Redundant Configuration Research Articles

Collaborative fault detection and diagnosis method for dual single-axis RINSs configuration in GNSS-denied environment

To ensure the reliability and safety of the long-endurance navigation, two or more rotational inertial navigation systems (RINSs) are usually equipped on ships. Through the redundant configuration, fault detection and diagnosis (FDD) can be realized. In this paper, a collaborative FDD method applied to dual homogeneous single-axis RINSs is proposed. The method utilizes geometric constraint observation to construct a detection filter, processes the filtering outputs through the modified Bayesian algorithm, and incorporates a fault confirmation stage to achieve fault diagnosis with stronger robustness. Besides, an azimuth gyro drift variation estimation method using least-squares is further proposed by exploiting the difference in Schuler oscillation mode resulting from an azimuth gyro fault as opposed to the fault-free state when the system is damped by the log. The efficiency and reliability are demonstrated through comparative and repeated experiments. According to the results based on repeated experiments, the proposed method achieves a comprehensive correct diagnosis rate of over 99% across various fault scenarios. When relatively significant faults occur, the average diagnosis time for horizontal gyros, horizontal accelerometers, and azimuth gyros is 0.4569h, 0.2686h, and 1.2204h respectively. In the case of more subtle faults, although the average diagnosis time is extended to 0.5920h, 0.2991h, and 1.9641h, the effectiveness of the method is still well demonstrated.

Robust acceleration schedule design for gas turbine engine using multilayer perceptron network with adaptive sample class weighting

The acceleration schedule is crucial for generating acceleration control references for the gas turbine engine (GTE) and ensuring optimal acceleration performance. However, under harsh operating conditions, GTEs may encounter difficult-to-diagnose pressure sensor faults, which lead to inaccurate control references and decreased acceleration performance. This paper proposes a data-driven robust acceleration schedule (RAS) design method to tackle this issue, including fault data augmentation and adaptive sample class weighting (ASCW). Fault data augmentation generates multiple pressure fault sample classes from a normal acceleration schedule dataset. Due to the redundant pressure sensor configuration, the RAS can reconstruct these classes and generate accurate control references when a single pressure sensor fails. The ASCW employs a multilayer perceptron network to reconstruct the normal and fault sample classes accurately. Proportional integral regulation adjusts their weights during training to ensure balanced reconstruction precision. Simulation cases were conducted to verify the effectiveness of the RAS under actual GTE conditions, including engine-model mismatches, performance deterioration, flight envelope, and measurement uncertainty. The results demonstrate that the RAS ensures superior acceleration performance of GTEs across the full flight envelope in both normal and single pressure sensor fault scenarios. Additionally, the ASCW achieves reconstruction precision of 0.068 %, 0.080 %, 0.080 %, 0.082 %, and 0.077 % for normal and fault sample classes, respectively, prioritizing the precision of the normal sample class and balancing the precision of fault sample classes.

A Low-Cost Redundant Attitude System for Small Satellites, Based on Strap-Down Inertial Techniques and Gyro Sensors Linear Clustering

The significant technological changes related to the manufacturing of the miniaturized sensors produced a higher impact at the level of the detection units equipping the strap-down inertial navigation systems (INSs). Together with miniaturization, many more advantages are brought by these technologies, related to low costs, low necessary energy, high robustness and high potential for adapting the design solutions. However, reducing the dimensions and weight of the sensors is reflected by a decrease in their performance in terms of sensitivity, noise and the possibility of controlling sensitive elements. On the other hand, there is a permanent increase in the need to have in-space applications of miniaturized systems with a high degree of redundancy and to equip miniaturized satellites, miniaturized space robots or space rovers. The paper proposes a new methodology to increase the quality of the signals received from the miniaturized inertial measurement units (IMUs), but also to increase the degree of redundancy, by using low-cost sensors arranged in redundant linear configurations. The presentation is focused on the development of an attitude system based on strap-down inertial techniques which uses a redundant IMU equipped with three linear clusters of miniaturized gyros. For each of the three clusters, a data fusion mechanism based on the maximal ratio combining method is applied. This fusion mechanism reduces the noise power and bias of the signal delivered to the navigation processor. Shown are the theory, software modeling and experimentation results for the attitude algorithm, for the data fusion method, and for the integrated system.

Redundant Configuration Method of MEMS Sensors for Bottom Hole Assembly Attitude Measurement.

Micro-electro-mechanical systems inertial measurement units (MEMS-IMUs) are increasingly being employed for measuring the attitude of bottom hole assemblies (BHAs). However, the reliability and measurement precision of a single MEMS-IMU may not meet drilling's stringent needs. Redundant MEMS-IMU systems can effectively enhance the reliability and precision. This paper proposes a redundant configuration method for MEMS sensors tailored to BHA attitude measurement. Firstly, based on reliability theory and a cost-benefit analysis, considering factors such as cost, size, and reliability, the optimal number of sensors in the redundant system was determined to be six. Considering the structural characteristics of the BHA, a hollow hexagonal prism-shaped redundant configuration scheme was proposed, ensuring the circulation of drilling fluid within the drill pipe. Next, by employing Kalman filtering to integrate the output data from the six sensors, a virtual IMU (VIMU) was formed. Finally, experimental verification was carried out. The results confirmed that, after redundancy implementation, the velocity random walk of the accelerometer decreased by an average of 58% compared to a single MEMS-IMU, and bias instability was reduced by an average of 54%. The angular random walk of the gyroscope decreased by an average of 58%, and bias instability was reduced by an average of 37%. This research provides a theoretical foundation for enhancing the precision and reliability of BHA attitude measurements.

Fault-Tolerant Control of Autonomous Underwater Vehicle Actuators Based on Takagi and Sugeno Fuzzy and Pseudo-Inverse Quadratic Programming under Constraints.

Autonomous Underwater Vehicles (AUVs) play a significant role in ocean-related research fields as tools for human exploration and the development of marine resources. However, the uncertainty of the underwater environment and the complexity of underwater motion pose significant challenges to the fault-tolerant control of AUV actuators. This paper presents a fault-tolerant control strategy for AUV actuators based onTakagi and Sugeno (T-S) fuzzy logic and pseudo-inverse quadratic programming under control constraints, aimed at addressing potential actuator faults. Firstly, considering the steady-state performance and dynamic performance of the control system, a T-S fuzzy controller is designed. Next, based on the redundant configuration of the actuators, the propulsion system is normalized, and the fault-tolerant control of AUV actuators is achieved using the pseudo-inverse method under thrust allocation. When control is constrained, a quadratic programming approach is used to compensate for the input control quantity. Finally, the effectiveness of the fuzzy control and fault-tolerant control allocation methods studied in this paper is validated through mathematical simulation. The experimental results indicate that in various fault scenarios, the pseudo-inverse combined with a nonlinear quadratic programming algorithm can compensate for the missing control inputs due to control constraints, ensuring the normal thrust of AUV actuators and achieving the expected fault-tolerant effect.

A forward design approach for fault thresholds in redundant flight control systems

Modern fly-by-wire flight control systems use redundancy configurations to meet high safety requirements. The fault threshold value of the redundancy monitoring and voting algorithm affects the false alarm rate and missing alarm rate of the flight control system and the excessive false alarm rate and missing alarm rate reduce the reliability of the flight control system. Traditionally, the design of fault threshold is based on the empirical trial-and-error method, which suffers from the problem of unclear optimization direction. Forward design is needed to improve design efficiency and quality. In this paper, a forward design method for fault detection threshold is proposed to determine the upper limit value of the fault threshold by analyzing the effect of the voting signal change on the transient response of the aircraft when a channel fault occurs. The lower limit value of the fault threshold is determined by analyzing the channel history data. The effectiveness of the design method is demonstrated by digital simulation.

Level Process Station Control Using ALE111 Communication Module

A noteworthy example of the technological evolution is the ALE111, a cutting-edge system equipped with advanced functionalities designed to monitor the status of field wireless devices. One of its key features is the implementation of bumpless output, a capability that ensures seamless transitions and adjustments in the control process. Remarkably, the ALE111 enables wireless process control, a capability traditionally associated with wired control systems utilizing 4-20 mA signal communication. This wireless paradigm not only introduces a new level of flexibility but also contributes to the overall enhancement of process control reliability. The ALE111 goes a step further by incorporating redundant configurations, a strategic approach that adds an extra layer of reliability to the system. By doing so, potential points of failure are mitigated, and the overall robustness of the process control system is substantially improved. Key Word: ALE111 Communication Module, Ethernet, Level Process Station, Vnet, Field Control Station ,HIS, Data Acquisition Card

Redundant Configuration and Fault Tolerance Control Method for Enhanced Reliability of Energy Storage System

Redundant Configuration and Fault Tolerance Control Method for Enhanced Reliability of Energy Storage System

EPR核电厂主给水调节阀FUM280 增加冗余配置方案研究

EPR核电厂主给水调节阀FUM280 增加冗余配置方案研究

Humanoid Motion Control using Auto Resonance Neural Network

It has been proven difficult to control robots with many mechanical joints due to a variety of problems, including redundant configurations, non-linear displacement, dynamic user surroundings, etc. Iterative computations have been applied to address inverse kinematic problems of industrial robots with up to six Degrees of Freedom (DoF). With one to three degrees of freedom in each of more than hundred joints used by humans for locomotion, the complexity is unfathomable. Consequently, in humanoid structures, algorithmic and heuristic approaches have failed. Numerous research fields that were earlier thought to be challenging to solve with computers have seen interest piqued by recent advancements in artificial intelligence and machine learning. One such domain is humanoid motion. This paper presents a novel kind of Artificial Neural Network named as Auto Resonance Neural Network (ARN). ARN uses the pull-relax mechanism applied by biological systems to control musculoskeletal motion. A variety of functions can be employed to build the pull-relax model, contingent on variables such as range, necessary coverage, and tunability. When employing ARN for joint control, inverse kinematics or some other kind of repetitive solution is not required. Its application is not influenced by the DoF or joint count. The network can use the learning methods like reinforcement learning or supervised or unsupervised learning.

A First-Out Alarm Detection Method via Association Rule Mining and Correlation Analysis.

Alarm systems are commonly deployed in complex industries to monitor the operation status of the production process in real time. Actual alarm systems generally have alarm overloading problems. One of the major factors leading to excessive alarms is the presence of many correlated or redundant alarms. Analyzing alarm correlations will not only be beneficial to the detection of and reduction in redundant alarm configurations, but also help to track the propagation of abnormalities among alarm variables. As a special problem in correlated alarm detection, the research on first-out alarm detection is very scarce. A first-out alarm is known as the first alarm that occurs in a series of alarms. Detection of first-out alarms aims at identifying the first alarm occurrence from a large number of alarms, thus ignoring the subsequent correlated alarms to effectively reduce the number of alarms and prevent alarm overloading. Accordingly, this paper proposes a new first-out alarm detection method based on association rule mining and correlation analysis. The contributions lie in the following aspects: (1) An association rule mining approach is presented to extract alarm association rules from historical sequences based on the FP-Growth algorithm and J-Measure; (2) a first-out alarm determination strategy is proposed to determine the first-out alarms and subsequent alarms through correlation analysis in the form of a hypothesis test on conditional probability; and (3) first-out rule screening criteria are proposed to judge whether the rules are redundant or not and then consolidated results of first-out rules are obtained. The effectiveness of the proposed method is tested based on the alarm data generated by a public simulation platform.

Design and Application of Station Power Supply System for Lithium Iron Phosphate Battery Based on Power Exchange Operation

Based on the engineering application design and development of the power supply system of lithium iron phosphate battery pack in the operation and maintenance mode, this paper conducts the application research from four aspects of battery quantity selection, capacity calculation selection, battery management system design, battery pack modular design, etc. The design scheme of the lithium iron phosphate power supply system is formulated, and the matching battery management system is designed. A universal lithium iron phosphate battery module with an “N+1” redundant configuration is developed to improve the maintenance efficiency of the battery pack. It reliably supports the substation power changing operation and maintenance mode and ensures the reliability and operation safety of the substation power supply system.

A Dynamic Bayesian Network model to evaluate the availability of machinery systems in Maritime Autonomous Surface Ships

With their complex structure, multiple failure modes and lack of maintenance crew, the safety problem of Maritime Autonomous Surface Ships’ (MASS) machinery systems are becoming an important research topic. The present study presents an availability model for ship machinery systems incorporating a maintenance strategy based on Dynamic Bayesian Networks (DBN). First, the availability of conventional ship machinery systems is evaluated and used as a benchmark based on the configuration and planned maintenance strategy. Secondly, the availability of MASS machinery systems is compared to the benchmark, before the introduction of any changes to the ship’s configuration and planned maintenance strategy. Finally, the availability improvement strategies, including redundant designs and planned maintenance strategies at port, are proposed based on sensitivity analysis and planned maintenance cost minimization. To exemplify the model's application, a case study of a cooling water system is explored. Based on a sensitivity analysis using the model, it is possible to decide which components need to be redundant. Different redundancy designs and corresponding planned maintenance strategies can be adopted to meet the availability demand. It is also shown that redundancy and enhanced detection capabilities reduce much of the planned maintenance cost. This framework can be used in the early design stages to determine whether the MASS machinery systems’ availability is at least equivalent to that of conventional ships, and has certain reference significance for redundant configuration designs and MASS planned maintenance strategy schedule.

Control Systems Synthesis of Maximum Robust Stability Degree Based on Vertex Critical Root Diagrams

The article solves the problem of operative selection of the redundant onboard equipment complex components configuration of the suitable in the current operating conditionаs in the interests of ensuring high fault tolerance of the complex, as well as achieving other operational and technical characteristics. The basis of the redundancy management system of the complex consists of configuration supervisors — as program subjects according to the number of its competitive configurations of heterogeneous and nonuniform equipment worked out in advance. The choice of the preferred configuration is proposed to be carried out by performing multi-level arbitration, which includes two phases of paired arbitration of computers and paired arbitration of configuration. It is proposed to include the means of both types of arbitration in each configuration supervisor, which ensures its self-sufficiency when participating in a competitive selection. The second part of the article is devoted to the computer’s arbitration for the implementation of redundancy management functions. The approach is applicable to a computing environment with many comparable computing devices and contains 2 phases. In the first phase, a preliminary selection of a competing pair of computers — as applicants for the implementation of redundancy management functions in them is carried out. In the break between the phases, the pair computers implement the procedures for pair arbitration of configurations given in the first part of the article. In the second phase, the final choice of the -computer is made, in which the supervisor who won the arbitration will be implemented. In order to achieve the maximum possible centralization of selection procedures and, as a consequence, the exclusion of " bottlenecks" in terms of reliability of places, additionally proposed: the organization of secure data exchange between computers based on distributed registry technology; the procedure of paired arbitration of computers, consisting in mutual cross-validation of dominant supervisors of a pre-allocated pair by comparing preference matrices, including information parcels of arbitration objects. A methodological example that demonstrates the features of the system functioning in the conditions of computers degradation is given. The proposed approach can be used to solve the problems of reconfiguration control of heterogeneous computing facilities of technical objects on-board equipment complexes.

Optimum number of cascade cells for multilevel inverter considering redundancy strategies based on cost and reliability optimization

AbstractThis paper proposes an economical reliability‐oriented solution to determine the optimal number of cascade cells and redundancies for STATCOM using a Non‐Dominated Sorting Genetic (NSGA‐II) algorithm based on different redundancy configuration schemes. A multi‐objective optimization framework is presented, to minimize the total cost and maximize its reliability index by considering power quality issues. In the presented model, a new cost modelling is introduced considering the cost of inductor loss. The multilevel cascade topology with staircase modulation strategy is also taken into account. The effectiveness of the proposed optimization framework is validated by the NSGA‐II algorithm compared to the results of goal attainment and goal programming as decomposition single‐objective optimization methods. The results verify that the proposed multi‐objective optimization framework could be useful in the optimal selection of high‐voltage STATCOM levels in the presence of redundancies while ensuring the required level of reliability at the minimum cost. Also, the presented NSGA‐II optimization algorithm is applicable to maintain both objective functions at an acceptable value.

Spot Market Cloud Orchestration Using Task-Based Redundancy and Dynamic Costing

Cloud computing has become ubiquitous in the enterprise environment as its on-demand model realizes technical and economic benefits for users. Cloud users demand a level of reliability, availability, and quality of service. Improvements to reliability generally come at the cost of additional replication. Existing approaches have focused on the replication of virtual environments as a method of improving the reliability of cloud services. As cloud systems move towards microservices-based architectures, a more granular approach to replication is now possible. In this paper, we propose a cloud orchestration approach that balances the potential cost of failure with the spot market running cost, optimizing the resource usage of the cloud system. We present the results of empirical testing we carried out using a simulator to compare the outcome of our proposed approach to a control algorithm based on a static reliability requirement. Our empirical testing showed an improvement of between 37% and 72% in total cost over the control, depending on the specific characteristics of the cloud models tested. We thus propose that in clouds where the cost of failure can be reasonably approximated, our approach may be used to optimize the cloud redundancy configuration to achieve a lower total cost.

EFFECT OF VOLTAGE STRESS ON RELIABILITY OF THE 3-LEVEL ANPC MULTILEVEL INVERTER FOR WIND TURBINES

Wind power with low or no greenhouse gas emissions has been highly prevalent over the last decade. Modern renewable energy systems rely heavily on power electronic devices such as multilevel converters (MLC) to integrate renewables into the grid or provide electricity to islanding loads. These converters’ power electronic switches have a high failure rate (approximately 34 percent). As a result, the reliability evaluation of these converters is vital. Most research has focused on developing a fault-tolerant, efficient and cost-effective topology that reduces components. Still, the reliability of these topologies has received relatively little attention. This paper studies the effect of voltage stress on three-level Active Neutral Point Clamped (ANPC) multilevel inverter reliability. The series redundancy is introduced in ANPC using redundant outer switches, making ANPC a fault-tolerant topology. The reliability of this fault-tolerant topology is compared with the fault-intolerant ANPC. The voltage stress factor is calculated for fault intolerant and proposed fault-tolerant ANPC topologies. Because of the reduced stress on the switches and redundant configuration of the outer switches, the proposed fault-tolerant ANPC is more reliable. The fault-tolerant topology proposed in this paper has the lowest voltage stress factor, resulting in better reliability.

Software Defined Networking Architecture for Energy Transaction in Smart Microgrid Systems

A decentralized power distribution network consisting of smart microgrids introduces opportunities to trade with energy called transactive energy. However, research studies in the existing literature suggest that several standardized information models for TE do not meet the network architecture’s reliability, flexibility, and security requirements. This limitation is mainly due to the static nature of traditional IP infrastructure. To achieve these requirements in the network architecture, this study investigates the optimized application of software-defined network architecture for transactive energy in smart microgrid systems. Through literature research, unique design approaches in an SDN architecture are identified that improve the reliability, flexibility, and security of the SDN architecture. These design approaches include a decentralized controller network layout, redundant link configuration, a mesh network topology, and data encryption. The proposed solution uniquely combines these design approaches into a single optimized SDN solution for TESMS. To validate the improvements of the findings from the literature research, each design approach is simulated in this study using Mininet SDN emulator and AnyLogic system simulation software. The proposed solution is then applied to a use-case scenario that shows the improvements required for TESMS. The use-case scenario shows significant improvement in the data path uptime. An improvement of 0.27% is achieved, which equates to a 2 h per month increase in the data path uptime. The results of the simulation show that the proposed SDN architecture improves the reliability and flexibility of a traditional SDN network. Furthermore, enabling encryption between the nodes improves the security of the SDN architecture.

Wavelet Denoising Applied to Hardware Redundant Systems for Rolling Element Bearing Fault Detection

This work presents a novel wavelet-based denoising technique for improving the signal-to-noise ratio (SNR) of non-steady vibration signals in hardware redundant systems. The proposed method utilizes the relationship between redundant hardware components to effectively separate fault-related components from the vibration signature, thus enhancing fault detection accuracy. The study evaluates the proposed technique on two mechanically identical subsystems that are simultaneously controlled under the same speed and load inputs, with and without the proposed denoising step. The results demonstrate an increase in detection accuracy when incorporating the proposed denoising method into a fault detection system designed for hardware redundant machinery. This work is original in its application of a new method for improving performance when using residual analysis for fault detection in hardware redundant machinery configurations. Moreover, the proposed methodology is applicable to non-stationary equipment that experiences changes in both speed and load.

On the suitability of redundant accelerometers for the implementation of smart oscillation monitoring system: Preliminary assessment

Structural health monitoring (SHM) is an essential aspect to ensure the safety and longevity of civil infrastructure. In recent years, there has been a growing interest in developing SHM systems based on Micro-Electro-Mechanical Systems (MEMS) technology. MEMS-based sensors are small, low-power, and cost-effective, making them ideal for large-scale deployment in structural monitoring systems. However, the use of MEMS-based sensors in SHM systems can be challenging due to their inherent errors, such as drift, noise, and bias instability; these errors can affect the accuracy and reliability of the measured data, leading to false alarms or missed detections. Therefore, several methods have been proposed to compensate for these errors and improve the performance of MEMS-based SHM systems. For this purpose, the authors propose the combined of a redundant configuration of cost-effective MEMS accelerometers and a Kalman Filter approach to compensate MEMS inertial sensor errors and data filtering; the performance of the method is preliminarily assessed by means of a custom-controlled oscillation generator and compared with that granted by a high-cost, high-performance MEMS reference system where amplitude differences of 0.02 m/s2 have been experienced. Finally, a sensor node for real-time monitoring has been proposed that exploits LoRaWAN and NFC protocols to access the structure information to be monitored.