Long-Reach DWDM-Passive Optical Fiber Sensor Network for Water Level Monitoring of Spent Fuel Pool in Nuclear Power Plant

High Accuracy and Cost-Effective Fiber Optic Liquid Level Sensing System Based on Deep Neural Network

In this paper, a novel liquid level sensing system is proposed to enhance the capacity of the sensing system, as well as reduce the cost and increase the sensing accuracy. The proposed sensing system can monitor the liquid level of several points at the same time in the sensing unit. Additionally, for cost efficiency, the proposed system employs only one sensor at each spot and all the sensors are multiplexed. In multiplexed systems, when changing the liquid level inside the container, the float position is changed and leads to an overlap or cross-talk between two sensors. To solve this overlap problem and to accurately predict the liquid level of each container, we proposed a deep neural network (DNN) approach to properly identify the water level. The performance of the proposed DNN model is evaluated via two different scenarios and the result proves that the proposed DNN model can accurately predict the liquid level of each point. Furthermore, when comparing the DNN model with the conventional machine learning schemes, including random forest (RF) and support vector machines (SVM), the DNN model exhibits the best performance.

Big datasets of optical-wireless cyber-physical systems for optimizing manufacturing services in the internet of things-enabled industry 4.0

The Industry 4.0 revolution is aimed to optimize the product design according to the customers' demand, quality requirements and economic feasibility. Industry 4.0 employs advanced two-way communication technologies for optimizing the manufacturing process to increase the sales of the products and revenues to cope the existing global economy issues. In Industry 4.0, big data obtained from the Internet of Things (IoT)-enabled industrial Cyber-Physical Systems (CPS) plays an important role in enhancing the system service performance to boost the productivity with enhanced quality of customer experience. This paper presents the big datasets obtained from the Internet of things (IoT)-enabled Optical-Wireless Sensor Networks (OWSNs) for optimizing service systems' performance in the electronics manufacturing Industry 4.0. The updated raw and analyzed big datasets of our published work [3] contain five values namely, data delivery, latency, congestion, throughput, and packet error rate in OWSNs. The obtained dataset are useful for optimizing the service system performance in the electronics manufacturing Industry 4.0.

16 Ch × 200 GHz DWDM-Passive Optical Fiber Sensor Network Based on a Power Measurement Method for Water-Level Monitoring of the Spent Fuel Pool in a Nuclear Power Plant

This paper presents a remote 16 Ch × 200 GHz dense wavelength division multiplexing (DWDM)-passive optical fiber sensor (OFS) network. We particularly investigate the remote water-level monitoring capability of the OFS network based on an optical power measurement that features simplicity and a fast processing speed. The OFS network utilizes a seeded amplified spontaneous emission (ASE) light that is spectrum-sliced and distributed by an arrayed waveguide grating (AWG) towards multiple sensing units (SU), where each SU is installed at a different height in the water pool. Then, each SU reflects either of the two different optical powers according to the medium (air vs. water) back to the monitoring station. Therefore, the total received optical power at the monitoring station linearly changes according to the water level. We can simply recognize the water level by utilizing the optical power meter (OPM) at the monitoring station rather than the optical spectrum analyzer (OSA), which is bulky and expensive and requires a relatively long processing time. Consequently, we can reduce the system complexity, processing time, and cost (both installation and maintenance). However, the OPM-based OFS network requires a new methodology to derive the water level from the measured optical power. Thus, we come up with the reference-to-power ratio (RPR) analysis, which can be used for the maximum distance analysis as well as water level recognition. Based on the new reception architecture supported by the new post-processing scheme, the OFS network can distinguish 17 different water levels of the SFP at the monitoring station, which is >40 km away from the SFP, without using any active devices (such as optical amplifiers) at the remote places.

Multiplexed Passive Optical Fiber Sensor Networks for Water Level Monitoring: A Review

Water management is a critical mission required to protect the water resources that is essential in diverse industrial applications. Amongst a variety of parameters such as level (or depth), temperature, conductivity, turbidity, and pH, the water level is the most fundamental one that needs to be monitored on a real-time basis for securing the water management system. This paper presents an overview of water level monitoring technologies based on optical fiber sensor (OFS) networks. Firstly, we introduce and compare the passive distributed and quasi-distributed (discrete) sensor networks with the recent achievements summarized. The performance (i.e., sensing range and resolution) of the OFS networks can be enhanced through diverse multiplexing techniques based on wavelength, time, coherence, space, etc. Especially, the dense wavelength division multiplexing (DWDM)-based sensor network provides remote sensing (where its reach can be extended to >40 km) with high scalability in terms of the channel number that determines the spatial resolution. We review the operation principle and characteristics of the DWDM-based OFS network with full theoretical and experimental analysis being provided. Furthermore, the key system functions and considerations (such as the link protection from physical damages, self-referencing, management of sensing units, and so on) are discussed that could be a guideline on the design process of the passive OFS network.