Faulty Situations Research Articles

Intelligent optimisation in smart and sustainable compressed air systems: Towards support for decision-making under faulty conditions

The monitoring of complex industrial systems through the implementation of smart approaches provides unique opportunities, such as the characterisation of their real-time performance. Within this scope, there exists the need to support decision-making during maintenance processes, due to the presence of a multitude of faults in real-world systems and the difficulty of identifying the appropriate mitigating solutions. The proposed solution uses intelligent optimisation techniques to identify the ideal control solution within these complex systems when faults arise. This paper presents a framework based on an intelligent optimisation approach, which provides a workflow process for the support of decision-making during faulty situations. It is adapted and implemented to the demand-side of a Compressed Air System (CAS), thus providing a holistic approach in automating fault mitigation during real-time system operation. In implementing this framework, multiple intelligent optimisation techniques such as the Genetic Algorithm and the Particle Swarm Optimisation algorithms were adopted and implemented. Both algorithms were successful in providing the ideal control solution under fault conditions. For a typical production case study, the proposed optimisation approach results in a reduction of 40% in its air consumption, which directly improves its environmental performance and energy costs. This result demonstrates that this approach contributes a suitable control strategy for CAS when experiencing a pneumatic fault, which has a direct positive effect on the operational energy performance and costs.

ANN-Based Statistical Computation for Remote End Fault Monitoring of the IEEE 14 Bus Microgrid Network

This paper focuses on an artificial neural network-based fog computing approach for remote end fault super vision in microgrid systems. The work presented here considers an Artificial Neural Network-based adaptive Line-to-Ground (L-G) and Line-to-Line (L-L) fault monitoring system for a standard IEEE 14 bus microgrid. Discrete Wavelet Transformation (DWT)-based study of statistical parameters of the currents going out from the various generator buses is carried out both in healthy and faulty situations. Faults (L-G and L-L) are created at different load buses and also an algorithm is proposed for detecting fault location. The rule set proposed here is unaffected by variations in fault resistance, making it very much suitable for ground fault monitoring. It provided satisfactory results when examined with various unknown cases. A fog computing layer helps in fast execution and fewer data storage requirements in the cloud. This assessment may be expanded for other kinds of faults in a microgrid system.

Novel control-aware fault detection approach for non-stationary processes via deep learning-based dynamic surrogate modeling

The use of surrogate models for forecasting dynamic behaviors of processes is a promising approach for optimizing process operation and control. This study aims to utilize the powerful prediction capabilities of deep learning-based dynamic surrogate models (DSMs) for fault detection in non-stationary processes, taking into account the impact of control actions on faults. A novel control-aware fault detection approach, utilizing data-driven dynamic surrogate modeling of closed-loop controlled processes, is proposed to detect potential faults under various control actions and operating conditions. DSMs for both one-step and multi-step ahead prediction are developed and combined to detect various types of faults, based on predictions and residuals while considering control effects. High-fidelity dynamic simulations are used to build first principles-based closed-loop process models and generate data under various operating conditions to train the DSMs. Deep learning methods such as long short-term memory and gated recurrent units are employed for dynamic surrogate modeling, and their performance is compared to select the optimal method. The proposed approach is demonstrated through two case studies using a continuous stirred tank reactor and a distillation column with an advanced control structure. The results consistently demonstrate that the approach can achieve both accurate fault detection and reliable false alarm avoidance under different dynamic operating conditions and faulty situations with varying severities and fault types.

Echo State Networks for anomaly detection in industrial systems

In the field of industrial engineering, it is extremely interesting and useful to develop models that faithfully represent the behaviour of real physical systems, which allow to understand their dynamics, to optimise them or to detect anomalous or faulty situations. This paper proposes the modelling of the dynamics of water tanks within an industrial plant with real instrumentation, using Echo State Networks. The developed models have been used to explore the detection of anomalies in the system by analysing the differences between the estimated system state and the actual state after the introduction of faults.

Hybrid Fault-Tolerant Control for Air-Fuel Ratio Control System of Internal Combustion Engine Using Fuzzy Logic and Super-Twisting Sliding Mode Control Techniques

Safety and critical applications employ fault-tolerant control systems (FTCS) to increase reliability and availability in the event of a failure of critical components. Process facilities may employ these technologies to cut down on production losses caused by equipment failures that occur on an irregular or unscheduled basis. Air–fuel ratio (AFR) adjustment in the fuel system of internal combustion engines (ICE) is crucial for enhancing engine efficiency, saving fuel energy, and safeguarding the environment. This paper proposes a novel hybrid fault-tolerant control system (HFTCS) for controlling the AFR in ICEs that combines the features of both an active fault-tolerant control system (AFTCS) and a passive fault-tolerant control system (PFTCS). The fault detection and isolation (FDI) unit is designed using fuzzy logic (FL) as part of an AFTCS to give estimated sensor values to the engine controller when the sensor becomes faulty. Super-twisting sliding mode control (ST-SMC) is implemented as part of a PFTCS to maintain AFR by adjusting the throttle actuator in the fuel supply line under faulty conditions. Lyapunov stability analysis is also performed to make sure that the system remains stable in both normal and faulty conditions. According to the results in the Matlab/Simulink environment, the suggested system stays robust and stable during sensor faults. In faulty situations, it also maintains the AFR at 14.6 without any degradation, and a comparison with previous studies is carried out. The study shows that the suggested approach is an innovative and highly dependable solution for AFR control in ICEs, preventing engine shutdown and output loss for higher profitability.

Secure power management strategy for direct torque controlled fuel cell/ supercapacitor electric vehicles

High reliability is recommended in hybrid electric vehicle applications. In this study, a secure power management strategy has been developed for a fuel cell—supercapacitor hybrid electric vehicle. In addition to its ability to detect the occurrence of failures in vehicle power sources, the proposed power management strategy isolates the faulty source and reconfigures the control scheme to always guarantee bus voltage stability and vehicle traction even in faulty situations. The developed power management strategy enhances vehicle comfort and prevents exhausting one source over another by allowing the fuel cell and the supercapacitor to operate at different power levels. The multiloop control scheme associated with the power sources is highly reliable since both sources can run the vehicle alone and regulate the bus voltage. Vehicle speed and torque controllers are simultaneously tuned using a particle swarm optimization algorithm. Torque and speed ripples are automatically minimized via the use of a new proposed cost function. This approach made the controller design easier and gave the designer the possibility to tradeoff between the variables to be minimized.

Current Sensor FTC Method for MPTC of Three-Phase Induction Motor Drives Without Speed Measurement

In encoderless Model Predictive Torque Control (MPTC) of three-phase Induction Motor (IM) drives, current sensors can face different electrical or mechanical faults in harsh industrial environments. In this research, single-phase current sensor Fault-Tolerant Control (FTC) method for MPTC of three-phase IM drives without speed measurement using a flux linkage observer and Model Reference Adaptive System (MRAS) algorithm is proposed. In the presented FTC method, Third-Difference operators, logic circuit module, flux linkage observer, and MRAS algorithm are utilized for fault detection, fault isolation, estimation of stator currents and fluxes, and speed estimation, respectively. In comparison with the conventional current sensor FTC methods, the proposed method can be utilized for encoderless three-phase IM drives. In order to confirm the usefulness and possibility of the proposed encoderless FTC method, experimental studies are performed for a 0.75 kW three-phase IM drive system in different situations. The achieved results demonstrate good performances of the proposed technique during both normal and faulty situations.

Optimization of decentralized control strategies of distributed resources under cyber failures in flexible distribution network

The flexible multi-state switch (FMSS) can realize the optimal control of the distribution network under normal and faulty situations. The dependence of distribution network on cyber system becomes heavier as distributed resources such as FMSS and distributed generations (DGs) integrated to the network. To reduce the operational risks under cyber failure scenarios, this paper proposes an optimization model to enable decentralized control strategies of distributed resources under cyber failures. First, the model of flexible cyber-physical distribution system is established and the coordinated control strategies of distributed resources under different operation statuses are analyzed. Then, the adaptive fuzzy-C means method and fault tree method based on consistency of failure consequences are used to cope with the uncertainties of DGs and cyber failures, respectively. On this basis, a bi-level optimization model of the decentralized control strategies of distribution resources is established to minimize the voltage deviation and load shedding. Second order cone method and heuristic algorithm are adopted for solving the optimization problem. Finally, the proposed control strategy is validated by case studies. This study is an effective operation strategy for distributed resources in flexible distribution network.© 2017 Elsevier Inc. All rights reserved.

Fault detection and diagnosis of monopolar configured VSC based high voltage direct current transmission line

The development of HVDC transmission technology using an MMC has been promoted, by overcoming the drawbacks of traditional VSC technology. The extension of flexible transmission to overhead lines, especially the use of HVDC transmission based on an MMC, raises the issue of DC fault. So identification of fault, clearing the dc fault, and design of fast-acting protection system operating against fault becomes significant. This article provides insights on the monopolar structured MMC and operational characteristics, fault analysis, and a fault protection scheme. DC line faults on HVDC lines using MMC-VSC are major issues; isolation of complete system is not a viable option. It is observed that Pole to ground fault is the most common fault, which leads to generous overcurrent in the AC grid and results in converter valves getting damaged. This article analyzes the response of MMC-HVDC under different DC and AC faults conditions for five-level MMC HVDC systems, to better understand systems under fault. Faults also have an impact on the converter stations' performance. The voltages fluctuate in faulty situations. In comparison to the inverter station, the rectifier station has the most impact. Simulation is performed out in PSCAD software. The correctness and effectiveness of DC and AC fault analysis helps to check the capability of locating fault occurring on HVDC transmission lines quickly and accurately

Decision fusion for reliable fault classification in energy-intensive process industries

Heavy industries such as oil & gas, steel & iron production, the cement industry, chemical processes, and pulp & paper mills are among the industries that contribute most to greenhouse gas emissions. The operation of processes in these industries requires massive amounts of energy, which is mainly generated through the consumption of natural resources, leading to the emission of harmful gases. These emissions can be reduced by improving the efficiency of process operations and with better management of abnormal events/faults through accurate fault classification. Managers and operators of these industries need intelligent fault classification strategies that provide accurate information and decisions on faulty situations management, thus helping day-to-day processes operate in safe, reliable and energy-efficient ways. This paper proposes a decision fusion approach that combines outputs produced by diversified machine learning fault classifiers, each with distinct pattern representations. The proposed approach mainly adopts the behavior knowledge space (BKS) method and updates the corresponding lookup table based on the F1-scores calculated for every single fault classifier based on historical data. The concept is based on the exploitation of the advantages of each fault classifier as a complementary information source to effectively classify faults and provide plant operators and engineers with unified, accurate and comprehensive decisions. The proposed approach is validated through two case studies in the pulp and paper industry. The first one is the pulp mill process benchmark using simulated data and the second case is on a reboiler in a Canadian thermomechanical pulp mill. The results obtained demonstrate that the accuracy of the proposed approach is higher than the accuracy of every single classifier and other comparable methods applied to such complex industrial processes. The results have helped the mill operators correctly identify the causes of abnormal events and have contributed to significant energy savings and reduction in Greenhouse Gas (GHG) emissions.

EVENT STUDY: ONE FIRM, TWO MARKETS AND INFORMATION ASYMMETRY

This study is an event window study specific to Turkey's agriculture, forestry and fishing sector. The changes that a company in the relevant sector has gone through in 2020 and 2021 are under the microscope. In the study, a firm 'story' is presented, ranging from the Borsa İstanbul emerging companies market to the close monitoring market(the watch list), and then to the actions that resulted in 'failure'. As of 2022, there are only two companies listed in the sector. The company, which was excluded from the sector, was both subject to the sanctions of the Capital Markets Board and engaged in market-distorting actions. More strict control is required in the approval and activity processes of the company that has changed the title and the companies that will want to enter the sector. As a prerequisite for the protection of small investors, transparency should be treated more strictly. The common desired situation in capital markets is to ensure transparency. When disruptions occur in public disclosure, the operation may be impaired. When such faulty situations arise, the probability of financial market crimes such as information fraud will increase, and the attractiveness of the financial market to investors will decrease. These possible realizations become clear as problematic points, for economies that want to grow through financial markets.

Optimized OP-FTC for SPMSM Considering Copper Loss Minimization

The traditional fault-tolerant control (FTC) strategy can guarantee that permanent magnet synchronous machine (PMSM) operate stably in open-phase fault (OPF) situation. However, the copper loss minimization is usually out of consideration. Therefore, an optimized open-phase FTC (OP-FTC) method is put forward for surface-mounted PMSM (SPMSM) considering copper loss minimization. First, when an OPF happens, according to the theory that magneto-motive force remains unchanged in normal and faulty situations, the relationships between the remaining two-phase currents and the dq-axis currents are obtained to make SPMSM output the same torque as that before the fault appearance. Then, based on the relationship between dq-axis currents and copper loss, the d-axis current is calculated to make copper loss minimum. Finally, the proposed OP-FTC strategy is presented for SPMSM drive system. The simulation and experiment are studied to test the optimized OP-FTC strategy.

Robustification of Unscented Kalman Filtering to Identify Faults in Gas Turbine Engine

A methodology is proposed to improve the robustification of the Unscented Kalman Filtering by adjusting the measurement noise covariance matrix by introducing two adaptive factors. The combined effect of the two adaptive factors helps to identify and filter the outliers more prominently. Improvement in the proposed robust UKF algorithm is established by analyzing the percentage reduction of accumulation error for different conditions with the larger measurement noise covariance. In addition, a method of fault identification of gas turbine engines is developed by the proposed robust UKF approach in the presence of system and measurement noises. The proposed fault identification method deals with the analysis of the residual signature of different states along with the cost function of the derived algorithm. Different component and sensor anomalies are introduced in the nonlinear dynamic model of the gas turbine engine to produce the faulty situations. The deviations in performances are compared with the nominal behavior to obtain the residual signature. A comparative study is performed to identify the faulty scenarios.

A data driven fault isolation method based on reference faulty situations with application to a nonlinear chemical process

A data driven fault isolation method based on reference faulty situations with application to a nonlinear chemical process

Linker: 공장 신속 재구성과 자율 운용을 위한 액티브 드라이버 Wrapper

In the era of Industry 4.0, manufacturing systems require essential capabilities of rapid-reconfiguration and autonomous operations. Many studies have been conducted to generate flexible plans and optimize task sequences at the shop floor system-level. However, little attention have paid on how to connect these task plans to the actual execution of field devices. Therefore, this paper proposes the concept and architecture of active driver wrapper (called linker) for improvised and interactive task execution. The linker architecture includes the definition for digital representation of devices and hardware abstraction to facilitate us to rapidly change the logical and physical configurations of manufacturing systems. In addition, each linker can proactively respond to the faulty situations of devices via peripheral intelligence. Four case studies were conducted to evaluate the potentials of the proposed architecture.

Rapid Detection of Small Faults and Oscillations in Synchronous Generator Systems Using GMDH Neural Networks and High-Gain Observers

This paper presents a robust and efficient fault detection and diagnosis framework for handling small faults and oscillations in synchronous generator (SG) systems. The proposed framework utilizes the Brunovsky form representation of nonlinear systems to mathematically formulate the fault detection problem. A differential flatness model of SG systems is provided to meet the conditions of the Brunovsky form representation. A combination of high-gain observer and group method of data handling neural network is employed to estimate the trajectory of the system and to learn/approximate the fault- and uncertainty-associated functions. The fault detection mechanism is developed based on the output residual generation and monitoring so that any unfavorable oscillation and/or fault occurrence can be detected rapidly. Accordingly, an average L1-norm criterion is proposed for rapid decision making in faulty situations. The performance of the proposed framework is investigated for two benchmark scenarios which are actuation fault and fault impact on system dynamics. The simulation results demonstrate the capacity and effectiveness of the proposed solution for rapid fault detection and diagnosis in SG systems in practice, and thus enhancing service maintenance, protection, and life cycle of SGs.

Maximum Power Extraction from Wind Turbines Using a Fault-Tolerant Fractional-Order Nonsingular Terminal Sliding Mode Controller

This work presents a nonlinear control approach to maximise the power extraction of wind energy conversion systems (WECSs) operating below their rated wind speeds. Due to nonlinearities associated with the dynamics of WECSs, the stochastic nature of wind, and the inevitable presence of faults in practice, developing reliable fault-tolerant control strategies to guarantee maximum power production of WECSs has always been considered important. A fault-tolerant fractional-order nonsingular terminal sliding mode control (FNTSMC) strategy to maximize the captured power of wind turbines (WT) subjected to actuator faults is developed. A nonsingular terminal sliding surface is proposed to ensure fast finite-time convergence, whereas the incorporation of fractional calculus in the controller enhances the convergence speed of system states and simultaneously suppresses chattering, resulting in extracted power maximisation by precisely tracking the optimum rotor speed. Closed-loop stability is analysed and validated through the Lyapunov stability criterion. Comparative numerical simulation analysis is carried out on a two-mass WT, and superior power production performance of the proposed method over other methods is demonstrated, both in fault-free and faulty situations.

A Generalized Space Vector Modulation for Cascaded H-Bridge Inverters under Faulty Conditions

In this research, a new space vector modulation control algorithm is proposed to increase the reliability and the accuracy of the cascaded H-bridge multilevel inverters in case of faulty situations where one or several power cells do not function. When one or more switches of a cell are opened or shorted, that cell is considered faulty. By giving a detailed analysis on the impact of the faulty power cells on the voltage space vectors, the inapplicable voltage vectors are removed precisely. Consequently, the optimal redundant switching states are chosen such that the highest possible output voltage can be achieved. In addition, the balance of the three phases line-to-line voltage and current are maintained. The proposed algorithm is also generalized so that it can be applied to any level of H-bridge inverters. The validity of the method is verified by numerical simulations using MATLAB Simulink with an 11-level cascaded H-bridge inverter.

An open-circuit fault diagnosis method for PMSM drives using symmetrical and DC components

In this paper, a diagnostic method based on symmetrical and DC components is presented for multiple open-circuit faults (OCFs) in a three-phase permanent magnet synchronous motor drive. The remaining phase currents under faulty situations are theoretically analyzed based on the Fourier series method and the mechanism of torque generation. To evaluate the asymmetry and discriminate fault types, the magnitude ratio of the positive sequence to the negative sequence of phase currents is designed as a fault detection index. Thereafter, the DC components of the phase currents in different reference frames are utilized to locate faults. Specifically, the polarities of the DC contents in the natural reference frame are utilized to locate a single OCF and two OCFs in different legs. Two OCFs in the same leg are diagnosed by evaluating the signs of the phase currents in the dq reference frame with a negative rotating direction. The simulated and experimental results validate the effectiveness of the developed method in fault detection and robustness against operating point and motor parameter variations.

On the provenance extraction techniques from large scale log files

AbstractNumerical weather prediction (NWP) models are the most important instruments to predict future weather. Provenance information is of central importance for detecting unexpected events that may develop during the long course of model execution. Besides, the need to share scientific data and results between researchers also highlights the importance of data quality and reliability. The weather research and forecasting (WRF) Model is an open‐source NWP model. In this study, we propose a methodology for tracking the WRF model and for generating, storing, and analyzing provenance. We implement the proposed methodology—with a machine learning‐based parser, which utilizes classification algorithms to extract provenance information. The proposed approach enables easy management and understanding of numerical weather forecast workflows by providing provenance graphs. By analyzing these graphs, potential faulty situations that may occur during the execution of WRF can be traced to their root causes. Our proposed approach has been evaluated and has been shown to perform well even in a high‐frequency provenance information flow.