Visualised inspection system for monitoring environmental anomalies during daily operation and maintenance

PurposeVisual inspection and human judgement form the cornerstone of daily operations and maintenance (O&M) services activities carried out by facility managers nowadays. Recent advances in technologies such as building information modelling (BIM), distributed sensor networks, augmented reality (AR) technologies and digital twins present an immense opportunity to radically improve the way daily O&M is conducted. This paper aims to describe the development of an AR-supported automated environmental anomaly detection and fault isolation method to assist facility managers in addressing problems that affect building occupants’ thermal comfort.Design/methodology/approachThe developed system focusses on the detection of environmental anomalies related to the thermal comfort of occupants within a building. The performance of three anomaly detection algorithms in terms of their ability to detect indoor temperature anomalies is compared. Based on the fault tree analysis (FTA), a decision-making tree is developed to assist facility management (FM) professionals in identifying corresponding failed assets according to the detected anomalous symptoms. The AR system facilitates easy maintenance by highlighting the failed assets hidden behind walls/ceilings on site to the maintenance personnel. The system can thus provide enhanced support to facility managers in their daily O&M activities such as inspection, recording, communication and verification.FindingsTaking the indoor temperature inspection as an example, the case study demonstrates that the O&M management process can be improved using the proposed AR-enhanced inspection system. Comparative analysis of different anomaly detection algorithms reveals that the binary segmentation-based change point detection is effective and efficient in identifying temperature anomalies. The decision-making tree supported by FTA helps formalise the linkage between temperature issues and the corresponding failed assets. Finally, the AR-based model enhanced the maintenance process by visualising and highlighting the hidden failed assets to the maintenance personnel on site.Originality/valueThe originality lies in bringing together the advances in augmented reality, digital twins and data-driven decision-making to support the daily O&M management activities. In particular, the paper presents a novel binary segmentation-based change point detection for identifying temperature anomalous symptoms, a decision-making tree for matching the symptoms to the failed assets, and an AR system for visualising those assets with related information.EPSRC, Innovate U

A handheld diagnostic system for 6LoWPAN networks

The successful deployment of low-power wireless sensor networks (WSNs) in real application environments is a much broader exercise than just the simple instrumentation of the intended monitoring site. Many problems, from node malfunctions to connectivity issues, may arise during commissioning of these networks. These need to be corrected on the spot, often within limited time, to avoid undesired delays in commissioning and yet a fully functional system does not guarantee that no new problems will occur after leaving the site. In this paper we present the first ever (to our knowledge) implementation of a handheld diagnostic system for fast on-site commissioning of low-power IPv6 (6LoWPAN) WSNs as well as troubleshooting of network problems during and after deployment. This system can be used where traditional solutions are insufficient to ascertain the root causes of any problems encountered at no additional complexity in the implementation of the WSN. The embedded diagnosis capability in our system is based on a lightweight decision tree that distills the functioning of communication protocols in use by the network, with a major focus on interoperable IPv6 standards and protocols for low-power WSNs. To show the applicability of our system, we present a set of experiments based on results from a real deployment in a large construction site. Through these experiments, important performance insights are gained that can be used as guidelines for improvement of operation and maintenance of 6LoWPAN networks.This research has been funded by the EPSRC Innovation and Knowledge Centre for Smart Infrastructure and Construction project (EP/K000314/1). The authors wish to thank Costain-Skanska Joint Venture (CSJV) and our industrial partner Crossrail for allowing access and instrumentation of the Paddington site referenced in this paper

Wireless sensor monitoring of Paddington Station Box Corner

This paper presents the real performance of three diaphragm wall panels on the southeast corner of Paddington Station Box during excavation, monitored using a wireless sensor network. In total, 15 LPDT displacement sensors, 12 tilt sensors, 13 relay nodes and a gateway were deployed at three different stages. Each wireless sensor node is programmed with Contiki OS using the in-built IPv6-based network layer (6LoWPAN/RPL) for link-local addressing and routing, and ContikiMAC at the medium access control (MAC) layer for radio duty cycling. Extensive testing and calibration was carried out in the laboratory to ensure that the system functioned as expected. Wireless tilt and displacement sensors were installed to measure the inclination, angular distortion and relative displacement of these corner panels at three different depths. The monitoring data reveal that the corner produced a stiffening effect on the station box, which might result in a breakdown of plane strain conditions. The network performance characteristics (e.g. message reception ratio and network topology status) and challenges are also highlighted and discussed

Poster abstract: Bridge structural monitoring through a vibration energy harvesting wireless sensor network

Structural monitoring applications such as corrosion assessment, measurement of concrete temperature or moisture content of critical bridge structures can greatly benefit from the use of wireless sensor networks (WSNs), however energy harvesting for the operation of the network remains a challenge in this setting. We present a multihop vibration-based energy harvesting WSN system for bridge monitoring applications. Our preliminary simulation experiments show that the system is able to maintain energy neutral operation over time, preserving energy with careful management of sleep and communication times.Engineering and Physical Sciences Research Council Innovation and Knowledge Centre for Smart Infrastructure and Construction project (Grant ID: EP/K000314/1)This is the author accepted manuscript. The final version is available from the Association for Computing Machinery via https://doi.org/10.1145/2993422.299640

Monitoring on the performance of temporary props using wireless strain sensing

Although temporary props have been extensively used in underground support systems, their actual performance is poorly understood, resulting in potentially conservative and over-engineered design. This paper presents the performance monitoring of 4 temporary props in an urban construction site using a newly developed wireless strain sensor node featuring a 24-bit ADC. For each prop, 6 strain gauges and 3 temperature sensors were directly attached onto the prop surface using super glue, and then connected to a wireless strain sensor node mounted in the middle span. Each sensor node transmitted both monitoring data and network diagnostic messages in near-real-Time over an IPv6-based (6LoWPAN) wireless mesh sensor network. The data were also stored locally at each node on a micro SD card. Extensive testing and calibration was undertaken in the laboratory to ensure that the system functioned as expected. The prop loads are presented without correction for temperature effects and compared with the design loads. The monitoring data reveal the development of loads in temporary props during excavation, the formation of the basement and the extraction of the props. The network performance characteristics in terms of message reception ratio and network topology evolution are also highlighted and discussed

Energy neutral operation of vibration energy-harvesting sensor networks for bridge applications

greatly benefit from the use of wireless sensor networks (WSNs), however energy harvesting for the operation of the network remains a challenge in this setting. While solar and wind power are possible and credible solutions to energy generation, the need for positioning sensor nodes in shaded and sheltered locations, e.g., under a bridge deck, is also often precluding their adoption in real-world deployments. In some scenarios vibration energy harvesting has been shown as an effective solution, instead. This paper presents a multihop vibration energy-harvesting WSN system for bridge applications. The system relies on an ultra-low power wireless sensor node, driven by a novel vibration based energy-harvesting technology. We use a receiver-initiated routing protocol to enable energy-efficient and reliable connectivity between nodes with different energy charging capabilities. By combining real vibration data with an experimentally validated model of the vibration energy harvester, a hardware model, and the COOJA simulator, we develop a framework to conduct realistic and repeatable experiments to evaluate the system before on-site deployment. Simulation results show that the system is able to maintain energy neutral operation, preserving energy with careful management of sleep and communication times. We also validate the system through a laboratory experiment on real hardware against real vibration data collected from a bridge. Besides providing general guidelines and considerations for the development of vibration energy-harvesting systems for bridge applications, this work highlights the limitations of the energy budget made available by traffic-induced vibrations, which clearly shrink the applicability of vibration energy-harvesting technology for WSNs to applications that do not generate an overwhelming amounts of data

Source code, simulation and data analysis scripts, and relevant data for "Power-efficient piezoelectric fatigue measurement using long-range wireless sensor networks"

This dataset consists of the simulation and experimental data, data analysis scripts, and the source code of our wireless sensor system for fatigue strain cycles monitoring, published in "Power-efficient piezoelectric fatigue measurement using long-range wireless sensor networks", Smart Materials and Structures, 2019. The dataset contains several Readme files in various folders - see these for further details

An AR-Based Inspection System for Monitoring Temperature Abnormalities in Daily O and M Management

Although facility management (FM) managers can control the daily operations and maintenance (O&M) events via visual senses, it is still challenging for them to inspect as-is conditions efficiently (especially unobserved pumping and pipes) and identify all possible abnormalities based on their experiences. Previous research has been conducted to facilitate O&M inspection via the FM management systems (e.g., computerized maintenance management systems, CMMS) and distributed sensor systems. However, there is still a lack of a visualized intelligent system that can help to inspect, record, communicate, and verify O&M issues in tandem for continuous improvement. In order to provide an intelligent and visualized inspection environment, this study developed an augment reality (AR)-based inspection system based on a digital twin (DT) and focused on the inspection of temperature abnormalities in daily O&M management. Firstly, intelligent abnormalities algorithms are implemented to detect temperature abnormalities. Next, a comprehensive classification and its corresponding sub-categories of temperature abnormalities relating to building assets are constructed based on fault tree analysis (FTA), which encompasses diverse events to distinguish different kinds of maintenance issues commonly appearing in daily O&M management. Expert interviews are conducted to verify and modify the FTA. Next, based on the developed FTA, a rule-based matching module is developed and refined to assist in the matching with the corresponding assets in the existed building DT. Thus, an AR-based system is developed and used to highlight the target assets on site, especially for unobserved assets and a demonstrator of this developed system is developed based on the Centre for Digital Built Britain (CDBB) West Cambridge digital twin pilot. Finally, the challenges involved in developing inspection system in practice, and future opportunities using dynamic DTs for O&M purposes are discussed. The results fill in the research gaps for asset management practitioners, policy makers, and researchers to improve asset performance in O&M phases