Fractures and Flow Patterns Detection in Carbonate Reservoirs Using Intelligent Sensor Selection in a Deep Learning and Uncertainty Framework

4th Industrial Revolution (4IR) technologies have assumed critical importance in the oil and gas industry, enabling data analysis and automation at unprecedented levels. Formation evaluation and reservoir monitoring are crucial areas for optimizing reservoir production, maximizing sweep efficiency and characterizing the reservoirs. Automation, robotics and artificial intelligence (AI) have led to tremendous transformations in these areas, in particular in subsurface sensing. We present a novel 4IR inspired framework for the real-time sensor selection for subsurface pressure and temperature monitoring, as well as reservoir evaluation. The framework encompasses a deep learning technique for sensor data uncertainty estimation, which is then integrated into an integer programming framework for the optimal selection of sensors to monitor the reservoir formation. The results are rather promising, showing that a relatively small numbers of sensors can be utilized to properly monitor the fractured reservoir structure.

Fault-Diagnosis Sensor Selection for Fuel Cell Stack Systems Combining an Analytic Hierarchy Process with the Technique Order Performance Similarity Ideal Solution Method

Multi-Criteria Decision Making (MCDM) methods have rapidly developed and have been applied to many areas for decision making in engineering. Apart from that, the process to select fault-diagnosis sensor for Fuel Cell Stack system in various options is a multi-criteria decision-making (MCDM) issue. However, in light of the choosing of fault diagnosis sensors, there is no MCDM analysis, and Fuel Cell Stack companies also urgently need a solution. Therefore, in this paper, we will use MCDM methods to analysis the fault-diagnosis sensor selection problem for the first time. The main contribution of this paper is to proposed a fault-diagnosis sensor selection methodology, which combines the rank reversal resisted AHP and TOPSIS and supports Fuel Cell Stack companies to select the optimal fault-diagnosis sensors. Apart from that, through the analysis, among all sensor alternatives, the acquisition of the optimal solution can be regarded as solving the symmetric or asymmetric problem of the optimal solution, which just maps to the TOPSIS method. Therefore, after apply the proposed fault-diagnosis sensor selection methodology, the Fuel Cell Stack system fault-diagnosis process will be more efficient, economical, and safe.

Greedy sensor selection based on QR factorization

We address the problem of selecting a given number of sensor nodes in wireless sensor networks where noise-corrupted linear measurements are collected at the selected nodes to estimate the unknown parameter. Noting that this problem is combinatorial in nature and selection of sensor nodes from a large number of nodes would require unfeasible computational cost, we propose a greedy sensor selection method that seeks to choose one node at each iteration until the desired number of sensor nodes are selected. We first apply the QR factorization to make the mean squared error (MSE) of estimation a simplified metric which is iteratively minimized. We present a simple criterion which enables selection of the next sensor node minimizing the MSE at iterations. We discuss that a near-optimality of the proposed method is guaranteed by using the approximate supermodularity and also make a complexity analysis for the proposed algorithm in comparison with different greedy selection methods, showing a reasonable complexity of the proposed method. We finally run extensive experiments to investigate the estimation performance of the different selection methods in various situations and demonstrate that the proposed algorithm provides a good estimation accuracy with a competitive complexity when compared with the other novel greedy methods.

Sensor Selection and State Estimation for Unobservable and Non-Linear System Models

To comply with the increasing complexity of new mechatronic systems and stricter safety regulations, advanced estimation algorithms are currently undergoing a transformation towards higher model complexity. However, more complex models often face issues regarding the observability and computational effort needed. Moreover, sensor selection is often still conducted pragmatically based on experience and convenience, whereas a more cost-effective approach would be to evaluate the sensor performance based on its effective estimation performance. In this work, a novel estimation and sensor selection approach is presented that is able to stabilise the estimator Riccati equation for unobservable and non-linear system models. This is possible when estimators only target some specific quantities of interest that do not necessarily depend on all system states. An Extended Kalman Filter-based estimation framework is proposed where the Riccati equation is projected onto an observable subspace based on a Singular Value Decomposition (SVD) of the Kalman observability matrix. Furthermore, a sensor selection methodology is proposed, which ranks the possible sensors according to their estimation performance, as evaluated by the error covariance of the quantities of interest. This allows evaluating the performance of a sensor set without the need for costly test campaigns. Finally, the proposed methods are evaluated on a numerical example, as well as an automotive experimental validation case.

Real-World Data-Driven Machine-Learning-Based Optimal Sensor Selection Approach for Equipment Fault Detection in a Thermal Power Plant

Due to growing electricity demand, developing an efficient fault-detection system in thermal power plants (TPPs) has become a demanding issue. The most probable reason for failure in TPPs is equipment (boiler and turbine) fault. Advance detection of equipment fault can help secure maintenance shutdowns and enhance the capacity utilization rates of the equipment. Recently, an intelligent fault diagnosis based on multivariate algorithms has been introduced in TPPs. In TPPs, a huge number of sensors are used for process maintenance. However, not all of these sensors are sensitive to fault detection. The previous studies just relied on the experts’ provided data for equipment fault detection in TPPs. However, the performance of multivariate algorithms for fault detection is heavily dependent on the number of input sensors. The redundant and irrelevant sensors may reduce the performance of these algorithms, thus creating a need to determine the optimal sensor arrangement for efficient fault detection in TPPs. Therefore, this study proposes a novel machine-learning-based optimal sensor selection approach to analyze the boiler and turbine faults. Finally, real-world power plant equipment fault scenarios (boiler water wall tube leakage and turbine electric motor failure) are employed to verify the performance of the proposed model. The computational results indicate that the proposed approach enhanced the computational efficiency of machine-learning models by reducing the number of sensors up to 44% in the water wall tube leakage case scenario and 55% in the turbine motor fault case scenario. Further, the machine-learning performance is improved up to 97.6% and 92.6% in the water wall tube leakage and turbine motor fault case scenarios, respectively.

Sensor Selection and Placement for Track Switch Condition Monitoring through Validated Structural Digital Twin Models of Train–Track Interactions

Railway track switches experience high failure rates, which can be reduced by monitoring their structural health. The results obtained from a validated Finite Element (FE) model for train–track switch interaction have been introduced to support sensor selection and placement. For the FE models with nominal and damaged rail profiles, virtual strain sensor measurements have been obtained after converting the true strains to engineering strains. Comparisons for the strains before and after the introduction of the fault have demonstrated greater amplitude for the strains after fault introduction. The highest difference in strain amplitude is in the vertical direction, followed by the longitudinal and lateral directions.

Human-posture-tracking sensor selection for motion recognition

Very recently special attention has been paid to soft sensors for motion tracking. It is known from the work by Chen et al [2021, Comp Anim Virtual Worlds. 2021; e1993.] that a wearable motion tracking system was developed, in which five sensors were placed around the region of arm and shoulder. In this study, we explore the effect of different sensors on motion recognition and select the sensors with excellent differentiation for different movements.