System Performance Degradation Research Articles

A note on diagnosis and performance degradation detection in automatic control systems towards functional safety and cyber security

This note addresses diagnosis and performance degradation detection issues from an integrated viewpoint of functionality maintenance and cyber security of automatic control systems. It calls for more research attention on three aspects: (i) application of control and detection unified framework to enhancing the diagnosis capability of feedback control systems, (ii) projection-based fault detection, and complementary and explainable applications of projection- and machine learning-based techniques, and (iii) system performance degradation detection that is of elemental importance for today’s automatic control systems. Some ideas and conceptual schemes are presented and illustrated by means of examples, serving as convincing arguments for research efforts in these aspects. They would contribute to the future development of capable diagnosis systems for functionality safe and cyber secure automatic control systems.

Resource Allocation Algorithm Based on Power Control and Dynamic Transmission Protocol Configuration for HAPS-IMT Integrated System

The high altitude platform station (HAPS) system is an essential component of the air-based network. It can shorten transmission delay and make a better user experience compared with satellite networks, and it can also be easily deployed and cover a larger area compared with international mobile telecommunications (IMT). In order to meet the needs of users with asymmetric and random data flow, the spectrum sharing and dynamic time division duplexing (TDD) mode are used in HAPS-IMT heterogeneous network. However, the cross-link interference brought by TDD mode will lead to the degradation of system performance. In this paper, a resource allocation algorithm based on power control and dynamic transmission protocol configuration is proposed. Firstly, a specific timeslot, “low power almost-bank subframe (LP-ABS)”, is introduced into the frame structure of the HAPS physical layer. The transmission protocol designing could mitigate inter-layer interference efficiently by power control in “LP-ABS”. Secondly, the utilization function is adopted for assessing the system performance, which gives attention to both diversified requirements on the quality of services (QoS) and the throughput of the HAPS-IMT system. Simulation results show that power control and resource allocation technologies proposed in this paper can effectively improve system performance and user satisfaction.

Assessing the performance of ultrasound imaging systems using images from relatively high-density random spherical void phantoms: A simulation study.

The development of clinically meaningful, objective, and quantitative methods for assessing the performance of ultrasound imaging systems represents a continuing area of interest. One approach has been to image phantoms with randomly distributed spherical voids. The objectives of this study were: (1) to explore the potential of using relatively high-volume fraction random spherical void (RSV) phantoms as an approach for quantitatively assessing the performance of ultrasound imaging systems; (2) to identify potential metrics that can be used to provide quantitative assessments of images obtained from relatively high-volume fraction RSV phantoms; and (3) to demonstrate changes in the quantitative metrics that can occur as image features are degraded. A series (10 each) of computer-simulated RSV phantoms with a range of RSV volume fractions (0.05, 0.15, and 0.25) were generated. To determine the number of image planes necessary to provide robust measurements, a series of consecutive planes (ranging from 1 to 150) within each type of simulated phantom were analyzed. The observed circular cross-section radii histogram distributions (representing the intersection of each plane with the local distribution of spherical voids) were compared with the theoretical histogram distribution. Simulated phantom images were produced by adding speckle and degradation of imaging system performance was modeled by averaging 1 to 9 neighboring planes to represent increasing elevation plane thicknesses. Quantification of the performance of the imaging system was determined by measuring the: (1) mean number of circular cross-sections detected per image frame; (2) mean fractional area of circular cross-sections detected per image frame; (3) agreement of observed circular cross-section radii histogram distribution with the theoretical distribution (Chi-square statistic); and (4) contrast and contrast-to-noise ratio as a function of observed circular cross-section radius. Results suggest that analyses of a sufficient number of image planes (providing over approximately 3000 total circular cross-sectional areas) provides excellent agreement between the observed and theoretical histogram distributions (mean Chi-square<0.004). For the 0.15 volume fraction series of simulated RSV phantoms, using 150 image plane analyses, phantom images show decreasing mean number of circle cross-sections detected per frame (31.5 ± 0.3, 28.4 ± 0.3, 28.2 ± 0.3, 26.3 ± 0.3, and 25.3 ± 0.3); decreasing mean fractional area of circle cross-sections per frame (0.157 ± 0.002, 0.133 ± 0.001, 0.133 ± 0.001, 0.111 ± 0.001, and 0.108 ± 0.001); and a decreasing agreement with the theoretical histogram distribution of radii (Chi-square values: 0.070 ± 0.004, 0.140 ± 0.005, 0.149 ± 0.007, 0.379 ± 0.011, and 0.518 ± 0.010) for 1, 3, 5, 7, and 9 plane averages, respectively. Contrast and contrast-to-noise measurements as a function of observed circular cross-section radius also demonstrate marked changes with simulated image degradation. Results of this simulation study suggest that analyses of images obtained from relatively high-density RSV phantoms may offer a promising approach for assessing ultrasound imaging systems. The proposed measurements appear to provide reproducible, robust, quantitative metrics that can be compared with corresponding theoretical values to provide quantifiable, objective metrics of imaging system performance.

Formula omitted]-norm constraint kernel adaptive filtering framework for precise and robust indoor localization under the internet of things

The mixed noise such as Gaussian noise together with the abrupt noise widely exists in the indoor environment, which always leads to the problem of performance degradation of the positioning system under the Internet of Things (IoT). In this paper, a novel kernel function named generalized Student’s t kernel (GSt) and a resulting sparse generalized Student’s t kernel adaptive filter (SGStKAF) is proposed to attack this problem. The proposed SGStKAF utilizes the kernel mean p-power error criterion (KMPE) with the L1-norm penalty. The proposed SGStKAF has three significant features. Firstly, the generalized Student’s t kernel can suppress the abrupt noise effectively. Secondly, the L1-norm penalty guarantees that the fixed-point sub-iteration is available so that the more precise solution can be obtained in a few iterations. At last, a sparse structure of neural networks for the implementation of the proposed method can also be obtained via the L1 constraint. Three experiments and comparisons are carried out to prove the effectiveness of the proposed positioning framework in terms of accuracy and robustness in both the simulation situation and the real-world indoor environments.

Reliability degradation prediction of photovoltaic modules based on dependability methods

PurposeThe reliability prediction is among the most important objectives for achieving overall system performance, and this prediction carried out by anticipating system performance degradation. In this context, the purpose of this research paper is to development of methodology for the photovoltaic (PV) modules' reliability prediction taking into account their future operating context.Design/methodology/approachThe proposed methodology is framed by dependability methods, in this regard, two methods of dysfunctional analysis were used, the Failure Mode and Effects Criticality Analysis (FMECA) method is carried out for identification of the degradation modes, and the Fault Tree Analysis (FTA) method is used for identification the causes of PV modules degradation and the parameters influencing its degradation. Then, based on these parameters, accelerated tests have been used to predict the reliability of PV modules.FindingsThe application of the proposed methodology on PWX 500 PV modules' in different regions of Algeria makes it possible to predict its reliability, taking into account the future constraints on its operation. In this case, the temperature and relative humidity vary from one region to another was chosen as constraints. The results obtained from the different regions confirms the reliability provided by the designer of the Saharan cities Biskra, In Salah, Tamanraset, and affirms this value for the two Mediterranean cities of Oran and Algiers.Originality/valueThe proposed methodology is developed for the reliability prediction of the PV modules taking into account their future operating context and, the choice of different regions confirms or disproves the reliability provided by the designer of the PV modules studied. This application confirms their performance within the framework of the reliability prediction.

A Survey on Model-Based Fault Detection Techniques for Linear Time-Invariant Systems with Numerical Analysis

With the ongoing increase in complexity, less tolerance to performance degradation and safety requirements of practical systems has increased the necessity of fault detection (FD) as early as possible. During the last few decades, many research findings have been developed in fault diagnosis that addresses the issue of fault detection and isolation in linear and nonlinear systems. The paper’s objective is to present a survey on various state-of-art model-based FD techniques developed for linear time-invariant (LTI) systems for the interested readers to learn about recent development in this field. Model-based FD techniques for LTI systems are classified as parameter-estimation methods, parity-space-based methods, and observer-based methods. The background and recent progress, in context to fault detection, of each of these methods and their practical applications are discussed in this paper. Furthermore, two different FD techniques are compared via analytical equations and simulation results obtained from the DC motor model. In the end, possible future research directions in model-based FD, particularly for the LTI system, are highlighted for prosperous researchers. A comparison and emerging research topic make this contribution different from the existing survey papers on FD.

Adaptive neural network–based fault estimation for nonlinear systems with actuator faults in simultaneous multiplicative and additive forms

Non-linearities and actuator faults often exist in practical systems which may degrade system performance or even lead to catastrophic accidents. In this article, a fault-tolerant compensation control strategy is proposed for a class of non-linear systems with actuator faults in simultaneous multiplicative and additive forms. First, radial basis function neural network is employed to approximate the system non-linearity. The approximation is achieved by only one adaptive parameter, which simplifies the computation burden. Then, by means of the backstepping technique, an adaptive neural controller is developed to cope with the adverse effects brought by the system non-linearity and actuator faults in multiplicative and additive forms. Meanwhile, the proposed control design scheme can guarantee that the considered closed-loop system is stable. The novelty of the article lies in that the system non-linearity, the additive actuator faults, and the multiplicative actuator faults that often exist in practical engineering are catered for simultaneously. Furthermore, compared with some existing works, the approximation of the system non-linearity is achieved by only one adaptive parameter for the purpose of reducing the computation burden. Therefore, its applicability is more general. Finally, a numerical simulation and a comparative simulation are carried out to show the effectiveness of the developed controller.

Radar main‐lobe jamming suppression and identification based on robust whitening Blind Source Separation and Convolutional Neural Networks

The main-lobe jamming causes serious degradation of radar system performance in practical applications. In recent years, the Blind Source Separation (BSS) method has been applied to suppress the main-lobe jamming. However, the received echoes of target and jamming cannot be effectively identified while the robustness of the conventional BSS method is poor due to the influence of system noise. To tackle these problems, a radar main-lobe jamming suppression and identification method is proposed based on robust whitening BSS and Convolutional Neural Networks methods. First, the target echoes and jamming are separated by the proposed robust whitening BSS method. An iterative optimisation method is proposed to obtain the robust whitening matrix, and sufficient conditions are provided for solvability. Then, a Target echo and Jamming Identification Network (TJINet) is proposed to identify the signals after the robust whitening BSS. The proposed TJINet method uses residual blocks and branch structures to improve classification performance and utilises asymmetric convolution blocks to extract subtle features between the target echo and deceptive jamming. Simulation and experimental results show that the proposed method has better jamming suppression performance than the traditional methods under low signal-to-noise ratio (SNR) conditions, the algorithm converges faster, and also can stably identify the target echo and jamming signals.

Performance degradation in parallel-server systems with shared resources and lack of coordination

Parallel server systems are ubiquitous. Multicore CPUs are in practically every personal device from mobile handsets to high-end desktop PCs. At larger scale, data centers consist of a huge number of physical servers often shared by multiple users (for economic reasons). Moreover, the simultaneous users are typically unaware of each other due to reasons that can be technical (cf. security & privacy), practical (coordination layer would add complexity) and business related (usage can be business sensitive information). The workload patterns of different users may also vary significantly. This results in unpredictable and often long response times. We study means for tackling these challenges. In particular, we consider a model where multiple users (dispatchers) route their jobs to a pool of servers using uncoordinated static dispatching policies. The goal is to determine how different policies interact: whether users’ decisions support each other, or if some decisions are simply counterproductive. The lack of proper coordination is shown to increase the mean response times, with two common and robust dispatching policies: Size-Interval-Task Assignment (SITA) and Round-Robin (RR). We refer to this phenomenon as the price of ignorance.

Fuzzy Prognosis System for Decision Making to Vibrations Monitoring in Gas Turbine

Abstract This paper proposes a decision making approach based on the development of a fuzzy prognostic system to ensure the vibrations monitoring of a gas turbine based on real time information obtained from different installed sensors. In this approach the case of incomplete obtained data which may occur frequently is taken into account by using an approach of full data reconstitution form incomplete data. The proposed fuzzy prognostic system approach presented in this paper allows the analysis of the data obtained via the vibration indicators of a gas turbine system for the accurate identification of the faults to avoid the performance degradation of such systems. In order to prove the robustness of the proposed approach presented in this paper, several tested has been performed.

Improved energy balance calculation of unitary air conditioners via virtual in-situ calibration

Data-driven technologies for intelligent building energy management and automation have been developed under the energy balance state of systems. The datasets required are typically derived using energy and mass balance principles at each system boundary. Hence, the energy balance of systems significantly affects the system and building performances. However, sensor errors in a building energy sensor network can hinder the identification of balance states, thereby causing energy balance calculation errors in operational systems. In this study, the cooling capacity balance errors between the air and refrigerant sides of two unitary air-conditioner systems, caused by measurement errors, are investigated experimentally. Virtual in-situ sensor calibration (VIC) is performed to determine hidden sensor errors that cause the cooling capacity balance error, as well as to improve the balance and the degraded system performance analysis by calibrating the measurement errors. Experiments reveal the extent of cooling capacity balance errors occurring between the air and refrigerant sides under different operational conditions (0.5%–14.8%). VIC reveals erroneous measurements and the manner by which energy balance errors with measurement errors are solved (below 2%). Finally, the system performance analysis of the two test units shows that the VIC-driven calibration datasets improve the accuracy (0.3%–1.8% errors) of the system energy efficiency ratio even for new test cases after VIC is applied. The primary contributions of this study are as follows: (1) VIC is applied to solve energy balance calculation errors; (2) a strategy is proposed to overcome insufficient datasets to ensure energy balance improvements; and (3) calibrated measurements in real systems are validated.

Error performance optimization utilizing the dynamic detection cycle in a SPAD-based free space optical communication system.

Afterpulsing is a critical non-ideal factor of the single photon avalanche diode (SPAD) at telecommunication wavelength, which limits the performance of a SPAD-based free space optical communication (FSO) system. Afterpulsing probability (AP) is highly dependent on the SPAD detection cycle. In a conventional SPAD-based system, the detection cycle is set to a relatively large constant time length to mitigate afterpulsing. However, it will limit the SPAD counting rate and degrade system performance. In order to improve system performance, a new scheme of the dynamic detection cycle is proposed to adapt to different operation conditions. Then, a multi-exponential model of AP is built and fitted with the test data of the single photon detector QCD-300. Furthermore, based on the joint model of the multi-exponential model of AP and bit error rate (BER) model of the SPAD-based FSO system, a simple and effective optimization algorithm is developed to optimize the detection cycle. And the optimization of the detection cycle under different operation conditions is also investigated. The results indicate that optimal detection cycle is dominated by signal light, background radiation, and SPAD gate length. Compared with a conventional scheme with a constant detection cycle, the proposed scheme with the dynamic detection cycle can improve system error performance effectively.

Mixed Numerology Interference Recognition Approach for 5G NR

The coexistence of multiple numerologies can cause interference and degrade system performance in 5G devices. In this letter, a convolutional neural networks (CNN)-based mixed numerology interference (MNI) recognition approach is devised to resolve the interference for user equipment (UE) to receive new radio (NR) downlink signal. The results are learned from the frequency domain magnitude and phase waveform, for the best precision and efficiency. We use as little sample data as possible to reduce memory/buffer costs and to alleviate the computing resources. Simulations are performed to verify the viability on various signal-noise ratio (SNR), channel condition, modulation, etc. It is shown that the accuracy can reach 97% or higher at varying SNR and fading channels. Furthermore, a dynamic filtering/windowing (DFW) scheme is proposed. The link level simulation results show that the scheme can improve the attainable Signal to Interference plus Noise Ratio (SINR) by 3~6dB for the indicated cases.

Lumped-Network FDTD Method for Simulating Transient Responses of RF Amplifiers Excited by Intentional Electromagnetic Interference Signals

Unintentional as well as intentional electromagnetic interference can cause interference and performance degradation of microwave circuits or systems. In this article, a modified multiport lumped-network finite-difference time-domain method (FDTD) method based on Y-matrix is proposed, where bilinear transform is used to incorporate the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V–I</i> relation of the lumped-network into FDTD iteration. It is further used for simulating transient responses of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mesfet</small> -based amplifiers, which are often used in many radar and communication systems. The excitation of such an RF amplifier is assumed to be an intentional electromagnetic interference (IEMI) signal but with different waveforms and magnitudes. Parametric studies are performed to show hybrid effects of waveforms and magnitudes of the IEMI signals on the transient coupled voltages at the output port of such <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mesfet</small> -based amplifier, with useful information obtained for understanding the interaction process between the intentional electromagnetic interference signals and active components.

A novel multivariable nonlinear robust control design for turbofan engines

Traditional steady-state control methods are applied to turbofan engines operating in the small region near certain operating conditions, which need to switch controllers for operating in the large region and then may lead to instability and performance degradation of the closed-loop system. In this paper, a novel multivariable nonlinear robust control method for turbofan engines is proposed to improve the control performance within the large region. To enlarge the controllable region, a polynomial state-space model describes the nonlinear characteristics of turbofan engines. Based on the analysis of the closed-loop control system, by using the Lyapunov function theorems, a polynomial robust controller is designed to ensure the stability and desired nonlinear control performance of turbofan engines. Compared with the classical PI, mixed sensitivity, and H∞ control, simulation results show that the proposed method has better transient responses, disturbance rejection, and other control performance for the turbofan engine within the large region.

FAMU: Fault-Tolerant Mutual Assisted Virtual Emotion Barrier System Using Intelligent Smart UAVs

Recently, a concept of virtual emotion barrier by wireless signal gained a lot of interests because it supports human emotion recognition within given IoT environment. If detection of human emotion can be made with possibly high detection accuracy by virtual emotion barrier, it can be utilized to an extensive scope of emotion-based applications and communication software services. But, some IoT devices failures in the barrier may cause emotion-based system performance degradation critically. Also, thanks to recent advent of technology, intelligent smart UAVs with rapid speed are considered as promising devices when existing infrastructures are recovered. In this study, fault-tolerant mutual assisted virtual emotion barrier system is introduced with intelligent autonomous UAVs in convergent smart cities. Moreover, we present a formal definition of the problem whose goal is maximizing entire total accuracy of emotion detection where the failed locations are recovered by UAVs’ movement from initial locations without any potential conflicts among UAVs. A greedy UAV movement scheme that constructs resilient virtual emotion barriers is proposed by handling those failed parts in the barriers continuously. Moreover, the proposed schemes are executed by comprehensive simulations with useful scenarios and we evaluate the earned outcome.

On the Suitability of Intrusion Detection System for Wireless Edge Networks

Multi-access edge computing has become a strategic concept of the Internet of Things. The edge computing market has reached USD several billion and is growing intensively. In the edge-computing paradigm, most of the data is processed close to, or at the edge of, the network. This greatly reduces the computation and communication load of the network core. Moreover, edge computing provides better support for user privacy. On the other hand, an increase in data processing locations will proportionately increase the attack surface. An edge node can be put out of service easily by being flooded with spoofed packets owing to limited capacities and resources. Furthermore, wireless edge nodes are quite vulnerable to energy exhaustion attacks. In this situation, traditional network security mechanisms cannot be used effectively. Therefore, a tradeoff between security and efficiency is needed. This study considered the requirements under which the use of an intrusion detection system (IDS) is justified. To the best of our knowledge, this is a first attempt to combine IDS quality, system performance degradation due to IDS operations, and workload specificity into a unified quantitative criterion. This paper is an extended version of a report published in the proceedings of the ICCSA 2020 and differs from it in many ways. In particular, this paper considers novel mathematical problems regarding the deployment strategies for an IDS and the corresponding inverse problems and provides closed-form solutions for a few previously unsolved problems.

Two-phase spray cooling for high ambient temperature data centers: Evaluation of system performance

A lab-scale spray cooled rack system with a server and simulated heaters, operating with near-ambient temperature coolant of approximately 30 °C, was developed for data center cooling application. This system has significant energy saving potential as it eliminates the need for a chiller unit, which is usually necessary in conventional air-cooling schemes. The system was designed to dissipate a maximum heat load of 5.6 kW. Despite its higher operating temperature, our investigation shows that the spray cooled system leads to better cooling performance of the microprocessors as compared to conventional air-cooling scheme. A durability study for more than 40 h of cumulative intermittent operation showed no degradation in system performance when operated at high chamber temperature conditions (>35 ℃). The thermal performance of the spray cooled system and the effects of parameters such as spray nozzle flow rate, condenser fan power, condenser flow rate and heat loads were systematically characterized. It is found that the thermal performance of the simulated heaters is mainly affected by the spray nozzle flow rate, while the condenser fan power and condenser cooling water flow rate strongly affect the condensation rate and are, therefore, critical parameters for spray chamber pressure control. In summary, this work demonstrates the possibility of employing the newly developed spray cooled system for the thermal management of high-performance servers of data centers in tropical climates and identified key operating parameters that are useful for the design, operation and implementation of this system.

Enhancing 2,6-dichlorophenol degradation and nitrate removal in the nano-zero-valent iron (nZVI) solid-phase denitrification system

Nano-zero-valent iron (nZVI), as a typical nano-material, has been recently used in wastewater treatment and combination with bioreactors. Using nZVI coupled denitrification system research the effect and influence of nZVI enhanced denitrification sludge on the degradation of toxic compounds and system performance. The nZVI coupled denitrification system showed better resistance to 2,6-DCP impact, and the concentrations of effluent NO2− and NO3− were below 2.0 mg/L. At the same time, the addition of nZVI enabled the denitrification system to quickly adapt to the toxic environment of 2,6-DCP within 15 days, and the degradation efficiency of 2,6-DCP reached 99.9%. The released SMP reduced after nZVI coupled with denitrification sludge in 2,6-DCP environment, which could improve the effluent water quality. Nuclear magnetic resonance spectroscopy showed that the addition of nZVI would change the structure of EPS in denitrification sludge. After 90 days of operation, the dominant bacteria in the denitrifying sludge have undergone great changes. Moreover, Thauera was responsible as the dominant bacteria for degrading 2,6-DCP in the denitrification system. The increased in the proportion of functional bacteria with nitrate_reduction, nitrogen_respiration, nitrate_respiration and nitrite_respiration in the presence of NZVI further reveals the mechanism of enhanced denitrification.

Transceiver Imbalances Compensation and Monitoring by Receiver DSP

In-phase and quadrature (IQ) imbalances in optical transceiver, such as amplitude imbalance, phase imbalance, and IQ skew, can cause serious system performance degradation, especially when high order modulation formats are used. In this work, we present a method to isolate and separately monitor these various impairments from received signals by coherent digital signal processing (DSP). Receiver amplitude and phase imbalances could be compensated and monitored by Gram-Schmidt orthogonalization procedure (GSOP) and receiver skew could be compensated and monitored by a 4 x 4 or 4 x 2 multiple-input and multiple-output (MIMO) equalizer. Transmitter imperfections compensation and monitoring take place behind the residual frequency offset compensation and carrier phase recovery by a 4 x 4 MIMO equalizer based on minimal mean square error (MMSE) criterion. By these methods, we analyze and investigate the interaction of transceiver's impairments impact on each monitor numerically. After that, we conduct an experiment to verify the feasibility of the proposed method. All of these results show that each monitor could operate very robust even when multiple co-exist.