Manhole Localization and Condition Diagnostics in Telecom Networks Using Distributed Acoustic and Temperature Sensing

Summary A Case Study on The Monitoring of Dual-String Heavy Oil Producers in Field B, Malaysia, Using Distributed Fibre Optic Acoustic and Temperature Sensing

This paper presents the design and development of a proof of concept (PoC) open-source data logger system for wireless data acquisition via Wi-Fi aimed at bench testing and fault detection in combustion and electric engines. The system integrates multiple sensors, including accelerometers, microphones, thermocouples, and gas sensors, to monitor critical parameters, such as vibration, sound, temperature, and CO2 levels. These measurements are crucial for detecting anomalies in engine performance, such as ignition and combustion faults. For combustion engines, temperature sensors detect operational anomalies, including diesel engines operating beyond the normal range of 80 °C to 95 °C and gasoline engines between 90 °C and 110 °C. These readings help identify failures in cooling systems, thermostat valves, or potential coolant leaks. Acoustic sensors identify abnormal noises indicative of issues such as belt misalignment, valve knocking, timing irregularities, or loose parts. Vibration sensors detect displacement issues caused by engine mount failures, cracks in the engine block, or defects in pistons and valves. These sensors can work synergistically with acoustic sensors to enhance fault detection. Additionally, CO2 and organic compound sensors monitor fuel combustion efficiency and detect failures in the exhaust system. For electric motors, temperature sensors help identify anomalies, such as overloads, bearing problems, or excessive shaft load. Acoustic sensors diagnose coil issues, phase imbalances, bearing defects, and faults in chain or belt systems. Vibration sensors detect shaft and bearing problems, inadequate motor mounting, or overload conditions. The collected data are processed and analyzed to improve engine performance, contributing to reduced greenhouse gas (GHG) emissions and enhanced energy efficiency. This PoC system leverages open-source technology to provide a cost-effective and versatile solution for both research and practical applications. Initial laboratory tests validate its feasibility for real-time data acquisition and highlight its potential for creating datasets to support advanced diagnostic algorithms. Future work will focus on enhancing telemetry capabilities, improving Wi-Fi and cloud integration, and developing machine learning-based diagnostic methodologies for combustion and electric engines.

Markets are forecasted, 1982-1992, for Government and commercial/industrial fiber optic sensors. Included are gyroscope, acoustic, magnetic, position, temperature, pressure, liquid status and other function sensors. Both phase modulated and amplitude modulated sensors are reviewed. Related demand for fiber optic components also is forecasted. Funding, participants and applications are reviewed.Markets are forecasted, 1982-1992, for Government and commercial/industrial fiber optic sensors. Included are gyroscope, acoustic, magnetic, position, temperature, pressure, liquid status and other function sensors. Both phase modulated and amplitude modulated sensors are reviewed. Related demand for fiber optic components also is forecasted. Funding, participants and applications are reviewed.

The Value of Downhole Diagnostics to Perform Successful Permanent Isolation of Flow Potential Sources from Surface

A MEMS (Micro Electro-Mechanical System) acoustic sensor-array quench detection method has been proposed for quick, unobtrusive, and precisely localized detection of quench events in superconducting magnets. A cryogenic probe was designed and built to study the quench event in a gaseous coolant induced in a segment of an HTS tape and verify the effectiveness of acoustic MEMS sensors in detecting such events. Prior work characterized the performance of a commercially available Vesper MEMS microphone, and associated amplifiers, in cryogenic conditions which were utilized in this work. Critically, the application specific integrated circuit (ASIC) used to amplify the MEMS piezo-sensor output signal fails to operate at temperatures below 110 K. Thus, the ASIC was removed from the microphone package and replaced with a custom low temperature preamplifier which we have shown performs with no loss of function at temperatures as low as 10 K. The quench experiment is conducted using the probe within a two-stage Gifford-McMahon (GM) cryocooler in a gaseous helium environment, at cryogenic temperatures between 20 K to 60 K. Three temperature sensors within the sample region monitor temperature of the HTS sample tape and environment. The HTS sample, a 2 mm wide REBCO tape, is mounted within a square G10 tube. A linear array of the repackaged sensors is mounted to the tube to detect the quench event. The quench detection behavior using the sensors is compared with the readings from voltage taps on the sample. The response characteristics of the Vesper acoustic piezo-sensor with the newly identified, cryogenic-safe preamplifier in detecting quench events of REBCO tapes in gaseous helium are presented.

This project successfully demonstrated novel wireless microwave acoustic temperature and pressure sensors that can be embedded into equipment and structures located in fossil fuel power plant environments to monitor the condition of components such as steam headers, re-heat lines, water walls, burner tubes, and power turbines. The wireless microwave acoustic sensor technology researched and developed through a collaborative partnership between the University of Maine and Environetix Technologies Corporation can provide a revolutionary impact in the power industry since it is anticipated that the wireless sensors will deliver reliable real-time sensing information in harsh power plant conditions that involve temperatures up to 1100oC and pressures up to 750 psi. The work involved the research and development of novel high temperature harsh environment thin film electrodes, piezoelectric smart microwave acoustic sensing elements, sensor encapsulation materials that were engineered to function over long times up to 1100oC, and a radio-frequency (RF) wireless interrogation electronics unit that are located both inside and outside the high temperature harsh environment. The UMaine / Environetix team have interacted with diverse power plant facilities, and identified as a testbed a local power generation facility, which burns municipal solid waste (MSW), the Penobscot Energy Recovery Company (PERC), Orrington, Maine. In this facility Environetix / UMaine successfully implemented and tested multiple wireless temperature sensor systems within the harsh-environment of the economizer chamber and at the boiler tubes, transferring the developed technology to the power plant environment to perform real-time sensor monitoring experiments under typical operating conditions, as initially targeted in the project. The wireless microwave acoustic sensor technology developed under this project for power plant applications offers several significant advantages including wireless, battery-free, maintenance-free operation, and operation in the harsh-environment of power plant equipment up to about 1100 oC. Their small size and configuration allows flexible sensor placement and embedding of multiple sensor arrays into a variety of components within power systems that can be interrogated by a single RF unit. The outcomes of this project and technological transfer respond to a DOE analysis need, which indicated that if one percent efficiency in coal burning is achieved, an additional 2 gigawatt-hours of energy per year is generated and the resulting coal cost savings is $300 million per year, also accompanied by a reduction of more than 10 million metric tons of CO2 per year emitted into the atmosphere. Therefore, the developed harsh environment wireless microwave acoustic sensor technology and the technological transfer achievements that resulted from the execution of this project have significant impact for power plant equipment and systems and are well-positioned to contribute to the cost reduction in power generation, the increase in power plant efficiency, the improvement in maintenance, the reduction in down-time, and the decrease in environmental pollution. The technology is also in a position to be extended to address other types of high-temperature harsh-environment power plant and energy sector sensing needs.

To intelligently control and adjust LED lighting network system, to make use of the sensor technology, wireless communication protocol, intelligent control technology, it overcomes the difference of the various technical standard, the lack of communication that is problem to impede the promotion of the intelligent lighting. Extending the conventional control method with intelligent technology, planning scheme according to the functions, the design carried out the three respectively subsystems, then the debugging and fusion of the software and hardware. By using the method of software, the high or low level of I/O output can simulate PWM control signal whose duty cycle of the PWM can be adjusted. The brightness data collected from the environment is changed into PWM control information. Using acoustic sensors and temperature sensors and voltage sensors to further adjust the distribution curves of the light and color, these collected data are turned into PWM fine tuning signal. The wireless intelligent lighting control system designed can well solve problems such as the large regional span, the high cost of the network transmission and the uncertain of the node number.

In deep coal seams where intact coal interbeds with tectonic coal, the advancement of excavation working faces towards tectonic coal zones, under in-situ stress and gas pressure, is highly susceptible to induced coal and gas outbursts. To investigate the influence of distance on outburst mechanisms during the approach of excavation faces to tectonic coal zones, this study focuses on an outburst-prone coal seam from a mine in Henan. A “tectonic coal mass” experimental model was established to simulate tectonic coal zones embedded within intact coal. Using a self-developed true triaxial coal and gas outburst simulation test system, outburst simulation experiments were conducted at varying approaching distances (L) ranging from 0 to 35 mm between the excavation face and the tectonic coal zone. Throughout the experiments, acoustic emission (AE) technology and temperature sensors were employed to monitor the entire outburst process. New parameters, including the cumulative energy growth rate at each stage and the primary and secondary peaks of AE energy, were defined. Based on the relationship between the primary and secondary AE energy peaks, precursor coefficients for low, medium, and high outburst risks were proposed. The study analyzed AE evolution patterns and temperature characteristics within the tectonic coal zone at different approaching distances. The research findings indicate: (1) A critical approaching distance exists when the excavation face approaches tectonic coal zones. Under consistent in-situ stress conditions, the unit outburst intensity demonstrates a quadratic relationship with the approaching distance, initially increasing and then decreasing. A critical value of unit outburst intensity is observed at an approaching distance of 20 mm. (2) During the outburst incubation stage, the cumulative energy growth rates (<italic>K</italic>₁, <italic>K</italic>₂, <italic>K</italic>₄) exhibit a distinct two-phase trend in relation to the approaching distance: a positive correlation between 10–20 mm and a negative correlation between 20–35 mm. The energy release intensity (<italic>K</italic>₄/<italic>K</italic>₃) during the late triggering phase is 7.17–15.59 times greater than that during the early phase (<italic>K</italic>₂/<italic>K</italic>₁). The approaching distance and AE energy release initially increase before subsequently decreasing. (3) The temperature increment during outbursts in the tectonic coal zone first rises and then declines as the approaching distance increases. In the proximal zone, the temperature increase is minimal and primarily influenced by in-situ stress. In the intermediate zone, accelerated fracture propagation and continuous gas pressure loading lead to a significant temperature rise. In the distal zone, the temperature increase diminishes due to the predominant effect of gas desorption endothermic behavior.

Monitoring of the health plays a crucial role in the medical industry; an application for monitoring the children from distances by their parents is developed. Different types of sensors are used like; a temperature sensor is a device typically is an RTD. Heartbeat senor works on the principle of the basic optoelectronics. The Raspberry Pi 3 B + Model are presently the latest, faster, and easier to use. Massimo’s acoustic respiration sensor the RAS-45 for Rainbow Acoustic Monitoring scored FDA clearance for infants and patients 10kg and under. We use an IOT board that is used for online applications with many advantages and authorize the embedded system. We use ADC to convert analog values to digital values as output.

Polysilicon microbeams in integral vacuum enclosures on silicon substrates have optical and mechanical properties that provide excellent opportunities for fiber-optic sensors. The microbeam, shell, and silicon substrate form a structure with Fabry-Perot-like properties that functions as an optomechanical modulator. When the beam vibrates incident light is modulated and reflected light is used to sense vibration of the beam. Thus, the structure can be used as a mechanical vibration or acoustic emission sensor. Microbeams attached to the substrate at both ends are highly strain sensitive and form the basis of a variety of sensors, including pressure sensors, accelerometers, strain, vibration, and temperature sensors. Excitation at the wafer level by a polymer film piezoelectric transducer provides a simple non-contact optical method for testing the microbeams before the water is cut into sensor die. Modulated light from a laser diode can also be use to excite the microbeams into resonance. The test results suggest that optically resonant microbeams can be used for low-cost precision fiber-optic sensors. Fiber-optic sensors are especially attractive for aerospace applications because optical fibers provide wide-bandwidth communication capability while eliminating electromagnetic interference (EMI), ground loops, and shielding requirements.

Superimposed temperature variations and dynamic strain applied through a 400 Hz acoustic signal on a 195 m single-mode fiber section are successfully measured using a coherent correlation optical time domain reflectometry as an interrogator.

This paper proposes a quality fluctuation problem of the Three-Dimensional Printing 3DP process, including an online prediction method with multi-source information and machine learning. Based on the NVIDIA Jetson platform, it puts forward an embedded sensing system structure design equipped with temperature, acoustic, and high-speed visual sensors. It makes provision for real-time acquisition, synchronization, and fusion of heterogeneous time-series data. In this regard, the system supports edge computing, highfrequency sampling, and precise timestamp alignment to make sure that the collection of data should be of low latency and high fidelity that can easily be deployed in industries. Next, a dedicated pipeline for preprocessing and feature extraction of high-dimensional, noisy, and asynchronous signals is proposed: time-frequency domain fusion, dimensionality reduction, and deep feature representation. On this basis, with the help of public and simulated datasets, a multimodal prediction framework was built using CNN, LSTM, and attention mechanisms to effectively capture spatial-temporal patterns, dynamic anomalies, and correlations involving quality in the printing process. Model parameters are optimized through Bayesian hyperparameter tuning, whereas performance is evaluated through k-fold cross-validation across several metrics such as accuracy, F1-score, and RMSE. The experimental results showed that the framework had high precision, stability, and generalization under different process conditions. The proposed method establishes a deployable algorithmic foundation by tightly integrating embedded sensing, heterogeneous data fusion, and advanced machine learning techniques. It provides support for dynamic quality monitoring and process control with strong engineering applicability and adaptiveness to various 3DP platforms, materials, and use scenarios. This work contributes to intelligent additive manufacturing with improved reliability and automation.

Applying Distributed Acoustic Sensing (DAS) to sense offshore telecom cables offers a unique opportunity to expand permanent seismological networks far offshore with an unprecedented instrumentation density but presents challenges in managing and processing data. This study introduces a workflow for automating earthquake catalog generation by combining DAS arrays with regional on‐land seismic networks. We apply it to the ABYSS offshore permanent observatory in central Chile, which began continuous recording in October 2023 on three 150‐km segments of a regional dark‐fiber telecommunication network. Our workflow includes state‐of‐the‐art components originally developed for conventional seismic sensor data. We first show that PhaseNet, pretrained on conventional seismic stations, can perform automatic phase picking on DAS strain‐rate data converted to velocity. In a second step, we demonstrate that the GaMMA associator, tuned with parameters adapted to our dense data set, can cluster picked arrivals of small and moderate‐sized earthquakes. We then adapt NonLinLoc to compute hypocenter locations using numerous phase picks, combining DAS and onland seismic data for improved accuracy. Finally, we show that reliable local magnitudes can be estimated on DAS recordings using the Richter scale. The earthquake catalog is stored in an SQL database using QuakeML formalism and is accessible via a user‐friendly webpage. By applying the workflow to onshore station recordings, we evaluate the benefits of adding permanent seafloor DAS instrumentation, demonstrating that offshore DAS significantly improves the detection of small‐magnitude earthquakes and reduces the offshore magnitude of completeness to 1.3.

We have deployed the Bonne Bay Observatory (B20) in Bonne Bay, a fjord on the west coast of Newfoundland, in the spring of 2004. The scientific goal of this system is to provide continuous, year-round, real-time data to enhance our understanding of the coupling between the physical and biological environment in this sub-Arctic fjord, which is ice covered for several months each year. The Observatory permits investigators to schedule and interactively manage real time data acquisition and control of the network of sensors. Serving a multidisciplinary team, the instrument array is diverse, including acoustic sensors to determine currents, bubble distribution and plankton abundance, video to determine plankton and benthos species abundance, and sensors for temperature, salinity, chlorophyll fluorescence, carbon dioxide, oxygen, inorganic nutrient concentrations and spectral irradiance. Instruments are deployed on fixed and moveable structures and on an underwater profiling winch. The control and telemetry system includes a power distribution sub-system and TCP/IP based network consisting of two local area networks, one on shore linking data acquisition and control computers and one underwater connecting the sensors, joined by an armored 1.4 km electro-optic cable. The cable provides up to two 100BASE-FX network connections and 2 kW of power to the underwater systems. The system is designed to operate autonomously, and to be controlled remotely by the DACNet ocean observatory operating system. The underwater hardware elements are modular, accommodating guest instruments at spare Ethernet and serial ports. The paper describes the system design with description of instrumentation deployed underwater for the first time, lessons learned during design and deployment and presents preliminary samples of the data collected.

Fiber-optic sensing of deformation and temperature has characteristics that make it a promising, emerging technology for applications in underground rock engineering. In general, there are two main types of fiber-optic sensors, discrete and distributed. Discrete sensors respond to strain and temperature, by changes in spacing of a diffraction grating in the fiber or in a cavity between two ends of fiber that are micrometer-scale. In distributed sensing, the fiber optic cable itself is the strain and temperature sensor. Distributed sensors are based on Rayleigh, Raman, and Brillouin scattering of light from the fiber-optic cable itself. The backscattered light can be analyzed for strain and/or temperature with a spatial resolution of one meter. In this chapter, the physics underlying Fiber Bragg Gratings (FBG), SOFO, Distributed Temperature Sensing (DTS), Distributed Strain and Temperature (DST), and Distributed Acoustic Sensing (DAS) are described. Several prototype case studies of fiber-optic monitoring in underground mines, recent developments in fiber-optic extensometers, and fiber-optic sensing in boreholes in the oil and gas industry are reviewed.