Event Of Sensor Failure Research Articles

Energy-efficient and Fault-Tolerant Routing Mechanism for WSN using Optimizer based Deep Learning Model

Fault tolerance is the network's capacity to continue operating normally in the event of sensor failure. Sensor nodes in wireless sensor networks (WSNs) may fail due to various reasons, such as energy depletion or environmental damage. Sensor battery drain is the leading cause of failure in WSNs, making energy-saving crucial to extending sensor lifespan. Fault-tolerant protocols use fault recovery methods to ensure network reliability and resilience. Many issues can affect a network, such as communication module breakdown, battery drain, or changes in network architecture. Our proposed FT-RR protocol is a WSN routing protocol that is both reliable and fault-tolerant; it attempts to prevent errors by anticipating them. FT-RR uses Bernoulli's rule to find trustworthy nodes and then uses those pathways to route data to the base station as efficiently as possible. When CHs have greater energy, they construct these pathways. Based on the simulation findings, our approach outperforms the other protocols concerning the rate of loss of packet, end-to-end latency, and network lifespan.

Replacement of Fault Sensor of Cutter Suction Dredger Mud Pump Based on MCNN Transformer

The mud pump water sealing system (MPWSS) is important in the efficient operation and prolonged service life of the cutter suction dredger’s (CSD) mud pump. Considering that the underwater pump operates underwater and the shaft seal water pressure sensor is prone to failure, a hybrid deep learning model MCNN transformer is proposed to predict the underwater pump shaft seal water pressure in the event of sensor failure. This paper uses big data from the dredging project to deeply excavate the relationship between the shaft end sealing water pressure and other construction data by combining experience and artificial intelligence, and then uses multi-scale convolutional neural network (MCNN) to reconstruct the data, highlighting the time series characteristics of the multi-scale data were then input into the transformer model for prediction, and compared with a single MCNN, transformer model and four other neural networks. Finally, the cutter suction dredger “Hua An Long” was selected as an application research case; experimental comparisons were conducted on seven different models to verify the accuracy and applicability of the MCNN-transformer model.

Proposed Fault Detection Algorithm with Optimized Hybrid Speed Control

The Brushless DC (BLDC) motor is a common choice for industrial applications, particularly in the automotive sector, owing to its high efficiency and robust capabilities. To detect the position of the motor rotor, hall-effect sensors can be used, but these sensors may prevent the system from operating if they fail. Consequently, fault-tolerant control (FTC) has been proposed in several studies to ensure continuity of operation in the event of sensor failure. This paper proposes an innovative method of fault detection in the hall effect sensor for a BLDC motor using combinatorial functions. This paper proposes an innovative method of hall-effect sensor fault detection for a BLDC motor using combinatorial functions. For the speed control of the BLDC under study, a hybrid adaptive neuro-fuzzy inference control (ANFIS) is implemented. In addition, the FTC signal reconstruction technique adopted has been improved to achieve motor start-up despite a fault in one of the sensors, thanks to well-defined fault detection algorithms. Simulation results are presented for each sensor failure case to test the effectiveness of the method used.

A Deep Reinforcement Learning Strategy for Surrounding Vehicles-Based Lane-Keeping Control.

As autonomous vehicles (AVs) are advancing to higher levels of autonomy and performance, the associated technologies are becoming increasingly diverse. Lane-keeping systems (LKS), corresponding to a key functionality of AVs, considerably enhance driver convenience. With drivers increasingly relying on autonomous driving technologies, the importance of safety features, such as fail-safe mechanisms in the event of sensor failures, has gained prominence. Therefore, this paper proposes a reinforcement learning (RL) control method for lane-keeping, which uses surrounding object information derived through LiDAR sensors instead of camera sensors for LKS. This approach uses surrounding vehicle and object information as observations for the RL framework to maintain the vehicle's current lane. The learning environment is established by integrating simulation tools, such as IPG CarMaker, which incorporates vehicle dynamics, and MATLAB Simulink for data analysis and RL model creation. To further validate the applicability of the LiDAR sensor data in real-world settings, Gaussian noise is introduced in the virtual simulation environment to mimic sensor noise in actual operational conditions.

Застосування методу парирування відмов датчиків при управлінні роботою малорозмірного авіадвигуна

The subject of the research is the application of the method of parrying sensor failures - one of the modern progressive approaches to ensuring the smooth and efficient operation of complex automatic control systems. This article is devoted to the application of this method in the control of a small-sized aircraft engine, which is an actual solution for the development of the aviation industry. The purpose of this study is to choose the optimal method for parrying sensor failures of a small-sized aircraft engine by analyzing existing methods and choosing engine parameters that should be used for parrying. Tasks. Consider the main methods of countering sensor failure and justify the choice of the most effective one. To state the main principles of the method of replacing the parameter value at all stages of the development of the control unit for the operation of a small-sized aircraft engine and to carry out an analysis of the disadvantages and advantages of such an approach. Consider algorithms for identifying and countering sensor failures for the engine management system. Analyze the simulation of individual parameters of a small-sized aircraft engine and propose parameters that should be replaced in the event of sensor failure. Research methods. Comparative analysis of methods of parrying sensor failures and models for simulating the parameters of small-sized aircraft engines with adjacent ones. The results. The method for replacing the parameter value with an adjacent one, switching to a backup value, redundancy, and the use of backup data sources are analyzed. Among the parrying methods, the two most optimal ones are selected (the method of replacing the value of the failed sensor parameter with an adjacent parameter and the method of switching to a backup value) and their work algorithms are described. The advantages are increased reliability and efficiency of engine operation, prevention of information loss, adaptability, and improved diagnostics. Among the shortcomings, the existence of risks of erroneous measurements, complex settings, accuracy limitations, dependence on other parameters, cost and complexity of the system are highlighted. Using the example of the ECU-35 unit developed by JSC "Element", the model of replacing the engine rotor speed parameter using the current temperature of the gases behind the turbine, taking into account the calibration of the measurement channel, was described. Conclusions (scientific and practical novelty). It was found that the application of the method of identification and parrying of sensor failures ensured the smooth operation of the control system when individual sensors failed. Thanks to the method for parrying sensor failures, it was possible to achieve a significant improvement in the performance of a small-sized aircraft engine.

Integrating BIM into sensor-based facilities management operations

Integrating BIM into sensor-based facilities management operations

Cooperative obstacle avoidance for heterogeneous unmanned systems during search mission

The problem of cooperative obstacle avoidance by a group of unmanned ground vehicles (UGVs) and unmanned air vehicles (UAVs) during a typical search mission is addressed in this paper. The group of UAVs and UGVs are performing a search operation in the designated area. All the UAVs and UGVs are equipped with a vision sensor/LIDAR to identify the possible obstacles in the search space. Due to their operation on the ground, UGVs are more likely to encounter obstacles. The obstacle avoidance for UGV under the event of sensor failure is done with environment information from the nearest UAV. The UAV plans its trajectory according to the UGV’s expected future trajectory, leading towards the base station. The UGV replans its trajectory to avoid obstacles after obtaining the information from its nearest UAV. A simulation study is performed with 10 UAVs and five UGVs performing a search mission in 1[Formula: see text]km[Formula: see text][Formula: see text][Formula: see text]1[Formula: see text]km area. The proposed obstacle avoidance method is experimentally validated in the outdoor environment with an autonomous UAV equipped with a camera and an autonomous UGV navigating based on GPS localization and environment information from the UAV.

Sensor Failure Detection and Faulty Data Accommodation Approach for Instrumented Wastewater Infrastructures

In wastewater industry, real-time sensing of surface temperature variations on concrete sewer pipes is paramount in assessing the rate of microbial-induced corrosion. However, the sensing systems are prone to failures due to the aggressively corrosive environmental conditions inside sewer assets. Therefore, reliable sensing in such infrastructures is vital for water utilities to enact efficient wastewater management. In this context, this paper presents a sensor failure detection and faulty data accommodation (SFDFDA) approach that aids to digitally monitor the health conditions of the sewer monitoring sensors. The SFDFDA approach embraces seasonal autoregressive integrated moving average model with a statistical hypothesis testing technique for enabling temporal forecasting of sensor variable. Then, it identifies and isolates anomalies in a continuous stream of sensor data whilst detecting early sensor failure. Finally, the SFDFDA approach provides reliable estimates of sensor data in the event of sensor failure or during the scheduled maintenance period of sewer monitoring systems. The SFDFDA approach was evaluated by using the surface temperature data sourced from the instrumented wastewater infrastructure and the results have demonstrated the effectiveness of the SFDFDA approach and its applicability to surface temperature monitoring sensor suites.

Sensor Fault-Resilient Control of Interior Permanent-Magnet Synchronous Motor Drives

In a conventional ac motor drive using field-oriented control, a dc-link voltage, speed, and at least two current sensors are required. Hence, in the event of sensor failure, the performance of the drive system can be severely compromised. This paper presents a sensor fault-tolerant control strategy for interior permanent-magnet synchronous motor (IPMSM) drives. Three independent observers are proposed to estimate the speed, dc-link voltage, and currents of the machine. If a sensor fault is detected, the drive system isolates the faulty sensor while retaining the remaining functional ones. The signal is then acquired from the corresponding observer in order to maintain the operation of the drive system. The experimental results provided verify the effectiveness of the proposed approach.

Sensorless Control Method in IPMSM Position Sensor Fault for HEV

The widely used motors in HEV(Hybrid Electric Vehicles) are IPMSM(Interior Permanent Magnet Synchronous Motor) which has no rotor heat, higher efficiency and advantageous in volume and weight comparing with other motors. For vector control of IPMSM, position information of rotor is required but Resolver is mainly used as the detecting sensor. However, the use of position sensors will reduce the system reliability of hybrid electric vehicles. In this paper, a way to control the motor by sensorless was proposed at the event of sensor failure. We also implemented IPMSM sensorless operation by the expanded EMF(Electro Motive Force) voltage way and harmonic voltage which is applying in the low speed area. And we proposed how to change with sensorless control by detecting the position sensors failure and verified it through experiments.

Unknown Input Observer with Fuzzy Fault Tolerant Control for Wind Energy System

This paper proposes a Fuzzy Dedicated Observers (FDOS) method using a Nonlinear Unknown Input Fuzzy Observer (UIFO) with a Fuzzy Fault Tolerant Control (FFTC) algorithm for fuzzy Takagi-Sugeno (TS) systems subject to sensor faults. FDOS provide residuals for detection and isolation of sensor faults which can affect a TS model. Parallel Distributed Compensation (PDC) is employed to design the fuzzy Fault Tolerant Controller from a TS fuzzy model. Sufficient stabilization conditions of the FFTC systems are given, which are formulated in terms of Linear Matrix Inequalities (LMIs). The LMIs can be efficiently solved using convex optimization techniques. Based on the derivation and simulation of the proposed FFTC approach, the stability of the closed-loop system in the event of sensor failures and unknown inputs is guaranteed. A wind Energy System (WES) with a Doubly-Fed Induction Generator (DFIG) example is illustrated to show the effectiveness of the proposed design method.

A Fault-Tolerant Control Architecture for Induction Motor Drives in Automotive Applications

This paper describes a fault-tolerant control system for a high-performance induction motor drive that propels an electrical vehicle (EV) or hybrid electric vehicle (HEV). In the proposed control scheme, the developed system takes into account the controller transition smoothness in the event of sensor failure. Moreover, due to the EV or HEV requirements for sensorless operations, a practical sensorless control scheme is developed and used within the proposed fault-tolerant control system. This requires the presence of an adaptive flux observer. The speed estimator is based on the approximation of the magnetic characteristic slope of the induction motor to the mutual inductance value. Simulation results, in terms of speed and torque responses, show the effectiveness of the proposed approach.

Robust H-infinity fault-tolerant control for uncertain descriptor systems by dynamical compensators

The problem of robust H-infinity fault-tolerant control against sensor failures for a class of uncertain descriptor systems via dynamical compensators is considered. Based on H-infinity theory in descriptor systems, a sufficient condition for the existence of dynamical compensators with H-infinity fault-tolerant function is derived and expressions for the gain matrices in the compensators are presented. The dynamical compensator guarantees that the resultant colsed-loop system is admissible; furthermore, it maintains certain H-infinity norm performance in the normal condition as well as in the event of sensor failures and parameter uncertainties. A numerical example shows the effect of the proposed method.

Robust reliable H ∞ controllers design via LMIS

The normal H ∞ control design deals with both plant modeling uncertainties and exogenous signal uncertainties by constructing a controller which stabilizes uncertain linear systems while satisfying an H ∞ -norm bound constraint on disturbance attenuation for all admissible uncertainties. However, the control design may result in unsatisfactory performances or even instabilities in the event of sensor failures in practical plants. This paper presents a novel technique to design robust reliable H ∞ controllers for a class of time varying uncertainty system with sensor failures by solving three linear matrix inequalities (LMIs).