Normal Operation Behavior Research Articles

Spatiotemporal assessment of post-earthquake road network resilience using a data-driven approach

An accurate assessment of the spatial and temporal changes in the post-earthquake road network is essential for scientifically establishing emergency rescue programs and recovery strategies. This paper utilizes high-resolution traffic data to evaluate the resilience of city-to-city road networks to earthquakes. Additionally, it elucidates the distribution of link-level resilience in both temporal and spatial dimensions. Through a case study of the Luding earthquake, the results indicate that the road network's recovery duration exceeds five days, emphasizing the persistent and temporally delayed impact of earthquakes on traffic flow. Specifically, earthquakes disrupt link resilience, causing it to fluctuate around normal operational behavior. In the immediate aftermath of the earthquake, travel time from the epicenter to Luding surged by 132 %, triggering congestion that spread to neighboring cities. At the spatial level, spatial autocorrelation in link resilience is evident, with concentrated clusters of low-resilience links observed in segments such as G248 and S211. Our findings offer valuable and novel insights into the resilience of road networks to earthquakes from an empirical perspective.

Long-short term memory networks aided fault detection of power facilities

In recent years, long-short term memory (LSTM) networks have emerged as a promising tool for time-series analysis, offering the ability to capture temporal dependencies effectively, especially for the fault detection in power facilities which is crucial for maintaining operational integrity and ensuring continuous power supply. This paper presents a study on the utilization of LSTM networks for fault detection in power facilities, focusing on evaluating the detection accuracy as a performance metric. We propose a novel LSTM-based fault detection framework that utilizes historical operational data to identify abnormal patterns indicative of faults or anomalies. The framework is trained on labeled data to learn the normal operational behavior of the power facility, enabling it to detect deviations from expected patterns in practical data streams. To assess the effectiveness of the proposed approach, extensive experiments are conducted using real-world operational data from diverse power facilities. We systematically evaluate the detection accuracy of the LSTM-based fault detection system under various operating conditions, including different types of faults and levels of noise in the data. Comparative analyses are performed against traditional fault detection methods to demonstrate the superiority of the LSTM-based approach in terms of accuracy. Moreover, sensitivity analyses are conducted to investigate the impact of key parameters, such as network architecture and training data size, on the detection performance. The results illustrate the robustness and effectiveness of the proposed LSTM-based fault detection framework, highlighting its potential for enhancing the reliability and efficiency of power facility operations.

A multivariate Gaussian mixture model for anomaly detection in transient current signature of control element drive mechanism

The analysis of transient coil current signals for anomaly detection in a control element drive mechanism (CEDM) involves identifying signature mismatch based on comparison to normal signal profiles. This paper presents an anomaly detection model for optimizing the health monitoring framework for CEDMs. Our approach is a machine learning clustering algorithm based on the multivariate Gaussian Mixture Model (GMM), for the detection of incipient abnormal transient signatures in CEDM coils. The underlying considerations for the choice of a GMM-based clustering approach are that (1) the transient coil current profiles at normal operational behavior are very similar, and (2) only very few data are usually available that describe anomalous CEDM behavior. Since available data is heavily skewed toward normal patterns, this methodology allows us to establish the norm for detecting anomalous patterns based on a preset detection threshold. The model evaluation result shows a very low false-negative rate for anomaly detection.

An integrated data-driven scheme for the defense of typical cyber–physical attacks

With the frequent occurrence of safety incidents in cyber–physical systems (CPSs), great significance has been attached to the study of defense schemes against cyber–physical attacks. In this paper, an integrated data-driven defense scheme is proposed, which can sensitively detect data integrity attacks such as false data injection (FDI) attacks, denial-of-service (DoS) attacks, and replay attacks, and ensures secure transmission against eavesdropping attacks. Specifically, a novel deep learning model is designed so that both the online detection task and the encryption/decryption task can be completed under the same framework. The main idea is inspired by denoising auto-encoders whereas necessary changes are made to adapt to the challenges in the context of CPS attacks, and in light of this, the proposed approach is called modified denoising auto-encoder (MDAE). Unlike supervised classifier-based detectors, the proposed detector can retain sensitivity to unknown attacks because it is trained to learn the normal operation behavior. Moreover, to improve the detectability of the DoS and replay attacks on all data, the check code is designed. Encrypting the transmitted data through nonlinear mapping is achieved using the same MDAE, which prevents the attackers from recording useful information. Benefiting from the fact that the dimension of the variables is reduced after encryption, the transmission traffic can be saved. Simulation results on the measurement data instances generated by the IEEE 118-bus system validate the encryption effects and detection accuracy of the proposed scheme and show the superiority by comparison study.

Analysis of gas preparation processes for improvement of gas transportation technology

At production, collection and transport of low – pressure gas to deep water offshore platforms in sea conditions because of thermodynamic indices change in the system, complications are generated in connection with liquid phases – separation. These complications disturb normal operational well behavior, gas preparation unit and trunk (main) pipeline conditions. As a result of these phenomena high – volume losses of gas, gas condensate and chemical reagent take place.
 In the process of testing, the following process parameters were determined: pressure, gas temperature, facility performance, regeneration temperature, amount of absorbent injected into the gas flow, concentration of regenerated and saturated absorbent, dry gas dew point and so on. In the process of investigating the effect of the amount of inhibitor on the degree of corrosion prevention, hydrate formation and salt deposit at the facilities, regression equations. That is why, to guarantee uninterrupted transportation of low-pressure gas in field conditions, new methods are required for these phenomena prevention.
 On the basis of field study results some variants of calculation were given to increase efficiency of low-pressure gas transportation system in offshore oil and gas field’s conditions.
 Results of high-pressure gas optimal working pressure calculation for precipitated liquid phase displacement at low-pressure petroleum gas transportation to deepwater offshore platforms are shown in the article.
 As well, method for precipitated liquid phase displacement from low-pressure gas pipeline with usage of high-viscosity elastic gelling compositions on the basis of domestic petrochemical products

Mondrian conformal anomaly detection for fault sequence identification in heterogeneous fleets

We considered the case of monitoring a large fleet where heterogeneity in the operational behavior among its constituent units (i.e., systems or machines) is non-negligible, and no labeled data is available. Each unit in the fleet, referred to as a target, is tracked by its sub-fleet. A conformal sub-fleet (CSF) is a set of units that act as a proxy for the normal operational behavior of a target unit by relying on the Mondrian conformal anomaly detection framework. Two approaches, the k-nearest neighbors and conformal clustering, were investigated for constructing such a sub-fleet by formulating a stability criterion. Moreover, it is important to discover the sub-sequence of events that describes an anomalous behavior in a target unit. Hence, we proposed to extract such sub-sequences for further investigation without pre-specifying their length. We refer to it as a conformal anomaly sequence (CAS). Furthermore, different nonconformity measures were evaluated for their efficiency, i.e., their ability to detect anomalous behavior in a target unit, based on the length of the observed CAS and the S-criterion value. The CSF approach was evaluated in the context of monitoring district heating substations. Anomalous behavior sub-sequences were corroborated with the domain expert leading to the conclusion that the proposed approach has the potential to be useful for both diagnostic and knowledge extraction purposes, especially in domains where labeled data is not available or hard to obtain.

An efficient intrusion detection system for cognitive radio networks with improved fuzzy logic based spectrum utilization

Wireless Communication is one of most demanded and technologically evolving field due to the growing services which is regarded as the foremost challenge. Spectrum scarcity arises due to the growing services in turn demands new spectrum bands and also the allocated spectrum is not resourcefully utilized. Cognitive radio (CR) is deliberated as key solutions for effective utilization of spectrum band. Among the CR techniques, fuzzy logic based framework forms the foundation for this research. The Spectrum Access Control is mainly achieved in the prevailing Fuzzy Logic System (FLS), however precise results are obtained due to the assumptions involved in perceiving outcome. Additionally, more concentration is given for spectrum utilization efficacy and spectrum utilization with security is not accomplished. The familiar security extortions of wireless systems are inherited by means of Cognitive Radio Networks which is mitigated through novel approach called enhanced spectrum access control (ESAC) by chiefly exploiting probabilistic neural network for spectrum access control. As well as, the antecedents are regarded for selection of spectrum for secondary user with various possibilities. An enhanced Intrusion Detection System (IDS) expending support vector machine is suggested for network anomaly behavior detection which is attained through normal protocol operation behavior, traffic flow and primary user access time. These factors help in recognition of abnormal activities for attaining security. The suggested intrusion detection has its major role in spectrum utilization performance enhancing pertaining to Throughput, System Utility Rate and Packet Delivery Ratio which is validated by the experimental results, which is the spectrum scarcity, despite the fact that new spectrum bands are greatly necessitated; in addition, underutilized licensed spectrum are presently observed. Security threats is one of the major threat of all basic wireless networks including CRNs because of inheriting property.

A Moving Target Defense for Securing Cyber-Physical Systems

This article considers the design and analysis of multiple moving target defenses for recognizing and isolating attacks on cyber-physical systems. We consider attackers who perform integrity attacks on a set of sensors and actuators in a control system. In such cases, a model aware adversary can carefully design attack vectors to bypass bad data detection and identification filters while causing damage to the control system. To counter such an attacker, we propose the moving target defense which introduces stochastic, time-varying parameters in the control system. The underlying random dynamics of the system limit an attacker's model knowledge and inhibits his/her ability to construct stealthy attack sequences. Moreover, the time-varying nature of the dynamics thwarts adaptive adversaries. We explore three main designs. First, we consider a hybrid system where parameters within the existing plant are switched among multiple modes. We demonstrate how such an approach can enable both the detection and identification of malicious nodes. Next, we investigate the addition of an extended system with dynamics that are coupled to the original plant but do not affect system performance. An attack on the original system will affect the authenticating subsystem and in turn be revealed by a set of sensors measuring the extended plant. Lastly, we propose the use of sensor nonlinearities to enhance the effectiveness of the moving target defense. The nonlinear dynamics act to conceal normal operational behavior from an attacker who has tampered with the system state, further hindering an attacker's ability to glean information about the time-varying dynamics. In all cases mechanisms for analysis and design are proposed. Finally, we analyze attack detectability for each moving target defense by investigating expected lower bounds on the detection statistic. Our contributions are tested via simulation.

Machine Learning Approach Using MLP and SVM Algorithms for the Fault Prediction of a Centrifugal Pump in the Oil and Gas Industry

The demand for cost-effective, reliable and safe machinery operation requires accurate fault detection and classification to achieve an efficient maintenance strategy and increase performance. Furthermore, in strategic sectors such as the oil and gas industry, fault prediction plays a key role to extend component lifetime and reduce unplanned equipment thus preventing costly breakdowns and plant shutdowns. This paper presents the preliminary development of a simple and easy to implement machine learning (ML) model for early fault prediction of a centrifugal pump in the oil and gas industry. The data analysis is based on real-life historical data from process and equipment sensors mounted on the selected machinery. The raw sensor data, mainly from temperature, pressure and vibrations probes, are denoised, pre-processed and successively coded to train the model. To validate the learning capabilities of the ML model, two different algorithms—the Support Vector Machine (SVM) and the Multilayer Perceptron (MLP)—are implemented in KNIME platform. Based on these algorithms, potential faults are successfully recognized and classified ensuring good prediction accuracy. Indeed, results from this preliminary work show that the model allows us to properly detect the trends of system deviations from normal operation behavior and generate fault prediction alerts as a maintenance decision support system for operatives, aiming at avoiding possible incoming failures.

Real-Time Identification of Cyber-Physical Attacks on Water Distribution Systems via Machine Learning–Based Anomaly Detection Techniques

Smart water infrastructures are prone to cyber-physical attacks that can disrupt their operations or damage their assets. An algorithm was developed to identify suspicious behaviors in the different cyber-physical components of a smart water distribution system. The algorithm incorporated multiple modules of anomaly-detection techniques to recognize different types of anomalies in the real-time monitoring and control data. Trained artificial neural networks were used to detect unusual patterns that do not conform to normal operational behavior. Principal component analysis was conducted to decompose the high-dimensional space occupied by the sensory data to uncover global anomalies. The algorithm was trained using a historical data set of trusted observations and tested against a validation and a test data set, both featuring a group of simulated attack scenarios. The proposed approach successfully identified all the attacks featured in the Battle of the Attack Detection Algorithms (BATADAL) data sets with high sensitivity and specificity. Nevertheless, the performance was sensitive to high background noise in the sensory data.

Stability Analysis of Discrete Event Systems Modeled by Petri Nets Using Unfoldings

The stability of discrete event systems (DESs) is a property related to its robustness; a stable DES is guaranteed to reach a set of desired states in a finite number of steps. Algorithms proposed in the literature are useful for analyzing the stability of DES modeled either by finite automata or a constrained subclass of Petri nets (PNs). In this paper, a novel method to decide the stability of safe PNs is presented. It is based on the analysis of the finite complete prefix of the net unfolding rather than the reachability graph. For concurrent systems, the prefix is usually smaller than the reachability graph and then the analysis using the proposed algorithm is more efficient than analyzing the reachability set of the PN. Note to Practitioners —The stability of a discrete event system (DES) is a property that guarantees that the DES will return to the normal operation behavior after a disturbance deviates the system from such a behavior. This is a key property that a controlled DES must fulfill. The algorithms proposed in this paper allow determining efficiently the stability of concurrent DES by analyzing the Petri net structure.

Anomaly detection and predictive maintenance for photovoltaic systems

Abstract We present a learning approach designed to detect possible anomalies in photovoltaic (PV) systems in order to let an operator to plan predictive maintenance interventions. The anomaly detection algorithm presented is based on the comparison between the measured and the predicted values of the AC power production. The model designed to predict the AC power production is based on an Artificial Neural Network (ANN), that is capable of estimating the AC power production using solar irradiance and PV panel temperature measurements, and that is trained using a dataset previously gathered from the plant to be monitored. Live trend data coming from the PV system are then compared with the output of the model and the vector of residuals is analyzed to detect anomalies and generate daily predictive maintenance alerts; there residuals are aggregated over 1-day and processed to detect out-of-threshold samples and system degradation trends; these trends are extracted by computing the Triangular Moving Average (TMA) where the window size is automatically determined. The paper also reports experimental data results revealing that the model leads to a good anomaly detection rate, which is measured as a positive predictive detection rate greater than 90%. Moreover, the algorithm is able to recognize trends of system’s deviations from normal operation behavior and generate predictive maintenance alerts as a decision support system for operatives, with the aim of avoiding possible incoming failures.

Neutronics and thermal hydraulic coupling analysis of integrated pressurized water reactor

SUMMARY In this paper, three-dimensional (3D) power distribution of newly designed small nuclear reactor core has been achieved by using neutron kinetic/thermal hydraulic (NK/TH) coupling. This is pressurized water reactor-based small nuclear reactor in which plate type fuel element has been used and the core of the reactor has hexagonal type geometry. This paper depicts the design of the reactor core by using coupling approach of neutronics(Neutron Kinetic) and thermal hydraulic studies. For this purpose, neutronic analysis has been obtained by using lattice physics code, i.e. HELIOS and neutron kinetic code, i.e. REMARK. HELIOS code gives the cross-section data which is being used as input to the REMARK code. At the same time, THEATRe code was used for the thermal hydraulic analysis of the reactor core. In the coupling process, some data (fuel temperature, moderator temperature, void fraction, etc.) from THEATRe code has been used in conjunction with HELIOS and REMARK codes. After finalizing the NK/TH coupling, 3D evaluation of the power distribution of the reactor core has been achieved and is included in the paper. The purpose of this paper is to evaluate the design and get the normal operational behavior of the reactor core by NK/TH coupling approach. Copyright © 2012 John Wiley & Sons, Ltd.

Knowledge-independent traffic monitoring: Unsupervised detection of network attacks

The philosophy of traffic monitoring for detection of network attacks is based on an acquired knowledge perspective: current techniques detect either the well-known attacks on which they are programmed to alert, or those anomalous events that deviate from a known normal operation profile or behavior. In this article we discuss the limitations of current knowledge-based strategy to detect network attacks in an increasingly complex and ever evolving Internet. In a diametrically opposite perspective, we place the emphasis on the development of unsupervised detection methods, capable of detecting network attacks in a changing environment without any previous knowledge of either the characteristics of the attack or the baseline traffic behavior. Based on the observation that a large fraction of network attacks are contained in a small fraction of traffic flows, we demonstrate how to combine simple clustering techniques to accurately identify and characterize malicious flows. To show the feasibility of such a knowledge-independent approach, we develop a robust multiclustering-based detection algorithm, and evaluate its ability to detect and characterize network attacks without any previous knowledge, using packet traces from two real operational networks.

A MOSFET power supply clamp with feedback enhanced triggering for ESD protection in advanced CMOS technologies

A novel power supply protection clamp is presented which incorporates feedback techniques to improve ESD and normal operational mode behavior. The design uses a short duration RC trigger, which enables the clamp to tolerate very fast power supply ramp rates and exhibit reduced area and leakage. The design is built in a 90 nm CMOS technology with fully salicided source/drain regions.

Use of Weapons-Grade Plutonium in Existing PWRs—Supported by German MOX Recycling Experience

It is proposed that weapons-grade Pu recovered under the strategic arms reduction agreement be used in existing light water reactors under the current safety and licensing criteria. Based on experience with the commercial use of mixed-oxide (MOX) fuel in German pressurized water reactors, MOX fuel assemblies containing weapons-grade Pu have been designed. The related fuel management studies are presented. The possibility of using corresponding hexagonal MOX fuel assemblies in Russian-type VVER 1000 reactors is also discussed. In addition, the use of weapons-grade Pu in modern boiling water reactors is addressed. It can be stated that safe normal operation and benign accident behavior are achievable.

Parameter Study on the Influence of Prepressurization on PWR Fuel Rod Behavior during Normal Operation and Hypothetical LOCAs

To analyze the influence of prepressurization on fuel rod behavior, a parametric study has been performed that considers the effects of as-fabricated fuel rod internal prepressure on the normal operation and postulated loss-of-coolant accident (LOCA) rod behavior of a 1300-MW(electric) Kraftwerk Union (KWU) standard pressurized water reactor nuclear power plant.A variation of the prepressure in the range from 15 to 35 bars has only a slight influence on normal operation behavior. Considering the LOCA behavior, only a small temperature increase results from prepressure reduction, while the core-wide straining behavior is improved significantly. The KWU prepressurization takes both conditions into account.