Multi-Sensor Architectures

The practical application of human nose for fragrance recognition is severely limited by the fact that our sense of smell is subjective and gets tired easily. Consequently, there is a significant need for an instrument that can be a substitution of the human sense of smell. Development of an electronic nose devices is an active area of research starting from pioneering research of Dodd and Persuad in the mid-1980s. Such systems are used in growing number of applications. They comprise an array of several electrochemical gas sensors with partial specificity and a pattern recognition algorithms. Since most of the existing types of sensors are characterized by low selectivity, they can all be used in the construction of electronic noses. Most electronic nose systems are based only on one type of gas sensors. However, different types of sensors have different advantages and disadvantages. In this article, an electronic nose system for the purpose of incorporating different types of sensors has been proposed developed. Such approach allows to benefit from advantages of different type of sensors, and thus improve the operation of the entire system.

Fiber optic data transmission has caused revolutionary developments in the current information society. It was also an eye opener for the Oil & Gas industry when fiber optic-based Distributed Temperature Sensing was introduced in the nineties. Temperature profiles over the entire length of the wellbore could suddenly be monitored in real-time to deliver data that is used for troubleshooting and production optimization. Ongoing research in fiber optic sensing technology for applications with extreme operational conditions (such as aerospace or nuclear fusion) will further boost the development of fiber optic multi-parameter sensing systems. Innovations like these will particularly benefit the Oil & Gas industry in situations where conventional electronic sensors are not technically feasible or cost efficient. In multi-parameter sensing systems of the future, different types of fiber optic sensors (e.g. flow, pressure, acoustic, chemical, vibration, gravity) can be manufactured and read out via one single optical fiber. Data from the different types of sensors will be merged and processed via special data-mining algorithms to generate unambiguous information for faster and better decision making. Judging on the speed of developments witnessed today, we believe there will be a second fiber optic sensing revolution in the Oil & Gas industry in the near future. In this paper, we will give some in-house examples of fiber optic innovations from both in and outside the Oil & Gas industry to demonstrate the versatility and potential of fiber optic technology. Introduction Increasing energy demand combined with scarcity means that oil must be produced from new and often more complex reservoirs. These reservoirs may consist of many sections separated by geological faults or the oil may be very viscous, making production more difficult or economically impossible with current technology. This implies that well and production strategies are becoming increasingly complex and require accurate and reliable input for decision making. Maturing assets develop complex ‘time critical’ process dynamics such as production decline, liquid loading and salt precipitation creating the need for real-time monitoring and control. This measurement and control technology must give a more accurate understanding of the key processes involved. It should also improve asset diagnostics to lead to improved production control and recovery strategies without compromising reliability or safety. Furthermore the industry has to deal with increasingly complex environmental and safety regulations and an increasing shortage of expert personnel. We believe that innovations in fiber optic sensing technology have the potential to help better understand the key processes behind decision-making in the oil and gas industry. This insight is based on many projects both inside and outside the oil and gas industry related to monitoring, process modeling and data processing. These new insights are essential to control and optimize the ‘new’ Oil & Gas industry when one considers the challenges that lie ahead. Before we focus on these upcoming fiber optic technologies for the oil and gas industry, we would like to illustrate the versatility and potential of fiber optic technology based on applications from other industries and their applicability to the oil and gas industry. Fiber optic technology is a proven technology but needs to be adapted and modified to the restricting and demanding applications of the oil and gas industry. These applications also give rise to innovations as will be discussed later.

Flex sensors are frequently used as wearable tools for unobtrusively tracking human joint movements. Among all the flex sensor types, the resistive ones are the mostly adopted thanks to their electrical and mechanical properties capable to furnish electrical resistance values related to the amount of mechanical flexion. In particular, resistive flex sensors have been finding many applications when embedded into gloves, in order to evaluate fine flexion/extension movements of the finger joints. Within this frame, here we investigate the possible utilization of a different type of flex sensors embedded into gloves, i.e., the stretch ones, since the stretch sensors change in resistance proportionally to their stretch that can be just obtained when laid on-top of a finger joint. In such a view, here we compare the characteristics of commercial flex and stretch sensors obtained by means of an ad-hoc setup and protocol. Results demonstrate the different peculiarities of the two different types of sensors, so to determine when it is convenient to adopt one type instead of the other.

An intelligent multisensor integration and fusion model that uses fuzzy logic is developed. Measurement data from different types of sensors with different resolutions are integrated and fused based on the confidence in them derived from information not usually used in data fusion, such as operating temperature, frequency range, fatigue cycles, etc. These are fed as additional inputs to a fuzzy inference system (FIS) that has predefined membership functions for each of these variables. The output of the FIS are weights that are assigned to the different sensor measurement data that reflect the confidence in the sensor's behavior and performance. A modular approach is adopted. It allows adding or deleting a sensor, along with its fuzzy logic controller (FLC), anytime without affecting the entire data fusion system. This paper presents a preliminary model that fuses the data from three different types of sensors that monitor the strain at a single location in a cantilever beam. This will be later extended to sensors that will be fixed at different locations on the same beam. The results from the proposed work are a stepping stone toward the development of generic autonomous sensor models that are capable of data interpretation, self-calibration, data fusion from other sources, and even learning so as to improve their performance with time. This work is aimed at the development of smart structural health monitoring systems, but has applications in diverse fields such as robotics, controls, target tracking, and biomedical imaging.

This paper presents a variable gain potetiostat designed for the electrochemical control of Dissolved Oxygen (DO) sensors. The design is targeted for implementation using MIMOS 0.35 um CMOS process technology at 3.3V. The potentiostat amplifier for dissolved oxygen utilizes three electrodes (working, reference and counter) which work together to form the electrochemical reaction. There are several types of DO sensor available including membrane-based, also known as Clark electrode, and microelectrode-based. This results in different sensitivity and output current when different types of sensor are used on the same test solution. The variable gain feature is added in the design to cater to different DO sensors that are available in the market. This design implements four different gain values to accommodate four different current ranges. The resistor values chosen largely depend on the type of DO sensor used and the range of output current it produces. The transimpedance amplifier used has also been simulated in the post-extracted view across process corners to ensure its compatibility with the different feedback resistor values. The complete integrated design is then simulated to be working within a power supply of 2.2 V to 3.6 V, temperature of 0 to 90°C and has an operating range of 0 to 100 uA sensor current. The final chip has an area of 1.7 mm × 1.7 mm and has been simulated to be in working order.

The Wave Crest Sensor Inter-comparison Study (WACSIS) employed many different types of sensors to measure ocean surface waves. These sensors were attached to a steel jacket platform located in 18 m deep water about 9 km from the Dutch coast. To investigate the suitability and consistency of different wave sensors, wave characteristics at the locations of some sensors were deterministically predicted based on three other wave measurements using a Directional Hybrid Wave Model (DHWM). The comparisons between the predictions and related measurements were applied to the examination of the consistency among different sensors. As an example, a mistake of the orientation of a current meter has been found through the comparisons. This study also demonstrates that consistency among different types of wave sensors is satisfactory and the DHWM is valuable to the analysis of field wave measurements.

Identification of inlet pipe blockage level in centrifugal pump over a range of speeds by deep learning algorithm using multi-source data

In this paper, a newly designed wireless sensing network platform for structural monitoring applications is presented, and the results of a validation test performed in the authors' laboratory are reported. The main features offered by the proposed wireless platform are the capability of real-time and multi-channel data transmission, the high compatibility to different types of sensors, a highly efficient power-supply, and low-cost. Instead of adopting the most commonly used commercial wireless modems, the wireless communication is pursued by implementing an optimized and customized solution based on a recent System on Chip wireless transceiver. The Frequency Division Multiplexing method is exploited to ensure the real-time feature of the multi-channel data transmission. A simple and practical point-to-point topology is pursued. The usage of switching regulators which feature low quiescent current, highly efficient power conversion, an adjustable output voltage, and a high output power make this platform suitable for both low-power and non low-power structural monitoring applications involving different types of sensors. In order to validate the prototyped platform, a laboratory test is performed. The measurements of the acceleration response of a simple 3-storey structure mounted on a shaking table are acquired from both a wired DAQ system and the developed wireless platform. The data comparison enables to validate the efficacy of the real-time, multi-channel wireless transmission.

A heterogeneous wireless sensor network (WSN) consists of sensors with unequal ranges of sensing and/or communication. In a dense WSN, a part of the region covered by a sensor may also be covered redundantly by a neighbouring sensor. In this paper, we analyse the redundancy in the coverage of a heterogeneous WSN and define the redundancy degree of a sensor. We follow a probabilistic approach to derive the expected redundancy degree of a sensor with a given number of sensors of each type in the neighbourhood. We demonstrate the accuracy of the analysis, and study the impact of the number of sensors of different types on the expected redundancy degree with numerical and simulation results. We also demonstrate an application of the redundancy analysis in the design of a heterogeneous WSN. We propose an algorithm to determine the minimum number of sensors of different types required to satisfy the desired coverage ratio and simultaneously minimise the cost of the network.

Multisensor information fusion technology is an advanced processing method that is different from classical data processing technology. Capability and operational accuracy, in order to make an effective comprehensive evaluation of environmental quality, the environmental monitoring system built by NB-IoT technology and sensor technology is used to conduct multisensor data fusion research on the collected environmental factors such as temperature, humidity, formaldehyde, PM2.5, and TVOC. At the same time, the two-level parallel fusion method is adopted to evaluate the environmental quality. Before data fusion, median filtering is used to eliminate abnormal data. Then, the Kalman filter algorithm is used to fuse multiple sets of similar sensors to obtain the best value of it. Finally, the fuzzy comprehensive evaluation method is used to fuse the different types of sensors at the decision-making level, in which the weight value is determined by the entropy method and the membership function is Gaussian. The different environment scenarios are tested by using the above algorithms, and the simulation results show that multisensor data fusion can obtain more abundant and effective environmental information, overcome the simplicity and limitation of single-factor sensor for environmental quality assessment, and improve the reliability and accuracy of the overall environmental quality assessment.

Electrical capacitance tomography (ECT) is one of process tomography technique which developed rapidly in recent years. Classical electrical capacitance tomography is an imaging technique to obtain the permittivity distribution inside a closed pipe by using the electrodes mounted outside. However, it suffers from the poor sensitivity in the center. In the present paper, a new type of sensor with an internal plate electrode arrangement positioned in the pipe's center is proposed. An external-electrode sensor and a sensor with an annular internal electrode are also investigated. In order to compare the three different types of sensors, sensitivity maps are generated and the Landweber iterative algorithm is used for image reconstruction. The simulation experiments are carried out to evaluate the new type of sensor. The results show that, due to the internal electrode, it can improve the sensitivity in the central sensing area and thus is able to produce improved images. By using the new proposed sensor, it has a larger sensing area and can achieve similar image quality when compared to use the internal annular sensor.

Source localization is an important application of wireless sensor networks (WSNs). Many types of sensors can be used for source localization, e.g. range-only sensors, bearing-only sensors and time-of-arrival (TOA) sensors, etc. It is well known that the relative sensor-source geometry can significantly affect the performance of any particular localization algorithm. Existing works in the literature mainly deal with the geometry analysis for a single type of sensors. However, in real applications, different types of sensors may be utilized for source localization simultaneously. Hence, in this paper, we consider the optimal sensor placement problem in heterogeneous sensor networks, where two types of sensors are deployed for source localization. Relative optimal sensor-source configurations with the minimum number of sensors for source localization, are identified under the D-optimality criterion with potential extensions to a general case. Explicit characterizations of optimal sensor-source geometries are given for hybrid range-only and bearing-only sensors as well as hybrid bearing-only and TOA sensors, respectively.

Utilizing the data obtained from both scanning and counting sensors is critical for efficiently managing traffic flow on roadways. Past studies mainly focused on the optimal layout of one type of sensor, and how to optimize the arrangement of more than one type of sensor has not been fully researched. This paper develops a methodology that optimizes the deployment of different types of sensors to solve the well-recognized network sensors location problem (NSLP). To answer the questions of how many, where and what types of sensors should be deployed on each particular link of the network, a novel bi-level programming model for full route observability is presented to strategically locate scanning and counting sensors in a network. The methodology works in two steps. First, a mathematical program is formulated to determine the minimum number of scanning sensors. To solve this program, a new ‘differentiating matrix’ is introduced and the corresponding greedy algorithm of ‘differentiating first’ is put forward. In the second step, a scanning map and an incidence matrix are incorporated into the program, which extends the theoretical model for multiple sensors’ deployment and provides the replacement method to reduce total cost of sensors without loss of observability. The algorithm developed at the second step involved in two coefficient matrixes from scanning map and incidence parameter enumerate all possibilities of replacement schemes so that cost of different combination schemes can be compared. Finally, the proposed approach is demonstrated by comparison of Nguyen-Dupuis network and real network, which indicates the proposed method is capable to evaluate the trade-off between cost and all routes observability.

Wireless sensor networks (WSNs) are one of the most able technologies in the structural health monitoring (SHM) field. Through intelligent, self-organising means, the contents of this paper will test a variety of different objects and different working principles of sensor nodes connected into a network and integrated with data processing functions. In this paper the key issues of WSN applied in SHM are discussed, including the integration of different types of sensors with different operational modalities, sampling frequencies, issues of transmission bandwidth, real-time ability, and wireless transmitter frequency. Furthermore, the topology, data fusion, integration, energy saving, and self-powering nature of different systems will be investigated. In the FP7 project “Health Monitoring of Offshore Wind Farms,” the above issues are explored.

Supporting a helicopter pilot during landing and takeoff in degraded visual environment (DVE) is one of the challenges within DLR's project ALLFlight (Assisted Low Level Flight and Landing on Unprepared Landing Sites). Different types of sensors (TV, Infrared, mmW radar and laser radar) are mounted onto DLR’s research helicopter FHS (flying helicopter simulator) for gathering different sensor data of the surrounding world. A high performance computer cluster architecture acquires and fuses all the information to get one single comprehensive description of the outside situation. While both TV and IR cameras deliver images with frame rates of 25 Hz or 30 Hz, Ladar and mmW radar provide georeferenced sensor data with only 2 Hz or even less. Therefore, it takes several seconds to detect or even track potential moving obstacle candidates in mmW or Ladar sequences. Especially if the helicopter is flying with higher speed, it is very important to minimize the detection time of obstacles in order to initiate a re-planning of the helicopter’s mission timely. Applying feature extraction algorithms on IR images in combination with data fusion algorithms of extracted features and Ladar data can decrease the detection time appreciably. Based on real data from flight tests, the paper describes applied feature extraction methods for moving object detection, as well as data fusion techniques for combining features from TV/IR and Ladar data.