Weak abnormal vibration monitoring of multiple reference sensors for OPGW lines based on TOC optimization

The ever-growing development of sensor technology brings new opportunities to investigate impacts of the outdoor environment on human health at the individual level. However, there is limited literature on the use of multiple personalized sensors in urban environments. This review paper focuses on examining how multiple personalized sensors have been integrated to enhance the monitoring of co-exposures and health effects in the city. Following PRISMA guidelines, two reviewers screened 4898 studies from Scopus, Web of Science, ProQuest, Embase, and PubMed databases published from January 2010 to April 2021. In this case, 39 articles met the eligibility criteria. The review begins by examining the characteristics of the reviewed papers to assess the current situation of integrating multiple sensors for health and environment monitoring. Two main challenges were identified from the quality assessment: choosing sensors and integrating data. Lastly, we propose a checklist with feasible measures to improve the integration of multiple sensors for future studies.

Mobile and wearable sensors for data-driven health monitoring system: State-of-the-art and future prospect

Recently a variety of vibration monitoring devices based on MEMS (micro electro-mechanical system) 3-axis acceleration sensor has been introduced and is gradually replacing analog wire-type geophones for blasting vibration monitoring. Blasting vibration monitoring tasks generally require frequent movement of the monitoring devices. Since accurate device set along the vertical axis is essential at a new location, acceleration sensors sensitive to the gravitational acceleration are not suitable for accurate monitoring of the blasting vibration. In this study, the vibration monitoring system with a 3-axis MEMS acceleration sensor is developed for wireless mesh network monitoring. Individual monitoring units are equipped with an algorithm for reorientation along the direction of gravity once they are placed on a particular baseline. The algorithm aims at automatically adjusting the z-axis and resetting the zero offset value altered after each blasting vibration monitoring and relocation. With this feature, it shows individual unit can be applied as conventional portable devices as well. In addition, comparative studies are also carried out along with conventional units for 3-axis acceleration and primary frequency analysis. There are several advantages of the developed system. Firstly, this system has been designed for easy installation and wireless remote management to provide readings and alerts when the user-defined allowable limit is exceeded. Secondly, due to remote management, it can improve staff safety, reduce human resources, and save time and cost. Thirdly, this system can be positioned over a large area as each sensor can act as a repeater. Finally, multiple sensors can be installed to measure various locations monitoring at the same time. Furthermore, without the cables to interface with operations or accidental damage, this system improves safety and reduces maintenance costs. The readings from the multiple sensors deployed at target locations are transmitted to the management node connected to the PC. Thus, all the live data can be seen on the PC. This system is built to be deployed on mining and construction sites, tunnel, bridges, and other structures. The system is designed with the ultimate goal of understanding challenges and provide solutions to protect assets by the low-cost system with high accuracy and reliability.

In this paper, we present an RIP module with the features of supporting multiple inductive sensors, no variable frequency LC oscillator, low power consumption, and automatic gain adjustment for each channel. Based on the method of inductance measurement without using a variable frequency LC oscillator, we further integrate pulse amplitude modulation and time division multiplexing scheme into a module to support multiple RIP sensors. All inductive sensors are excited by a high-frequency electric current periodically and momentarily, and the inductance of each sensor is measured during the time when the electric current is fed to it. To improve the amplitude response of the RIP sensors, we optimize the sensing unit with a matching capacitor parallel with each RIP sensor forming a frequency selection filter. Performance tests on the linearity of the output with cross-sectional area and the accuracy of respiratory volume estimation demonstrate good linearity and accurate lung volume estimation. Power consumption of this new RIP module with two sensors is very low. The performance of respiration measurement during movement is also evaluated. This RIP module is especially desirable for wearable systems with multiple RIP sensors for long-term respiration monitoring.

Development of two-layer multiple transmitter fibre optic bundle displacement sensor and application in structural health monitoring

Due to their small size and flexibility fiber optics can be embedded into composite materials with little negative effect on strength and reliability of the host material. Fiber optic sensors such as Fiber Bragg Gratings (FBG) or Etched Fiber Sensors (EFS) can be used to detect a number of relevant parameters such as flow, degree of cure, quality and structural health throughout the life of a composite component. With a detection algorithm these embedded sensors can be used to detect damage in real time while the component remains in service. This paper presents the research being conducted on the use of fiber optic sensors for process and Structural Health Monitoring (SHM) of Resin Transfer Molded (RTM) composite structures. Fiber optic sensors are used at all life stages of an RTM composite panel. A laboratory scale RTM apparatus was developed with the capability of visually monitoring the resin filling process. A technique for embedding fiber optic sensors with this apparatus has also been developed. Both FBGs and EFSs have been embedded in composite panels using the apparatus. EFSs to monitor the fabrication process, specifically resin flow have been embedded and shown to be capable of detecting the presence of resin at various locations as it is injected into the mold. Simultaneously these sensors were multiplexed on the same fiber with FBGs, which have the ability to measure strain. Since multiple sensors can be multiplexed on a single fiber the number of ingress/egress locations required per sensor can be significantly reduced. To characterize the FBGs for strain detection tensile test specimens with embedded FBG sensors have been produced. These specimens have been instrumented with a resistive strain gauge for benchmarking. Both specimens and embedded sensors were characterized through tensile testing. Furthermore FBGs have been embedded into composite panels in a manner that is conducive to detection of Lamb waves generated with a centrally located PZT. To sense Lamb waves a high speed, high precision sensing technique is required to acquire data from embedded FBGs due to the high velocities and small strain amplitudes of these guided waves. A technique based on a filter consisting of a tunable FBG was developed. Since this filter is not dependant on moving parts, tests executed with this filter concluded with the detection of Lamb waves, removing the influence of temperature and operational strains. A damage detection algorithm was developed to detect and localize cracks and delaminations.

Combining multiple proximal sensors within a wireless sensor network (WSN) enhances our capacity to monitor vegetation, compared to using a single sensor or non-networked setup. Data from sensors with different spatial and temporal characteristics can provide complementary information. For example, point-based sensors such as multispectral sensors which monitor at high temporal frequency but, at a single point, can be complemented by array-based sensors such as digital cameras which have greater spatial resolution but may only gather data at infrequent intervals. In this article we describe the successful deployment of a prototype system for using multiple proximal sensors (multispectral sensors and digital cameras) for monitoring pastures. We show that there are many technical issues involved in such a deployment, and we share insights relevant for other researchers who may consider using WSNs for an operational deployment for pasture monitoring under often difficult environmental conditions. Although the sensors and infrastructure are important, we found that other issues arise and that an end-to-end workflow is an essential part of effectively capturing, processing and managing the data from a WSN. Our deployment highlights the importance of testing and ongoing monitoring of the entire workflow to ensure the quality of data captured. We demonstrate that the combination of different sensors enhances our ability to identify sensor problems necessary to collect accurate data for pasture monitoring.

Recent advancement in signal processing and information technology has resulted in the use of multiple sensors for the effective monitoring of tool conditions, which is the most crucial feedback information to the process controller. Interestingly, the abundance of data collected from multiple sensors allows us to employ various techniques such as feature extraction, selection, and classification methods for generating such crucial information. While the use of multiple sensors has improved the accuracy in the classification of tool conditions, design of tool condition monitoring system (TCM) for reduced complexity and increased robustness has been rarely studied. Therefore, this paper studies the design of effective multisensor-based TCM when machining 4340 steel by using a multilayer-coated and multiflute carbide end mill cutter. Multiple sensors tested in this paper include force, vibration, acoustic emission, and spindle power sensor for the time and frequency domain data. In addition, two feature selection methods and three classifiers with a machine ensemble technique are considered as design components. Importantly, different fusion methods are evaluated in this paper: (1) decision level fusion and (2) feature level fusion. The experimental results show that the design of TCM based on the feature level fusion can significantly improve the accuracy of the tool condition classification. It is also shown that the highest accuracy can be achieved by using force, vibration, and acoustic emission sensor together with correlation-based feature selection method and majority voting machine ensemble.

Multiple sensor expert system for diagnostic reasoning, monitoring and control of mechanical systems

This paper describes a novel approach to sensor and actuator integrity monitoring. Multiple sensor and actuator faults can be detected and isolated. Most importantly, fault magnitudes can be correctly estimated. Our approach is robust to disturbance and does not require additional sensors.

For aquaculture systems, pH is the prime quality indicator and is highly related to other water quality indicators like ammonia and ammonium ions. The available pH sensors using chemical references are not suitable for continuous in situ monitoring of aquaculture systems due to their frequent calibration requirement and high cost. This research develops a pH sensor with temperature compensation implementing a machine learning (ML) algorithm. Unlike traditional methods, this sensor utilizes electronic calibration, eliminating the need for chemical calibration and ongoing maintenance efforts. The application of this low-cost sensor is particularly well suited for in situ aquaculture scenarios, where multiple local sensor nodes operate under the control of a master node. The test results of the developed sensor show an improved sensitivity from 0.288 µA/pH to 0.316 µA/pH compared to the available pH sensors. Additionally, the response time has been improved from 1 s to 125 ms, showcasing the suitability of this pH sensor for real-time water quality monitoring of aquaculture applications.

The fusion of multiple monitoring sensors is crucial to improve the accuracy and robustness of machinery fault diagnosis. However, existing fault diagnosis methods may underestimate the interference of noise in the multi-sensor fusion process, leading to unsatisfied performance. To handle this problem, this paper proposes a deep model based on the frequency adaptive wavelet pyramid. First, an adaptive frequency selection strategy is designed to prune the seriously polluted frequencies and only retain some key frequencies. Then, the self-attention mechanism is used to perform information fusion on the selected frequency bands of different sensors. Finally, a wavelet fusion pyramid is adopted by repeating the fusion process at multiple wavelet decomposition levels. In this way, different sensors can be fused in a more fine-grained manner. The experimental results on two multi-sensor-based fault diagnosis datasets demonstrate the anti-noise capability of the proposed method.

This paper presents the design and development of synchronized wireless sensors for monitoring impact events which may occur on composite airframe. Rare, random and transitory impact events, such as bird strike, runway debris or tool drops during maintenance, can introduce barely visible damage to airframes (especially composite structures) which may reduce its load bearing capacity. An innovative wireless sensing skins concept consisting of piezoelectric transducers and wireless transceivers based on Bluetooth Low Energy (BLE) is proposed to monitor the operational condition on-board continuously. An event-based mechanism is adopted for low-power operation while maintaining the sensing performance. The sampling frequency can be set up to 200 kHz while the overall idle state current can be as low as 41 µA. A synchronization method is proposed and implemented to share common time stamps among multiple wireless sensors. The performance is validated by the results from an oscilloscope. The proposed wireless sensors demonstrate a potential solution for the next generation aircraft structural health monitoring (SHM).

A low-cost autonomous optical sensor for water quality monitoring

Electronic skins, as the integration of multiple distinct sensors, have aroused broad interests owing to their great potential in sensing applications. However, problems including the interference between sensing components and the difficulty in synchronous monitoring are practically encountered when they are applied to mixed signals. In this work, efforts are devoted to trouble‐free technical strategies for laminating three sensors with different sensing abilities into a skin‐like electronic device. The use of ionic liquid, combined with particular circuit topologies, ensures the reliable stability against mechanical disturbance during the real‐time sensing tests. The intrinsic layered structure and three independent sensing functions of natural skins are successfully presented by this particular device in which three sensors with the ease of preparation are spatially integrated. The changes of temperature, pressure, and infrared light can be recorded simultaneously yet without mutual signal interference. The perfect integration of multiple functional sensors into a single skin‐like device without any signal interference makes an important progress for pursuing the goal of future electronic skins that can practically be used as skin.