Feasibility Evaluation of a Vibration-Based Leak Detection Technique for Sustainable Water Distribution Pipeline System Monitoring

Conventional water pipeline leak-detection surveys employ labor-intensive acoustic techniques, which are usually expensive and less useful for continuous monitoring of distribution pipelines. Based on a comprehensive review of literature and available commercial products, it has been recognized that despite previous studies and products attempting to address the limitations of the conventional surveys by proposing and evaluating a myriad of leak-detection techniques (LDTs), they lacked extensive validation on complex looped systems. Additionally, they offer limited compatibility with some pipe materials such as those made of plastic and may even fail to distinguish leaks from other system disturbances. A novel LDT that addresses some of these limitations is developed and evaluated in the current study using an experimental set-up that is representative of a real-world pipeline system and made of Polyvinyl Chloride (PVC) pipe. The studied LDT requires continuous monitoring of the change in the cross spectral density of surface vibration measured at discrete locations along the pipeline. This vibration-based LDT was hypothesized to be capable of not only detecting the onset of leakage, but also determining its relative severity in complex pipeline systems. Findings based on a two-phase, controlled experimental testing revealed that the proposed LDT is capable of detecting leakages and estimating their relative severities in a real-size, multi-looped pipeline system that is comprised of multiple joints, bends and pipes of multiple sizes. Furthermore, the sustainability merits of the proposed LDT for a representative application scenario are estimated. Specifically, life cycle costs and energy consumption for monitoring the large diameter pipelines in the water distribution system of the Charleston peninsula region in South Carolina are estimated by developing conceptual prototypes of the sensing, communication and computation schemes for practically employing the proposed LDT. The prototype designs are informed by the knowledge derived from the two-phase experimental testing campaign. Overall, the proposed study contributes to the body of knowledge on water pipeline leak detection, specifically to non-intrusive vibration-based monitoring, applications on plastic pipelines, and smart and sustainable network-wide continuous monitoring schemes

Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems

Over the last few decades, the field of fault diagnostics and structural health management has been experiencing rapid developments. The reliability, availability, and safety of engineering systems can be significantly improved by implementing multifaceted strategies of in situ diagnostics and prognostics. With the development of intelligence algorithms, smart sensors, and advanced data collection and modeling techniques, this challenging research area has been receiving ever-increasing attention in both fundamental research and engineering applications. This has been strongly supported by the extensive applications ranging from aerospace, automotive, transport, manufacturing, and processing industries to defense and infrastructure industries

Novel Smart N95 Filtering Facepiece Respirator with Real-time Adaptive Fit Functionality and Wireless Humidity Monitoring for Enhanced Wearable Comfort

The widespread emergence of the COVID-19 pandemic has transformed our lifestyle, and facial respirators have become an essential part of daily life. Nevertheless, the current respirators possess several limitations such as poor respirator fit because they are incapable of covering diverse human facial sizes and shapes, potentially diminishing the effect of wearing respirators. In addition, the current facial respirators do not inform the user of the air quality within the smart facepiece respirator in case of continuous long-term use. Here, we demonstrate the novel smart N-95 filtering facepiece respirator that incorporates the humidity sensor and pressure sensory feedback-enabled self-fit adjusting functionality for the effective performance of the facial respirator to prevent the transmission of airborne pathogens. The laser-induced graphene (LIG) constitutes the humidity sensor, and the pressure sensor array based on the dielectric elastomeric sponge monitors the respirator contact on the face of the user, providing the sensory information for a closed-loop feedback mechanism. As a result of the self-fit adjusting mode along with elastomeric lining, the fit factor is increased by 3.20 and 5 times at average and maximum respectively. We expect that the experimental proof-of-concept of this work will offer viable solutions to the current commercial respirators to address the limitations.Comment: 20 pages, 5 figures, 1 table, submitted for possible publicatio

Self-powered Time-Keeping and Time-of-Occurrence Sensing

Self-powered and passive Internet-of-Things (IoT) devices (e.g. RFID tags, financial assets, wireless sensors and surface-mount devices) have been widely deployed in our everyday and industrial applications. While diverse functionalities have been implemented in passive systems, the lack of a reference clock limits the design space of such devices used for applications such as time-stamping sensing, recording and dynamic authentication. Self-powered time-keeping in passive systems has been challenging because they do not have access to continuous power sources. While energy transducers can harvest power from ambient environment, the intermittent power cannot support continuous operation for reference clocks. The thesis of this dissertation is to implement self-powered time-keeping devices on standard CMOS processes. In this dissertation, a novel device that combines the physics of quantum tunneling and floating-gate (FG) structures is proposed for self-powered time-keeping in CMOS process. The proposed device is based on thermally assisted Fowler-Nordheim (FN) tunneling process across high-quality oxide layer to discharge the floating-gate node, therefore resulting in a time-dependent FG potential. The device was fully characterized in this dissertation, and it does not require external powering during runtime, making it feasible for passive devices and systems. Dynamic signature based on the synchronization and desynchronization behavior of the FN timer is proposed for authentication of IoT devices. The self-compensating physics ensure that when distributed timers are subjected to identical environment variances that are common-mode noise, they can maintain synchronization with respect to each other. On the contrary, different environment conditions will desynchronize the timers creating unique signatures. The signatures could be used to differentiate between products that belong to different supply-chains or products that were subjected to malicious tampering. SecureID type dynamic authentication protocols based on the signature generated by the FN timers are proposed and they are proven to be robust to most attacks. The protocols are further analyzed to be lightweight enough for passive devices whose computational sources are limited. The device could also be applied for self-powered sensing of time-of-occurrence. The prototype was verified by integrating the device with a self-powered mechanical sensor to sense and record time-of-occurrence of mechanical events. The system-on-chip design uses the timer output to modulate a linear injector to stamp the time information into the sensing results. Time-of-occurrence can be reconstructed by training the mathematical model and then applying that to the test data. The design was verified to have a high reconstruction accuracy

Arctic Standards: Recommendations on Oil Spill Prevention, Response, and Safety in the U.S. Arctic Ocean

Oil spilled in Arctic waters would be particularly difficult to remove. Current technology has not been proved to effectively clean up oil when mixed with ice or when trapped under ice. An oil spill would have a profoundly adverse impact on the rich and complex ecosystem found nowhere else in the United States. The Arctic Ocean is home to bowhead, beluga, and gray whales; walruses; polar bears; and other magnificent marine mammals, as well as millions of migratory birds. A healthy ocean is important for these species and integral to the continuation of hunting and fishing traditions practiced by Alaska Native communities for thousands of years.To aid the United States in its efforts to modernize Arctic technology and equipment standards, this report examines the fierce Arctic conditions in which offshore oil and gas operations could take place and then offers a summary of key recommendations for the Interior Department to consider as it develops world-class, Arctic-specific regulatory standards for these activities. Pew's recommendations call for improved technology,equipment, and procedural requirements that match the challenging conditions in the Arctic and for full public participation and transparency throughout the decision-making process. Pew is not opposed to offshore drilling, but a balance must be achieved between responsible energy development and protection of the environment.It is essential that appropriate standards be in place for safety and for oil spill prevention and response in this extreme, remote, and vulnerable ecosystem. This report recommends updating regulations to include Arctic specific requirements and codifying temporary guidance into regulation. The appendixes to this report provide substantially more detail on the report's recommendations, including technical background documentation and additional referenced materials. Please refer to the full set of appendixes for a complete set of recommendations. This report and its appendixes offer guidelines for responsible hydrocarbon development in the U.S. Arctic Ocean

Applying CS and WSN methods for improving efficiency of frozen and chilled aquatic products monitoring system in cold chain logistics

Wireless Sensor Network (WSN) is applied widely in food cold chain logistics. However, traditional monitoring systems require significant real-time sensor data transmission which will result in heavy data traffic and communication systems overloading, and thus reduce the data collection and transmission efficiency. This research aims to develop a temperature Monitoring System for Frozen and Chilled Aquatic Products (MS-FCAP) based on WSN integrated with Compressed Sending (CS) to improve the efficiency of MS-FCAP. Through understanding the temperature and related information requirements of frozen and chilled aquatic products cold chain logistics, this paper illustrates the design of the CS model which consists of sparse sampling and data reconstruction, and shelf-life prediction. The system was implemented and evaluated in cold chain logistics between Hainan and Beijing in China. The evaluation result suggests that MS-FCAP has a high accuracy in reconstructing temperature data under variable temperature condition as well as under constant temperature condition. The result shows that MS-FCAP is capable of recovering the sampled sensor data accurately and efficiently, reflecting the real-time temperature change in the refrigerated truck during cold chain logistics, and providing effective decision support traceability for quality and safety assurance of frozen and chilled aquatic products.Agro-scientific Researc

Towards Long-Term Monitoring of the Structural Health of Deep Rock Tunnels with Remote Sensing Techniques

Due to the substantial need to continuously ensure safe excavations and sustainable operation of deep engineering structures, structural health monitoring based on remote sensing techniques has become a prominent research topic in this field. Indeed, throughout their lifetime, deep tunnels are usually exposed to many complex situations which inevitably affect their structural health. Therefore, appropriate and effective monitoring systems are required to provide real-time information that can be used as a true basis for efficient and timely decision-making. Since sensors are at the heart of any monitoring system, their selection and conception for deep rock tunnels necessitates special attention. This work identifies and describes relevant structural health problems of deep rock tunnels and the applicability of sensors employed in monitoring systems, based on in-depth searches performed on pertinent research. The outcomes and challenges of monitoring are discussed as well. Results show that over time, deep rock tunnels suffer several typical structural diseases namely degradation of the excavation damaged areas, corrosion of rock bolts and cable bolts, cracks, fractures and strains in secondary lining, groundwater leaks in secondary lining, convergence deformation and damage provoked by the triggering of fires. Various types of remote sensors are deployed to monitor such diseases. For deep rock tunnels, it is suggested to adopt comprehensive monitoring systems with adaptive and robust sensors for their reliable and long-lasting performance

In-situ health monitoring for wind turbine blade using acoustic wireless sensor networks at low sampling rates

PhD ThesisThe development of in-situ structural health monitoring (SHM) techniques represents a challenge for offshore wind turbines (OWTs) in order to reduce the cost of the operation and maintenance (O&M) of safety-critical components and systems. This thesis propos- es an in-situ wireless SHM system based on acoustic emission (AE) techniques. The proposed wireless system of AE sensor networks is not without its own challenges amongst which are requirements of high sampling rates, limitations in the communication bandwidth, memory space, and power resources. This work is part of the HEMOW- FP7 Project, ‘The Health Monitoring of Offshore Wind Farms’. The present study investigates solutions relevant to the abovementioned challenges. Two related topics have been considered: to implement a novel in-situ wireless SHM technique for wind turbine blades (WTBs); and to develop an appropriate signal pro- cessing algorithm to detect, localise, and classify different AE events. The major contri- butions of this study can be summarised as follows: 1) investigating the possibility of employing low sampling rates lower than the Nyquist rate in the data acquisition opera- tion and content-based feature (envelope and time-frequency data analysis) for data analysis; 2) proposing techniques to overcome drawbacks associated with lowering sampling rates, such as information loss and low spatial resolution; 3) showing that the time-frequency domain is an effective domain for analysing the aliased signals, and an envelope-based wavelet transform cross-correlation algorithm, developed in the course of this study, can enhance the estimation accuracy of wireless acoustic source localisa- tion; 4) investigating the implementation of a novel in-situ wireless SHM technique with field deployment on the WTB structure, and developing a constraint model and approaches for localisation of AE sources and environmental monitoring respectively. Finally, the system has been experimentally evaluated with the consideration of the lo- calisation and classification of different AE events as well as changes of environmental conditions. The study concludes that the in-situ wireless SHM platform developed in the course of this research represents a promising technique for reliable SHM for OWTBs in which solutions for major challenges, e.g., employing low sampling rates lower than the Nyquist rate in the acquisition operation and resource constraints of WSNs in terms of communication bandwidth and memory space are presente