compatible with applications in mapping aquifers and evaluating impacts on the water table7, archaeology8–11, determination of soil properties12 and water content13, and reducing the risk of unforeseen ground conditions in the construction of critical energy, transport and utilities infrastructure14, providing a new window into the underground.

We address the problem of accurately locating buried utility segments by fusing data from multiple sensors using a novel Marching-Cross-Section (MCS) algorithm. Five types of sensors are used in this work: Ground Penetrating Radar (GPR), Passive Magnetic Fields (PMF), Magnetic Gradiometer (MG), Low Frequency Electromagnetic Fields (LFEM) and Vibro-Acoustics (VA). As part of the MCS algorithm, a novel formulation of the extended Kalman Filter (EKF) is proposed for marching existing utility tracks from a scan cross-section (scs) to the next one; novel rules for initializing utilities based on hypothesized detections on the first scs and for associating predicted utility tracks with hypothesized detections in the following scss are introduced. Algorithms are proposed for generating virtual scan lines based on given hypothesized detections when different sensors do not share common scan lines, or when only the coordinates of the hypothesized detections are provided without any information of the actual survey scan lines. The performance of the proposed system is evaluated with both synthetic data and real data. The experimental results in this work demonstrate that the proposed MCS algorithm can locate multiple buried utility segments simultaneously, including both straight and curved utilities, and can separate intersecting segments. By using the probabilities of a hypothesized detection being a pipe or a cable together with its 3D coordinates, the MCS algorithm is able to discriminate a pipe and a cable close to each other. The MCS algorithm can be used for both post- and on-site processing. When it is used on site, the detected tracks on the current scs can help to determine the location and direction of the next scan line. The proposed “multi-utility multi-sensor” system has no limit to the number of buried utilities or the number of sensors, and the more sensor data used, the more buried utility segments can be detected with more accurate location and orientation.

In recent years, global wind power capacity has grown steadily at an annual rate of around 20%. This has led to wind energy becoming the most important renewable energy source on a global scale, with the total installed capacity reaching 430 GW. However, the strong growth of offshore wind power has been somewhat inhibited due to a number of operational challenges that are yet to be addressed in full. The most important of these challenges appears to be the reliability of the wind turbine gearbox (WTG). WTGs are currently unable to survive their anticipated design lifetime of 20-25 years. Most of them hardly reach a useful operational lifetime of more than seven years without serious refurbishment or replacement and, for offshore wind turbines, failures have been reported as early as within one to two years. In this paper, the damage mechanisms influencing WTGs supported by finite element analysis have been considered and presented in the context of condition monitoring diagnosis and prognosis.

The extraordinary performance offered by cold atom-based clocks and sensors has the opportunity to profoundly affect a range of applications, for example in gravity surveys, enabling long term monitoring applications through low drift measurements. While ground-based devices are already starting to enter the commercial market, significant improvements in robustness and reductions to size, weight, and power are required for such devices to be deployed by Unstaffed Aerial Vehicle systems (UAV). In this article, we realise the first step towards the deployment of cold atom based clocks and sensors on UAV’s by demonstrating an UAV portable magneto-optical trap system, the core package of cold atom based systems. This system is able to generate clouds of 2.1±0.2×107 atoms, in a package of 370 mm × 350 mm × 100 mm, weighing 6.56 kg, consuming 80 W of power.

Although wind turbine gearboxes are designed to remain in-service for 20-25 years, this is not normally the case due to defects initiating and developing prematurely. A large number of gearboxes fail after 7 to 8-years in service. In offshore wind farms gearbox failures have been reported after only 1-2 years in service leading to noteworthy production losses. Reliability issues associated with wind turbine gearboxes are yet to be resolved. Within this paper the quality of materials used for manufacturing wind turbine gearbox gears and bearings has been evaluated. The damage mechanisms affecting gearbox materials have been investigated based on metallographic analysis carried out on failed samples removed from in-service industrial wind turbines. Finite Element Analysis (FEA) has been carried out in order to simulate damage initiation and propagation under in-service conditions. The results have been compared with the experimental observations made on the failed field samples and have been found to be in good agreement. The applicability of acoustic emission in detecting and identifying defects in different wind turbine gearbox components remotely has been assessed following measurements in the field.