A Location Problem in Sensor Systems Considering System Reliability

Development and testing of a performance evaluation methodology to assess the reliability of occupancy sensor systems in residential buildings

The free market forces infrastructure operators to increase their operational efficiency. To achieve this, among other things personnel costs have to be reduced. This implicates the reduction of the number of train station inspectors. Since they were also responsible for checking the passing trains for fault states, the higher distances of unobserved movement of trains result in higher risks of heavy accidents. On the other hand, there were numerous developments of new sensor-systems for checking the mentioned train fault states. Unfortunately, all of these systems focus only on singular or at least on several particular deviations from normal and are completely stand-alone solutions. At higher passing speeds the accuracy and the reliability of these systems varies in a wide range. This is an important reason why today's fault-checking sensor systems are only used for alarming manned train guidance stations. Taking these trends into account, the demand for an overall train inspection system is obvious and resulted in a research project called "Checkpoint", which is funded by the Austrian government. Our approach is based on the acquisition of all relevant train properties to get knowledge of the complete train condition for better failure estimation. Thus, a concept for efficient integration of commercially available sensor systems and for universal data treatment was developed. It enables easy configuration of the data analysis and definition of fail-states by a rule based mechanism. The main technical innovation of the system comes from the direct connection to the interlocking system via the control system, which offers the ability to intervene automatically in case of a detected failure (e.g. stopping the train). To determine the reliability of new sensor systems under realistic conditions a prototype of a Checkpoint was built up and the most relevant sensor systems (hot box detector, flat wheel detector, dynamic scale and loading gauge detector) were integrated. Extensive testing would be performed to prove the functionality of the system.

Nowadays, inexpensive smart devices with multiple heterogeneous on-board sensors, networked through wired or wireless links and deployable in large numbers, are distributed throughout a physical process or in a physical environment, providing real-time, and dense spatio-temporal measurements and enabling surveillance and monitoring capability that could not be imagined a decade ago. Such a system-wide deployment of sensing devices is known as distributed sensing, and is considered one of the top ten emerging technologies that will change the world. Oil and gas pipeline systems, electrical grid systems, transportation systems, environmental and ecological monitoring systems, security systems, and advanced manufacturing systems are just a few examples among many others. Malfunction of any of the large-scale systems typically results in enormous economic loss and sometimes even endangers critical infrastructure and human lives. In any of these systems, the system state variables, whose values trigger various actions, are estimated based on the measurements gathered by the sensor system that monitors and controls the system of interest. Consequently, the reliability of these estimations is of utmost importance in economic and safe operation of these large-scale systems. In a linear sensor system, the sensor measurements are combined linear responses of the system states that need to be estimated. In the engineering literature, a linear model is often used to establish connection between sensor measurements in a system and the system's state variables through the sensor system's design matrix. In such systems, the sensor outputs y and the system states x are linked by the set of linear equations represented as y = Ax+e, where y and e are n by 1 vectors, and x is a p by 1 vector. A is an n by p design matrix (n >> p) that models the linear measurement process. The matrix A is assumed to be of full column rank i.e., r(A) = p, where r(A) denotes the rank of A. The last term e is a random noise vector, which is assumed to be normally distributed with mean 0. In the context of estimation reliability, the redundancy degree in a sensor system is the minimum number of sensor failures (or measurement outliers) which can happen before the identifiability of any state is compromised. This number, called the degree of redundancy of the matrix A and denoted by d(A), is formally defined as d(A) = {d-1: there esists A[-d] s.t. r(A[-d]) < r(A)}, where A[ − d] is the reduced matrix after deleting some d rows from the original matrix. The degree of redundancy of linear sensor systems is a measure of robustness of the system against sensor failures and hence the reliability of a linear sensor system. Finding the degree of redundancy for structured linear systems is proven to be NP-hard. Bound and decompose, mixed integer programming, l1-minimization methods have all been studied and compared in the literature. But none of these methods are suitable for finding the degree of redundancy in large scale sensor systems. We propose a decomposition approach which effectively disintegrates the problem into a reasonable number of smaller subproblems utilizing the structure inherent in such linear systems using concepts of duality and connectivity from matroid theory. We propose two different but related algorithms, both of which solves the same redundancy degree problem. While the former algorithm applies the decomposition technique over the vector matroid (the design matrix), the latter uses its corresponding dual matroid. These subproblems are then solved using mixed integer programming to evaluate the degree of redundancy for the whole sensor system. We report substantial computational gains (up to 10 times) for both these algorithms as compared to even the best known existing algorithms.

In this article, we are the first to propose deep learning algorithms for intensity wavelength division multiplexing (IWDM)-based self-healing fiber Bragg grating (FBG) sensor network. A deep learning algorithm is proposed to improve the accuracy of measuring the sensing signal of the sensor system. Furthermore, to increase the total number of FBG sensors multiplexed in the sensor network for multipoint measurements, a multiplexing technique called IWDM is proposed. The proposed IWDM-based ring structure FBG sensor network can also have a self-healing purpose to improve the sensor system's reliability and survivability. However, IWDM has unmeasurable gap or crosstalk problems when the number of FBG sensors increases, which causes high sensing signal measurement errors. To solve this problem, a gated recurrent unit (GRU) deep learning algorithm is proposed and experimentally demonstrated. To prove the sensing signal measurement performance of our proposed algorithm, we test the well-trained GRU model using two cases. The first case is when the spectra of FBGs are overlapped as well as the minimum intensity difference between FBGs is 10%, and the second case is when the spectra of FBGs are overlapped as well as the minimum intensity difference between FBGs is 3% which is a very small intensity difference. From the experimental results, the well-trained GRU algorithm achieves high strain sensing signal measurement performance in both cases compared to other algorithms. Therefore, the proposed IWDM based FBG sensor system using deep learning algorithm enhances the multiplexing capacity and survivability of the sensor system, reduces the computational time, and improves strain sensing signal measurement accuracy of FBGs even when FBGs has very small intensity difference and overlap problem.

A linear sensor system is defined as a sensor system in which the sensor measurements have a linear relationship to source variables that cannot be directly measured. Evaluation of the reliability of a general linear sensor system is a #P problem whose computational time increases exponentially with the increment of the number of sensors. To overcome the computational complexity, Monte Carlo methods are developed in this paper to approximate the sensor system's reliability. The crude Monte Carlo method is not efficient when the sensor system is highly reliable. A Monte Carlo method that has been improved for network reliability, known as the Recursive Variance Reduction (RVR) method, is further adapted for the reliability problem of linear sensor systems. To apply the RVR method, new methods are proposed to obtain minimal cut sets of the linear sensor system, particularly under the conditions where the states of some sensors are fixed as failed or functional. A case study in a multistage automotive assembly process is conducted to demonstrate the efficiency of the proposed methods.

This work presents a novel star-ring-bus sensor system and demonstrates its effectiveness. The main trunk of the proposed sensor system is a star topology and the sensing branches comprise a series of bus subnets. Any weakness in the reliability of the sensor system is overcome by adding remote nodes and switches to the ring and bus subnets. To construct the proposed star-ring-bus sensor system, a fiber ring laser scheme is used to improve the signal-to-noise ratio of the sensor system. The proposed system increases the reliability and capacity of fiber sensor systems.

With the rapid development of IoT in recent years, Smart Home, one of the IoT application markets, has also been gaining popularity. The emergence of Smart Homes has brought convenience to people’s lives, especially for people who live alone with physical illness. Smart Home users normally have higher expectations for reliability and safety of sensor systems, particularly in light of how complicated and uncertain the living environment is. The present work attempts to propose a data-knowledge integrated solution to analyze, model and evaluate the reliability of sensor systems in a smart home by combining quantitative reliability analysis and probabilistic model checking. Probabilistic model checking techniques use logical reasoning to check quantitative properties (as system requirements) and provide mathematical guarantee for them. More specifically, Smart Home Sensor Systems (SHSS) is described as a Markov Chain, commonly used probabilistic model, which models the system behaviour (e.g., probabilistic choice of state transition), and SHSS reliability properties are defined by Probabilistic Computation Tree Logic (PCTL). These choices of model and specification formula allow us to use one of the most recently developed open source probabilistic model checkers, PRISM, to perform the model checking of reliability verification task in SHSS. A real world smart home dataset (Van Kasteren dataset) is employed along with PRISM to illustrate the modeling approach and demonstrate the feasibility and applicability of the proposed approach.

In the development of ocean energy technologies, full-scale sea trials have proven technically challenging and expensive. As a contribution to the development of flexible, reliable, and affordable measurement systems for such sea trials, this paper presents and evaluates a portable data acquisition system. The system offers a cheap and flexible option for when and where signal infrastructure at site is not available. It is battery powered and consists of a sensor system and a logger unit. The sensor system is placed inside the object of study, which for this application is a wave power generator standing on the seafloor. The logger unit, which contains a logger with integrated data storage and a battery, is placed outside the object of study in a submersible and retrievable vessel. Sensor output is carried with 4–20-mA current signals between the sensor system and the logger, which makes the system directly compatible with a wide range of industrial sensors. For the specific setup implemented in this paper, the sensor system was used to measure voltage and current inside the generator. Beyond this, the system is designed to be adaptable for a wider range of sensors, with 16 individual signals and a sampling frequency up to 1 kHz. The custom logger is programmable and offers many options to apply different logging schedules and to limit the sensor system power supply accordingly. Evaluations show that the implemented system offers 5–22 weeks of 1-kHz and 16-b monitoring of 16 signals, depending on a chosen logging schedule. Suggestions are also given for how to raise the operation time up to 50 weeks. The system has been tested offshore for the collection of power production data from two wave energy converters, with good results.

A vehicle-deployed parts-per-billion in volume (ppbv)-level off-axis integrated cavity output spectroscopic (OA-ICOS) CH4/C2H6 sensor system was experimentally presented for mobile inspection of natural gas leakage in urban areas. For the time-division-multiplexing-based dual-gas sensor system, an antivibration 35-cm-long optical cavity with an effective path length of ∼2510 m was fabricated with a high-stability temperature and pressure control design. An Allan deviation analysis yielded a minimum detection limit of 0.2 ppbv for CH4 detection and 10 ppbv for C2H6 detection for a 1 s averaging time. A natural gas leakage source location algorithm was proposed using an improved hybrid Nelder-Mead simplex search method and a particle swarm optimization (NM-PSO) algorithm. For field industrial application, the accuracy of the sensor system and leakage source location algorithm was confirmed through a CH4/C2H6 cylinder leakage experiment on the campus. Furthermore, through natural gas pipeline network inspection measurements in urban areas, three types of leakage sources, including natural gas, biogas, and possible leakage source were respectively located and confirmed using the global positioning system and wind speed and direction measurement system, verifying the reliability and potential application of the vehicle-deployed inspection system for future natural gas pipeline leakage monitoring.

Radio frequency identification (RFID)-based sensor systems are emerging as a new generation of wireless sensor networks by inherently integrating identification, sensing, communications and computation capabilities. Security and privacy are critical issues in dealing with a large amount of sensed data. In the study, the authors propose a distributed key array authentication protocol (KAAP) that provides classified security protection. KAAP is synthetically analysed in three aspects: logic, security and performance. The logic analysis includes messages formalisation, initial assumptions and anticipant goals based on GNY Logic formal method to verify the design correctness of the protocol. The security analysis with respect to confidentiality, integrity, authentication, anonymity and availability is performed via the simulated attacks, which involves supposing the attacker's identity, simulating the attacker's authentication process and creating compromised conditions. Such analysis ensures that the protocol has an ability to resist both external attacks (spoofing, replay, tracking and Denial of Service) and internal forgery attacks. Additionally, the performance is evaluated and compared with other related protocols to show that KAAP can improve the reliability and efficiency of sensor systems with insignificantly increased complexity. The result indicates that the protocol is reliable and scalable in advanced RFID-based sensor systems.

This investigation proposes a fiber Bragg grating (FBG) sensor system with a two-level ring architecture. The survivability and capacity of a FBG for a multipoint sensor system are enhanced by adding remote nodes and optical switches in the two-level ring architecture. Additionally, to enhance the signal-to-noise ratio (SNR) of the sensor system, a fiber ring laser approach is utilized to construct the proposed two-level ring architecture. The fiber ring laser adopted herein yields the high SNR of the sensor system. The proposed system can increase the reliability of FBG sensor systems for multipoint smart structures.

Prognostics and health management (PHM) is an enabling discipline consisting of technologies and methods to assess the reliability of a product in its actual life cycle conditions to determine the advent of failure and mitigate system risk. Sensor systems are needed for PHM to monitor environmental, operational, and performance-related characteristics. The gathered data can be analyzed to assess product health and predict remaining life. In this paper, the considerations for sensor system selection for PHM applications, including the parameters to be measured, the performance needs, the electrical and physical attributes, reliability, and cost of the sensor system, are discussed. The state-of-the-art sensor systems for PHM and the emerging trends in technologies of sensor systems for PHM are presented.

Laboratory testing methods to evaluate the reliability of occupancy sensors for commercial building applications

The combined facility location and network design problem is an important practical problem for locating public and private facilities. Moreover, incorporating aspects of reliability into the modeling of facility location problems is an effective way to hedge against disruptions in the system. In this paper, we consider a combined facility location/network design problem that considers system reliability. This problem has a number of applications, many of which fall into the category of service systems, such as regional planning and locating schools, health care service centers and airline networks. Our model also includes an investment budget constraint. We propose a mixed integer programming formulation to model this problem, as well as an efficient heuristic based on the problem’s LP relaxation. Numerical results demonstrate that the proposed heuristic significantly outperforms CPLEX in terms of solution speed, while still maintaining excellent solution quality. Our results also suggest a favorable tradeoff between the “nominal cost” (including fixed facility location costs and link construction costs, as well as transportation costs) and system reliability; that is, substantial improvements in reliability are often possible with only slight increases in the total cost of investment and transportation.

Oceanography was started by Navy engineers and the references of readiness and functional specifications were military. Since the end of the 80s, a new generation of instruments was able to promote more cost-efficient technical solutions. They cover now the needs of scientific ocean research as well as operational oceanography and environmental monitoring or assessment of the coastal areas. The ambition in the EC FP7 NeXOS project is to proceed in this direction in order to improve the temporal and spatial coverage, resolution and quality of marine observations. The Technology Readiness Levels are now successfully used for oceanographic equipments. NeXOS promotes a specific approach for the sensors themselves and for sensor systems. It happens to be very useful to detect weak points both at the beginning of the development and at high level of maturity. Some criteria are more often weak in the ocean sensor development world such as: follow-up of cost drivers at an early stage of the design, exact scope of the market, safety, dependence on few component providers, etc. The practice of functional analysis of sensor systems shows also a need to focus on specific aspects. Marine environment constraints are known to be critical. The designer has to take into account surrounding functions dealing with data availability, interoperability, modularity, robustness which are in fact major objectives of the NeXOS project. Reliability analysis in the context of marine sensor systems is in many cases a key issue. Some sensors will be deployed for long term autonomous missions, some of them, for instance on-board Argo Floats, will never be recovered. It then needs to be very performed with the rather small amount of failure rated available. The fear events are not only coming from the operations at sea but also from several steps of the data dissemination process: metrology, associated metadata, processing, etc. In order to achieve this goal, is necessary to consider several alternative configurations of the system design in such a way that functional specifications remain unchanged but enhance dependability. This is framed in the so-called reliability allocation problems [1], usually addressed by firstly obtaining Fault Tree models of the system and then performing cost-constrained optimization of whole system reliability. The most common criteria used to overcome reliability issues consist in apply redundancy on critical components to provide backup in case of failure of some component, use diversity (i.e. components from different manufacturers) in redundant parts so as to avoid common cause failures and employ physical dispersion (i.e in a redundant configuration, locate components in different parts of the system).