Reliability Control Research Articles

Uncertainty-oriented reliability and risk-based output control for complex systems with compatibility considerations

This study addresses two challenges, namely, the generally existent uncertainty that has been overlooked by many current approaches, and compatibility with multiple surrogate models in the process of output control instead of relying on one specific model. In the proposed approach, reliability and risk are used to better represent and model the generally existent uncertainty. Furthermore, the proposed approach has a higher level of compatibility, which is independent of a specific surrogate model. These two aspects are the major theoretical contributions of the proposed approach. A practical case study of reducing building tilt rate (BTR) in tunnel engineering shows that the BTR can be reduced from the original [0.8516, 2.7573] to [0.4904, 1.0664] by 41.65%-73.17% with an average reduction of 56.64%. When uncertainty was not considered, the average reduction of the BTR was 50.36%, while overestimation and underestimation were observed in most sets of data. Three surrogate models were cross-checked to validate the compatibility of the proposed approach, namely the Gaussian process regression (GPR), back-propagation neural network (BPNN), and belief rule base (BRB). The results of cross-checking show that: (1) GPR and BPNN are mutually recognizable, that is, the mutual MAPE is less than 1%, (2) GPR has recommended the most robust parameter settings, and (3) BRB has underestimated the BTRs. Owing to the ability to represent and model uncertainty and the high compatibility of the surrogate models, the proposed approach can be extended to more engineering problems in various fields.

Artificial Intelligence Technology Based on Deep Learning in Building Construction Management System Modeling

In order to explore the application of AI technology in construction management system modeling, the author proposed the application of a deep learning-based AI technology in construction management system modeling. The 3 D reconstruction deep learning model is first introduced, and then the model idea of the construction progress reliability control system is designed based on BIM (building information model). Second, the construction process of the 4dbim model is described, and the construction method is introduced. The construction of the model provides data information for the construction schedule reliability control system. Finally, the three functional modules of progress monitoring, progress reliability early warning, and progress prediction are realized by combining the S-curve comparison method, and the work of the system is described through case simulation. The early warning result is from June 7 to June 11, the progress deviation is between (−2%, 2%), and the progress is basically controlled. On June 13, the planned workload was 81.099%, and the actual cumulative workload was 7.099%, which was 4% less than the planned workload. The project progress was out of date, so it needs to be closely tracked. On June 15, the planned workload was 85.511%, and the actual cumulative workload was 80.899%, 4.5% less than the planned workload. The forecast result is the line forecast of the actual cumulative completion percentage on June 17. After calculation, the forecast result on June 17 is 84.311%. The progress deviation on June 17 was 5.21%. If no timely delay is taken on June 15, the delay will get worse. In addition, the system can predict the completion period of the project. When the actual percentage of cumulative completion is greater than or equal to 100%, it indicates that the project has been completed. Therefore, we can calculate the completion period of the three-storey project, and the construction period is about June 29 or 30. When the simulation can be carried out, the simulation number is set as 1000 times, the completion probability of the project is only 40%, and the completion probability is not too high. Artificial intelligence technology can realize progress monitoring, progress reliability early warning, and progress prediction. This system model prepares for software development and is conducive to improving the progress reliability control level of construction enterprises. Starting from the schedule planning subsystem and the schedule control subsystem, this paper studies the application of the artificial intelligence technology based on deep learning in the modeling of a building construction management system. The results show that this technology can effectively improve the efficiency of the building construction schedule management. Compared with the existing management methods, it shows great advantages in terms of operating costs and ease of use. It also promotes the application of artificial intelligence technology in the construction phase.

Multistate Reliability Analysis of Recessive Fault of Mechanical Meta-Action Unit Based on Markov Process

In order to clearly express the reliability dynamic change process of mechanical meta-action units with recessive fault on continuous time series, this paper proposes a reliability analysis method for multistate systems with recessive failures of mechanical meta-action units based on the fusion of vibration signal analysis and Markov process. By analyzing the vibration signal, five major recessive fault types of mechanical meta-action units are determined. By analyzing the experimental data and based on the average time of the first occurrence of five major recessive faults, a performance level state representation model of mechanical meta-action unit based on fault importance weight is established. Then, based on the repairable characteristics of the mechanical element action unit, the two-way state transition model and state probability differential equation of mechanical meta-action units are established, and the state probability of each state is obtained. Next, under the condition of determining the initial state, the change process curves of the instantaneous availability, instantaneous average performance, and instantaneous average performance deficit of the mechanical meta-action unit are obtained by solving the reliability index calculation formula. Finally, this paper takes the worm rotation meta-action unit as an example to verify the law and probability of the state transition of the mechanical meta-action unit, and the performance level change accompanying the state transition process, by analyzing the failure modes and causes of recessive faults, the corresponding reliability control measures are formulated, and the control effects before and after reliability control are analyzed. The research results show that this method can effectively improve the accuracy of the state probability when calculating the state probability of each state, and compared with the discrete Markov model, when studying the reliability of complex multistate systems, this method can dynamically describe the change process of state probability, instantaneous availability, instantaneous average performance, and instantaneous average performance deficit in real time under the condition of continuous time-dependent variables. It has certain guiding significance for the reliability analysis of mechanical element action units in the long-term range.

Fault Tolerant Robust Passivity-Based Control Design for a Proton Exchange Membrane Fuel Cell Power Supply

Abstract The proton exchange membrane fuel cell (PEMFC) best exploitation needs to be based on reliability and fault tolerant control. One of the issues to fulfill this objective is ensuring high reliability indexes of the associated DC/DC converter, which is indispensable in practice. Three-stage interleaved boost converter is proposed in this context with a reliability-based reconfiguration control. The proposed control approach is passivity based, it is robust against high-temperature variations and load perturbations. However, this article proposes a novel method that adapts the reference current by a linear transformation. The fault tolerant control manages the open circuit failure to adapt the control algorithm to two-stage converter reconfigured structure. The matlab/simulink-based simulations show the effectiveness of the proposed control scheme as well as the reconfiguration method, which can give a technical improvement in the exploitation of PEMFC power supplies in the presence of faults and perturbations.

Как научить систему ЧПУ решать технологическую задачу по выбору надёжных значений параметров процесса металлообработки

As in the case of lathe machining, the problem of determining reliable values of the basic parameter such as cutting speed when working with CNC machines has been viewed. The necessity of preliminary diagnostics of contact pairs characteristics as a means of cutting process reliability control under conditions of indeterminacy of their physical and mechanical properties is indicated. The selection of processing conditions with autoatically-controlled method is described.

A High-Robust Sensor Activity Control Algorithm for Wireless Sensor Networks.

In wireless sensor networks, it is important to use the right number of sensors to optimize the network and consider the key design and cost. Due to the limited power of sensors, important issues include how to control the state of the sensor through an automatic control algorithm and how to power-save and efficiently distribute work. However, sensor nodes are usually deployed in dangerous or inaccessible locations. Therefore, it is difficult and impractical to supply power to sensors through humans. In this study, we propose a high reliability control algorithm with fast convergence and strong self-organization ability called the sensor activity control algorithm (SACA), which can efficiently control the number of sensors in the active state and extend their use time. In the next round, SACA considers the relationship between the total number of active sensors and the target value and determines the state of the sensor. The data transmission technology of random access is used between the sensor and the base station. Therefore, the sensor in the sleep state does not need to receive the feedback packet from the base station. The sensor can achieve true dormancy and power-saving effects. The experimental results show that SACA has fast convergence, strong self-organization capabilities, and power-saving advantages.

A Short Review on Surface‐Confined Monolayers of π‐Conjugated Polymers for Photovoltaics

π‐conjugated polymer (CPs)‐functionalized semiconductor surfaces are important components of photovoltaic (PV) devices. In this context, the use of surface‐confined monolayers (SCMs) of CPs has attracted great attention due to their operational stability, reliability, and excellent control over the nanostructure and morphology of CP films. The CPs with proper anchoring groups are covalently bonded to the semiconductor surface driven by the interactions between anchors and substrate, forming densely packed and orderly assembled polymer chains in thin films. The nanostructure of CP SCMs is highly dependent on the type and position of anchors, polymer structure, and chain length, as well as the film‐forming techniques. Herein, general features, anchoring mechanism, grafting approaches, and PV properties of CP SCMs are described and analyzed. Then, the applications of CP monolayers as photosensitizers for dye sensitized solar cells and hole transport interlayers for bulk‐heterojunction polymer solar cells and perovskite solar cells are summarized. With the structure tunability, future study directions for CP SCMs in terms of structure effect, assembly states, and efficiency enhancement are discussed.

Centralized Control System for Smart Street Lights Based on STM32 and LoRa

In order to solve the problem of waste of power and human resources in the traditional street lamp control system, this paper designs a smart street lamp centralized control system based on STM32 and LoRa technology. The centralized control system consists of a centralized control device and multiple single lamp control devices. The STM32F103 is the MCU of the centralized control device. The system uses the characteristics of low communication cost and long transmission distance of LoRa technology to realize the communication between the centralized control device and the single lamp control device. The centralized control device uses the Modbus RTU protocol to communicate with the sensor module, and adjusts according to environmental factors. The brightness of the street lamp realizes the "on-demand lighting" of the street lamp, and an adaptive rate algorithm is proposed to optimize the communication of the LoRa network and improve the working efficiency of the system. This centralized control system combines embedded and wireless communication technology and has advantages in cost, reliability, energy saving and intelligent control. It can be applied to street lamp lighting control in various environments. It has a high degree of intelligence and has a strong market potential.

Objective Bayesian inference for the Capability index of the Weibull distribution and its generalization

The Weibull distribution plays an important role in reliability and quality control monitoring. This model has been widely used to describe the process capability index (PCI) when data do not follow a normal distribution. In this scenario, the current studies focus on estimating the parameters using classical inference. In this paper, we consider Bayesian methods to estimate the PCI denominated Cpk from an objective perspective using reference priors. The proposed inference is further extended to a generalized version of the Weibull distribution that provides a good fit for more complex data with non-monotone hazard behavior. The posterior distributions are constructed and Bayes estimators based on the median are proposed. In this case, Markov Chain Monte Carlo methods are used to achieve the estimates and from an extensive simulation study, we observe that good results are observed in terms of mean relative and squared errors. The proposed approach is also used to construct adequate credibility intervals with low computational cost and accurate coverage probabilities. A real data application is presented which confirms that our proposed approach outperforms the current methods.

Change-level detection for Lévy subordinators

Let X=(Xt)t≥0 be a process behaving as a general increasing Lévy process (subordinator) prior to hitting a given unknown level m0, then behaving as another different subordinator once this threshold is crossed. This paper addresses the detection of this unknown threshold m0∈[0,+∞] from an observed trajectory of the process. These kind of model and issue are encountered in many areas such as reliability and quality control in degradation problems. More precisely, we construct, from a sample path and for each ε>0, a so-called detection level Lε by considering a CUSUM inspired procedure. Under mild assumptions, this level is such that, while m0 is infinite (i.e. when no changes occur), its expectation E∞(Lε) tends to +∞ as ε tends to 0, and the expected overshoot Em0([Lε−m0]+), while the threshold m0 is finite, is negligible compared to E∞(Lε) as ε tends to 0. Numerical illustrations are provided when the Lévy processes are gamma processes with different shape parameters.

Robust Stability of DC Microgrid Under Distributed Control

Compared with AC microgrid, DC microgrid has attracted more and more attention due to their high reliability and simple control. In this paper, we analyze the existence and stability of equilibrium of DC microgrid under distributed control. Firstly, the power-flow equation of the DC microgrid with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> DGs and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</i> CPLs is built. On this basis, under the limitation of currents of DGs, the sufficient solvability conditions are obtained based on Brouwer’s fixed-point theorem. Secondly, we build the small-signal model to forecast the system qualitative behavior around equilibrium. The eigenvalue analysis based on inertial theorem provides the analysis basis for system stability, then we obtain the robust stability condition based on the properties of a special matrix. The obtained stability conditions provide a reference for establishing a stable DC microgrid. Lastly, simulation results verify the correctness of the proposed theorems.

Critical Aspects of Electric Motor Drive Controllers and Mitigation of Torque Ripple—Review

Electric vehicles (EVs) are playing a vital role in sustainable transportation. It is estimated that by 2030, Battery EVs will become mainstream for passenger car transportation. Even though EVs are gaining interest in sustainable transportation, the future of EV power transmission is facing vital concerns and open research challenges. Considering the case of torque ripple mitigation and improved reliability control techniques in motors, many motor drive control algorithms fail to provide efficient control. To efficiently address this issue, control techniques such as Field Orientation Control (FOC), Direct Torque Control (DTC), Model Predictive Control (MPC), Sliding Mode Control (SMC), and Intelligent Control (IC) techniques are used in the motor drive control algorithms. This literature survey exclusively compares the various advanced control techniques for conventionally used EV motors such as Permanent Magnet Synchronous Motor (PMSM), Brushless Direct Current Motor (BLDC), Switched Reluctance Motor (SRM), and Induction Motors (IM). Furthermore, this paper discusses the EV-motors history, types of EV-motors, EV-motor drives powertrain mathematical modelling, and design procedure of EV-motors. The hardware results have also been compared with different control techniques for BLDC and SRM hub motors. Future direction towards the design of EV by critical selection of motors and their control techniques to minimize the torque ripple and other research opportunities to enhance the performance of EVs are also presented.

Fault Tolerant Control Based SFO-Intelligent PID: Application on SynRM for Flywheel Energy Storage Systems -Part I

Reliability, safety, and fault-tolerant control (FTC) for power systems are substantially raised in several industrial applications. It is critical to correctly diagnosis the faults occurring in a drive system to avoid harmful accidents and to ensure continuity of operation. It is necessary to design proper control techniques to recover the faulty system’s performance/functionality to its nominal level. This research work divided into two parts a design scheme of intelligent fault-tolerant control based artificial intelligence techniques (AI) is described and implemented integrates common knowledge acquisition methods with techniques developed in the fields of model-based diagnosis (MBD) to reach the so-called towards intelligent fault tolerant control to provide reliability and maintain the stability of the system in desired performance automatically; This paper presents the first part of this scheme proposes stator-flux oriented (SFO) control with GA-tuned proportional-integral-derivative (PID) controller. The principle is to set the q axis component of stator current invariant during healthy and faulty operating mode. The overall development scheme is summarized and an example illustrates features of the methods performed on a 1.0kW SynRM drive. Results presented have shown that the FT controller scheme developed is potentially capable of dealing with a larger number of faults and can successfully keep a good dynamic performance during faulty mode.

Demonstration of the reliability of a 5-kW-level oscillating-amplifying integrated fiber laser.

A novel fiber laser called an oscillating-amplifying integrated fiber laser was studied experimentally, in which the oscillating section and amplifying section share the pump between them. Based on this configuration, a 5-kW fiber laser system with optical-optical efficiency of 80.9% and M2 factor of 1.5 was achieved. The startup and shutdown sequence of the laser was studied in detail. When pumps of the laser were deliberately turned on in an inverted order, such as switching on/off the amplifying section before/after the oscillating section, which is normally disastrous in a classic fiber amplifier, the laser system turned out to operate stably at full power level. Thus, it is verified that there is no priority between the amplifier and the seed in this laser system. It combines the advantages of conventional fiber oscillators and fiber amplifiers, including high efficiency, high reliability, good anti-backreflection, and simple control logic.

Root cause identification approach using decomposition of QFD and extended RPN for product manufacturing reliability degradation

Reliability is reflected in product during manufacturing. However, due to uncontrollable factors during production, product reliability may degrade substantially after manufacturing. Thus, root cause analysis is important in identifying vulnerable parameters to prevent the product reliability degradation in manufacturing. Therefore, a novel root cause identification approach based on quality function deployment (QFD) and extended risk priority number (RPN) is proposed to prevent the degradation of product manufacturing reliability. First, the connotation of product manufacturing reliability and its degradation mechanism are expounded. Second, the associated tree of the root cause of product manufacturing reliability degradation is established using the waterfall decomposition of QFD. Third, the classic RPN is extended to focus on importance to reliability characteristics, probability, and un-detectability. Furthermore, fuzzy linguistic is adopted and the integrated RPN is calculated to determine the risk of root causes. Therefore, a risk-oriented root cause identification technique of product manufacturing reliability degradation is proposed using RPN. Finally, a root cause identification of an engine component is presented to verify the effectiveness of this method. Results show that the proposed approach can identify the root cause objectively and provide reference for reliability control during production.

Express method of electro-physical parameters extraction for power Schottky diodes

A detailed express procedure of basic electro-physical parameters determination for power Schottky diodes has been presented. The procedure was approved using commercial diodes based on 4H-SiC by CREE, Inc. It relies on the device physical model only and almost excludes the usage of experimental data as the initial calculation parameters. During the calculation procedure the information available in the device datasheet is mainly used. If necessary, it may be supplemented by simple C-V and I-V characterization data of the device. The calculation technique is remarkable for its ability to extract electro-physical parameters of Schottky diodes with high accuracy and reliability. This is due to both: the usage of analytical equations instead of mathematical processing of the device characterization data presented graphically as well as the implementation of reliability control of obtained electro-physical parameters by calculating on their basis already known characteristics and parameters given in the datasheet or in the literature. The results of this work could promote the development of power Schottky and p-i-n diode operation theory at high temperatures.

Mathematical model of mobile network reliability control based on nonlinear proportional differential

Mathematical model of mobile network reliability control based on nonlinear proportional differential

Toward carbon-neutral electricity and mobility: Is the grid infrastructure ready?

Toward carbon-neutral electricity and mobility: Is the grid infrastructure ready?

Joint Watermarking for Image Reliability Control in Encrypted and Compressed Domains

Sharing medical images can enormously help doctor in their day by day practice by permitting them to arrive at a symptomatic all the more rapidly. However, such images are sensitive and should be ensured. In this paper, propose the principal joint watermarking-encryption pressure conspire for the insurance of medical images. Its inventiveness is twofold. In a first time, it permits the admittance to watermarking-based security administrations from the scrambled and the compacted bitstreams without parsing them even part of the way. It gets conceivable to follow images and control their dependability from both the encoded and compacted spaces. In a second, it remains on the blend of spot replacement watermarking, JPEG-LS and the AES block figure in its CBC mode to make our plan. Tests directed on various medical images modalities (radiographic and retina pictures) show the ability of our framework to safely make accessible a message in both scrambled and compacted areas while limiting the contortion of the images.

Research on Reliability of Information Push Robot Based on Image Self-learning

In order to improve the intelligent control ability of information push robot, an output reliability control method of information push robot based on image self-learning is proposed. The parameter identification model of robot output reliability positioning is constructed, and the pose correction and adaptive adjustment of information push robot are carried out by combining fuzzy constraint parameter control method. Adaptive learning control law is adopted to carry out adaptive feedback correction of robot spatial distribution parameter error under computer image vision, and computer image vision information tracking fusion method is adopted to realize adaptive positioning and fuzzy control of information push robot, and improve the ability of pose correction and error feedback adjustment. The simulation results show that the output reliability control accuracy of the information push robot is high and the adaptive control ability is good.